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Lo Gullo R, Marcus E, Huayanay J, Eskreis-Winkler S, Thakur S, Teuwen J, Pinker K. Artificial Intelligence-Enhanced Breast MRI: Applications in Breast Cancer Primary Treatment Response Assessment and Prediction. Invest Radiol 2024; 59:230-242. [PMID: 37493391 PMCID: PMC10818006 DOI: 10.1097/rli.0000000000001010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
ABSTRACT Primary systemic therapy (PST) is the treatment of choice in patients with locally advanced breast cancer and is nowadays also often used in patients with early-stage breast cancer. Although imaging remains pivotal to assess response to PST accurately, the use of imaging to predict response to PST has the potential to not only better prognostication but also allow the de-escalation or omission of potentially toxic treatment with undesirable adverse effects, the accelerated implementation of new targeted therapies, and the mitigation of surgical delays in selected patients. In response to the limited ability of radiologists to predict response to PST via qualitative, subjective assessments of tumors on magnetic resonance imaging (MRI), artificial intelligence-enhanced MRI with classical machine learning, and in more recent times, deep learning, have been used with promising results to predict response, both before the start of PST and in the early stages of treatment. This review provides an overview of the current applications of artificial intelligence to MRI in assessing and predicting response to PST, and discusses the challenges and limitations of their clinical implementation.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Jorge Huayanay
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Radiology, National Institute of Neoplastic Diseases, Lima, Peru
| | - Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
| | - Sunitha Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jonas Teuwen
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
- Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands
- AI for Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66 Street, New York, NY 10065, USA
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Yang Y, Xiang T, Lv X, Li L, Lui LM, Zeng T. Double Transformer Super-Resolution for Breast Cancer ADC Images. IEEE J Biomed Health Inform 2024; 28:917-928. [PMID: 38079366 DOI: 10.1109/jbhi.2023.3341250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.
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Li W, Partridge SC, Newitt DC, Steingrimsson J, Marques HS, Bolan PJ, Hirano M, Bearce BA, Kalpathy-Cramer J, Boss MA, Teng X, Zhang J, Cai J, Kontos D, Cohen EA, Mankowski WC, Liu M, Ha R, Pellicer-Valero OJ, Maier-Hein K, Rabinovici-Cohen S, Tlusty T, Ozery-Flato M, Parekh VS, Jacobs MA, Yan R, Sung K, Kazerouni AS, DiCarlo JC, Yankeelov TE, Chenevert TL, Hylton NM. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge. Radiol Imaging Cancer 2024; 6:e230033. [PMID: 38180338 PMCID: PMC10825718 DOI: 10.1148/rycan.230033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/13/2023] [Accepted: 11/02/2023] [Indexed: 01/06/2024]
Abstract
Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Wen Li
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Savannah C. Partridge
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - David C. Newitt
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jon Steingrimsson
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Helga S. Marques
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Patrick J. Bolan
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael Hirano
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Benjamin Aaron Bearce
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jayashree Kalpathy-Cramer
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael A. Boss
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Xinzhi Teng
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jiang Zhang
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jing Cai
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Despina Kontos
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Eric A. Cohen
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Walter C. Mankowski
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael Liu
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Richard Ha
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Oscar J. Pellicer-Valero
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Klaus Maier-Hein
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Simona Rabinovici-Cohen
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Tal Tlusty
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michal Ozery-Flato
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Vishwa S. Parekh
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael A. Jacobs
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Ran Yan
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Kyunghyun Sung
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Anum S. Kazerouni
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Julie C. DiCarlo
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Thomas E. Yankeelov
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Thomas L. Chenevert
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Nola M. Hylton
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
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4
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Gullo RL, Partridge SC, Shin HJ, Thakur SB, Pinker K. Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring. AJR Am J Roentgenol 2024; 222:e2329933. [PMID: 37850579 PMCID: PMC11196747 DOI: 10.2214/ajr.23.29933] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA, USA 98109, USA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Micek M, Aebisher D, Surówka J, Bartusik-Aebisher D, Madera M. Applications of T 1 and T 2 relaxation time calculation in tissue differentiation and cancer diagnostics-a systematic literature review. Front Oncol 2022; 12:1010643. [PMID: 36531030 PMCID: PMC9749890 DOI: 10.3389/fonc.2022.1010643] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/31/2022] [Indexed: 01/07/2024] Open
Abstract
INTRODUCTION The purpose of this review was to summarize current applications of non-contrast-enhanced quantitative magnetic resonance imaging (qMRI) in tissue differentiation, considering healthy tissues as well as comparisons of malignant and benign samples. The analysis concentrates mainly on the epithelium and epithelial breast tissue, especially breast cancer. METHODS A systematic review has been performed based on current recommendations by publishers and foundations. An exhaustive overview of currently used techniques and their potential in medical sciences was obtained by creating a search strategy and explicit inclusion and exclusion criteria. RESULTS AND DISCUSSION PubMed and Elsevier (Scopus & Science Direct) search was narrowed down to studies reporting T1 or T2 values of human tissues, resulting in 404 initial candidates, out of which roughly 20% were found relevant and fitting the review criteria. The nervous system, especially the brain, and connective tissue such as cartilage were the most frequently analyzed, while the breast remained one of the most uncommon subjects of studies. There was little agreement between published T1 or T2 values, and methodologies and experimental setups differed strongly. Few contemporary (after 2000) resources have been identified that were dedicated to studying the relaxation times of tissues and their diagnostic applications. Most publications concentrate on recommended diagnostic standards, for example, breast acquisition of T1- or T2-weighted images using gadolinium-based contrast agents. Not enough data is available yet to decide how repeatable or reliable analysis of relaxation times is in diagnostics, so it remains mainly a research topic. So far, qMRI might be recommended as a diagnostic help providing general insight into the nature of lesions (benign vs. malignant). However, additional means are generally necessary to differentiate between specific lesion types.
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Affiliation(s)
| | - David Aebisher
- Department of Photomedicine and Physical Chemistry, Medical College of The University of Rzeszow, Rzeszow, Poland
| | | | - Dorota Bartusik-Aebisher
- Department of Biochemistry and General Chemistry, Medical College of The University of Rzeszow, Rzeszow, Poland
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6
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Lo Gullo R, Sevilimedu V, Baltzer P, Le Bihan D, Camps-Herrero J, Clauser P, Gilbert FJ, Iima M, Mann RM, Partridge SC, Patterson A, Sigmund EE, Thakur S, Thibault FE, Martincich L, Pinker K. A survey by the European Society of Breast Imaging on the implementation of breast diffusion-weighted imaging in clinical practice. Eur Radiol 2022; 32:6588-6597. [PMID: 35507050 PMCID: PMC9064723 DOI: 10.1007/s00330-022-08833-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/19/2022] [Accepted: 04/21/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVES To perform a survey among all European Society of Breast Imaging (EUSOBI) radiologist members to gather representative data regarding the clinical use of breast DWI. METHODS An online questionnaire was developed by two board-certified radiologists, reviewed by the EUSOBI board and committees, and finally distributed among EUSOBI active and associated (not based in Europe) radiologist members. The questionnaire included 20 questions pertaining to technical preferences (acquisition time, magnet strength, breast coils, number of b values), clinical indications, imaging evaluation, and reporting. Data were analyzed using descriptive statistics, the Chi-square test of independence, and Fisher's exact test. RESULTS Of 1411 EUSOBI radiologist members, 275/1411 (19.5%) responded. Most (222/275, 81%) reported using DWI as part of their routine protocol. Common indications for DWI include lesion characterization (using an ADC threshold of 1.2-1.3 × 10-3 mm2/s) and prediction of response to chemotherapy. Members most commonly acquire two separate b values (114/217, 53%), with b value = 800 s/mm2 being the preferred value for appraisal among those acquiring more than two b values (71/171, 42%). Most did not use synthetic b values (169/217, 78%). While most mention hindered diffusion in the MRI report (161/213, 76%), only 142/217 (57%) report ADC values. CONCLUSION The utilization of DWI in clinical practice among EUSOBI radiologists who responded to the survey is generally in line with international recommendations, with the main application being the differentiation of benign and malignant enhancing lesions, treatment response assessment, and prediction of response to chemotherapy. Report integration of qualitative and quantitative DWI data is not uniform. KEY POINTS • Clinical performance of breast DWI is in good agreement with the current recommendations of the EUSOBI International Breast DWI working group. • Breast DWI applications in clinical practice include the differentiation of benign and malignant enhancing, treatment response assessment, and prediction of response to chemotherapy. • Report integration of DWI results is not uniform.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, NY, New York, 10017, USA
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Denis Le Bihan
- NeuroSpin/Joliot, CEA-Saclay Center, Paris-Saclay University, Gif-sur-Yvette, France
- Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan
- National Institute for Physiological Sciences, Okazaki, Japan
| | | | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto, Japan
| | - Ritse M Mann
- Department of Diagnostic Imaging, Radboud University Medical Centre, Nijmegen, Netherlands
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Eric E Sigmund
- Department of Radiology, NYU Langone Health, 6, 60 1st Avenue, New York, NY, 10016, USA
| | - Sunitha Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Fabienne E Thibault
- Department of Medical Imaging, Institut Curie, 26 Rue d'Ulm, F-75005, Paris, France
| | - Laura Martincich
- Unit of Radiodiagnostics, Ospedale Cardinal G. Massaia -ASL AT, Via Conte Verde 125, 14100, Asti, Italy
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA.
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria.
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Kong X, Zhang Q, Wu X, Zou T, Duan J, Song S, Nie J, Tao C, Tang M, Wang M, Zou J, Xie Y, Li Z, Li Z. Advances in Imaging in Evaluating the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer. Front Oncol 2022; 12:816297. [PMID: 35669440 PMCID: PMC9163342 DOI: 10.3389/fonc.2022.816297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) is increasingly widely used in breast cancer treatment, and accurate evaluation of its response provides essential information for treatment and prognosis. Thus, the imaging tools used to quantify the disease response are critical in evaluating and managing patients treated with NAC. We discussed the recent progress, advantages, and disadvantages of common imaging methods in assessing the efficacy of NAC for breast cancer.
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Affiliation(s)
- Xianshu Kong
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Qian Zhang
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Xuemei Wu
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Tianning Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jiajun Duan
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Shujie Song
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jianyun Nie
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Chu Tao
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Mi Tang
- Department of Pathology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Maohua Wang
- First Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Jieya Zou
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhenhui Li
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
| | - Zhen Li
- Third Department of the Breast Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, China
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Le NN, Li W, Onishi N, Newitt DC, Gibbs JE, Wilmes LJ, Kornak J, Partridge SC, LeStage B, Price ER, Joe BN, Esserman LJ, Hylton NM. Effect of Inter-Reader Variability on Diffusion-Weighted MRI Apparent Diffusion Coefficient Measurements and Prediction of Pathologic Complete Response for Breast Cancer. Tomography 2022; 8:1208-1220. [PMID: 35645385 PMCID: PMC9149942 DOI: 10.3390/tomography8030099] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/13/2022] [Accepted: 04/15/2022] [Indexed: 11/16/2022] Open
Abstract
This study evaluated the inter-reader agreement of tumor apparent diffusion coefficient (ADC) measurements performed on breast diffusion-weighted imaging (DWI) for assessing treatment response in a multi-center clinical trial of neoadjuvant chemotherapy (NAC) for breast cancer. DWIs from 103 breast cancer patients (mean age: 46 ± 11 years) acquired at baseline and after 3 weeks of treatment were evaluated independently by two readers. Three types of tumor regions of interests (ROIs) were delineated: multiple-slice restricted, single-slice restricted and single-slice tumor ROIs. Compared to tumor ROIs, restricted ROIs were limited to low ADC areas of enhancing tumor only. We found excellent agreement (intraclass correlation coefficient [ICC] ranged from 0.94 to 0.98) for mean ADC. Higher ICCs were observed in multiple-slice restricted ROIs (range: 0.97 to 0.98) than in other two ROI types (both in the range of 0.94 to 0.98). Among the three ROI types, the highest area under the receiver operating characteristic curves (AUCs) were observed for mean ADC of multiple-slice restricted ROIs (0.65, 95% confidence interval [CI]: 0.52–0.79 and 0.67, 95% CI: 0.53–0.81 for Reader 1 and Reader 2, respectively). In conclusion, mean ADC values of multiple-slice restricted ROI showed excellent agreement and similar predictive performance for pathologic complete response between the two readers.
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Affiliation(s)
- Nu N. Le
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - Wen Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
- Correspondence:
| | - Natsuko Onishi
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - David C. Newitt
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - Jessica E. Gibbs
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - Lisa J. Wilmes
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA;
| | | | | | - Elissa R. Price
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - Bonnie N. Joe
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
| | - Laura J. Esserman
- Department of Surgery and Radiology, University of California, San Francisco, CA 94143, USA;
| | - Nola M. Hylton
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA 94158, USA; (N.N.L.); (N.O.); (D.C.N.); (J.E.G.); (L.J.W.); (E.R.P.); (B.N.J.); (N.M.H.)
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Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial. Tomography 2022; 8:701-717. [PMID: 35314635 PMCID: PMC8938828 DOI: 10.3390/tomography8020058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 11/16/2022] Open
Abstract
In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.
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Cho E, Baek HJ, Szczepankiewicz F, An HJ, Jung EJ, Lee HJ, Lee J, Gho SM. Clinical experience of tensor-valued diffusion encoding for microstructure imaging by diffusional variance decomposition in patients with breast cancer. Quant Imaging Med Surg 2022; 12:2002-2017. [PMID: 35284250 PMCID: PMC8899958 DOI: 10.21037/qims-21-870] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 12/13/2021] [Indexed: 08/28/2023]
Abstract
BACKGROUND Diffusion-weighted imaging plays a key role in magnetic resonance imaging (MRI) of breast tumors. However, it remains unclear how to interpret single diffusion encoding with respect to its link with tissue microstructure. The purpose of this retrospective cross-sectional study was to use tensor-valued diffusion encoding to investigate the underlying microstructure of invasive ductal carcinoma (IDC) and evaluate its potential value in a clinical setting. METHODS We retrospectively reviewed biopsy-proven breast cancer patients who underwent preoperative breast MRI examination from July 2020 to March 2021. We reviewed the MRI of 29 patients with 30 IDCs, including analysis by diffusional variance decomposition enabled by tensor-valued diffusion encoding. The diffusion parameters of mean diffusivity (MD), total mean kurtosis (MKT), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), macroscopic fractional anisotropy (FA), and microscopic fractional anisotropy (µFA) were estimated. The parameter differences were compared between IDC and normal fibroglandular breast tissue (FGBT), as well as the association between the diffusion parameters and histopathologic items. RESULTS The mean value of MD in IDCs was significantly lower than that of normal FGBT (1.07±0.27 vs. 1.34±0.29, P<0.001); however, MKT, MKA, MKI, FA, and µFA were significantly higher (P<0.005). Among all the diffusion parameters, MKI was positively correlated with the tumor size on both MRI and pathological specimen (rs=0.38, P<0.05 vs. rs=0.54, P<0.01), whereas MKT had a positive correlation with the tumor size in the pathological specimen only (rs=0.47, P<0.02). In addition, the lymph node (LN) metastasis group had significantly higher MKT, MKA, and µFA compared to the metastasis negative group (P<0.05). CONCLUSIONS Tensor-valued diffusion encoding enables a useful non-invasive method for characterizing breast cancers with information on tissue microstructures. Particularly, µFA could be a potential imaging biomarker for evaluating breast cancers prior to surgery or chemotherapy.
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Affiliation(s)
- Eun Cho
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
| | - Hye Jin Baek
- Department of Radiology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
- Department of Radiology, Institute of Health Sciences, Gyeongsang National University School of Medicine, Jinju-daero, Jinju, Republic of Korea
| | - Filip Szczepankiewicz
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Klinikgatan, Sweden
| | - Hyo Jung An
- Department of Pathology, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Seongsan-gu, Changwon, Republic of Korea
| | - Eun Jung Jung
- Department of Surgery, Gyeongsang National University School of Medicine and Gyeongsang National University Changwon Hospital, Changwon, Republic of Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-gu, Busan, Republic of Korea
| | | | - Sung-Min Gho
- MR Clinical Solutions & Research Collaborations, GE Healthcare, Seoul, Republic of Korea
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Vasmel JE, Groot Koerkamp ML, Mandija S, Veldhuis WB, Moman MR, Froeling M, van der Velden BH, Charaghvandi RK, Vreuls CP, van Diest PJ, van Leeuwen AG, van Gorp J, Philippens ME, van Asselen B, Lagendijk JJ, Verkooijen HM, van den Bongard HD, Houweling AC. Dynamic Contrast-enhanced and Diffusion-weighted Magnetic Resonance Imaging for Response Evaluation After Single-Dose Ablative Neoadjuvant Partial Breast Irradiation. Adv Radiat Oncol 2022; 7:100854. [PMID: 35387418 PMCID: PMC8977856 DOI: 10.1016/j.adro.2021.100854] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 11/01/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose We aimed to evaluate changes in dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) magnetic resonance imaging (MRI) scans acquired before and after single-dose ablative neoadjuvant partial breast irradiation (NA-PBI), and explore the relation between semiquantitative MRI parameters and radiologic and pathologic responses. Methods and Materials We analyzed 3.0T DCE and DW-MRI of 36 patients with low-risk breast cancer who were treated with single-dose NA-PBI, followed by breast-conserving surgery 6 or 8 months later. MRI was acquired before NA-PBI and 1 week, 2, 4, and 6 months after NA-PBI. Breast radiologists assessed the radiologic response and breast pathologists scored the pathologic response after surgery. Patients were grouped as either pathologic responders or nonresponders (<10% vs ≥10% residual tumor cells). The semiquantitative MRI parameters evaluated were time to enhancement (TTE), 1-minute relative enhancement (RE1min), percentage of enhancing voxels (%EV), distribution of washout curve types, and apparent diffusion coefficient (ADC). Results In general, the enhancement increased 1 week after NA-PBI (baseline vs 1 week median – TTE: 15s vs 10s; RE1min: 161% vs 197%; %EV: 47% vs 67%) and decreased from 2 months onward (6 months median – TTE: 25s; RE1min: 86%; %EV: 12%). Median ADC increased from 0.83 × 10−3 mm2/s at baseline to 1.28 × 10−3 mm2/s at 6 months. TTE, RE1min, and %EV showed the most potential to differentiate between radiologic responses, and TTE, RE1min, and ADC between pathologic responses. Conclusions Semiquantitative analyses of DCE and DW-MRI showed changes in relative enhancement and ADC 1 week after NA-PBI, indicating acute inflammation, followed by changes indicating tumor regression from 2 to 6 months after radiation therapy. A relation between the MRI parameters and radiologic and pathologic responses could not be proven in this exploratory study.
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12
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Fournier L, de Geus-Oei LF, Regge D, Oprea-Lager DE, D’Anastasi M, Bidaut L, Bäuerle T, Lopci E, Cappello G, Lecouvet F, Mayerhoefer M, Kunz WG, Verhoeff JJC, Caruso D, Smits M, Hoffmann RT, Gourtsoyianni S, Beets-Tan R, Neri E, deSouza NM, Deroose CM, Caramella C. Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper. Front Oncol 2022; 11:800547. [PMID: 35083155 PMCID: PMC8784734 DOI: 10.3389/fonc.2021.800547] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Accepted: 11/30/2021] [Indexed: 12/15/2022] Open
Abstract
Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.
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Affiliation(s)
- Laure Fournier
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Université de Paris, Assistance Publique–Hôpitaux de Paris (AP-HP), Hopital europeen Georges Pompidou, Department of Radiology, Paris Cardiovascular Research Center (PARCC) Unité Mixte de Recherche (UMRS) 970, Institut national de la santé et de la recherche médicale (INSERM), Paris, France
| | - Lioe-Fee de Geus-Oei
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
- Biomedical Photonic Imaging Group, University of Twente, Enschede, Netherlands
| | - Daniele Regge
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, Fondazione del Piemonte per l’Oncologia-Istituto Di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Turin, Italy
| | - Daniela-Elena Oprea-Lager
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Cancer Centre Amsterdam, Amsterdam University Medical Centers [Vrije Universiteit (VU) University], Amsterdam, Netherlands
| | - Melvin D’Anastasi
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Medical Imaging Department, Mater Dei Hospital, University of Malta, Msida, Malta
| | - Luc Bidaut
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- College of Science, University of Lincoln, Lincoln, United Kingdom
| | - Tobias Bäuerle
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Egesta Lopci
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine Unit, Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS) – Humanitas Research Hospital, Milan, Italy
| | - Giovanni Cappello
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, Fondazione del Piemonte per l’Oncologia-Istituto Di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Turin, Italy
| | - Frederic Lecouvet
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology, Institut de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium
| | - Marius Mayerhoefer
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang G. Kunz
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, University Hospital, Ludwig Maximilian University (LMU) Munich, Munich, Germany
| | - Joost J. C. Verhoeff
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Damiano Caruso
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Medical-Surgical Sciences and Translational Medicine, Sapienza University of Rome, Rome, Italy
| | - Marion Smits
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
- Brain Tumour Centre, Erasmus Medical Centre (MC) Cancer Institute, Rotterdam, Netherlands
| | - Ralf-Thorsten Hoffmann
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Institute and Policlinic for Diagnostic and Interventional Radiology, University Hospital, Carl-Gustav-Carus Technical University Dresden, Dresden, Germany
| | - Sofia Gourtsoyianni
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, School of Medicine, National and Kapodistrian University of Athens, Areteion Hospital, Athens, Greece
| | - Regina Beets-Tan
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, Netherlands
- School For Oncology and Developmental Biology (GROW) School for Oncology and Developmental Biology, Maastricht University, Maastricht, Netherlands
| | - Emanuele Neri
- European Society of Oncologic Imaging (ESOI), European Society of Radiology, Vienna, Austria
- Diagnostic and Interventional Radiology, Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Pisa, Italy
| | - Nandita M. deSouza
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Division of Radiotherapy and Imaging, The Institute of Cancer Research and Royal Marsden National Health Service (NHS) Foundation Trust, London, United Kingdom
- European Imaging Biomarkers Alliance (EIBALL), European Society of Radiology, Vienna, Austria
- Quantitative Imaging Biomarkers Alliance, Radiological Society of North America, Oak Brook, IL, United States
| | - Christophe M. Deroose
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Nuclear Medicine, University Hospitals Leuven, Leuven, Belgium
- Nuclear Medicine & Molecular Imaging, Department of Imaging and Pathology, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
| | - Caroline Caramella
- Imaging Group, European Organisation of Research and Treatment in Cancer (EORTC), Brussels, Belgium
- Radiology Department, Hôpital Marie Lannelongue, Groupe Hospitalier Paris Saint Joseph Centre International des Cancers Thoraciques, Université Paris-Saclay, Le Plessis-Robinson, France
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13
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Curcean S, Cheng L, Picchia S, Tunariu N, Collins D, Blackledge M, Popat S, O'Brien M, Minchom A, Leach MO, Koh DM. Early Response to Chemotherapy in Malignant Pleural Mesothelioma Evaluated Using Diffusion-Weighted Magnetic Resonance Imaging: Initial Observations. JTO Clin Res Rep 2021; 2:100253. [PMID: 34870249 PMCID: PMC8626584 DOI: 10.1016/j.jtocrr.2021.100253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/22/2021] [Accepted: 10/27/2021] [Indexed: 11/28/2022] Open
Abstract
Introduction We compared the magnetic resonance imaging total tumor volume (TTV) and median apparent diffusion coefficient (ADC) of malignant pleural mesothelioma (MPM) before and at 4 weeks after chemotherapy, to evaluate whether these are potential early markers of treatment response. Methods Diffusion-weighted magnetic resonance imaging was performed in 23 patients with MPM before and after 4 weeks of chemotherapy. The TTV was measured by semiautomatic segmentation (GrowCut) and transferred onto ADC maps to record the median ADC. Test-retest repeatability of TTV and ADC was evaluated in eight patients. TTV and median ADC changes were compared between responders and nonresponders, defined using modified Response Evaluation Criteria In Solid Tumors on computed tomography (CT) at 12 weeks after treatment. TTV and median ADC were also correlated with CT size measurement and disease survival. Results The test-retest 95% limits of agreement for TTV were -13.9% to 16.2% and for median ADC -1.2% to 3.3%. A significant increase in median ADC in responders was observed at 4 weeks after treatment (p = 0.02). Correlation was found between CT tumor size change at 12 weeks and median ADC changes at 4 weeks post-treatment (r = -0.560, p = 0.006). An increase in median ADC greater than 5.1% at 4 weeks has 100% sensitivity and 90% specificity for responders (area under the curve = 0.933, p < 0.001). There was also moderate correlation between median tumor ADC at baseline and overall survival (r = 0.45, p = 0.03). Conclusions Diffusion-weighted magnetic resonance imaging measurements of TTV and median ADC in MPM have good measurement repeatability. Increase in ADC at 4 weeks post-treatment has the potential to be an early response biomarker.
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Affiliation(s)
- Sebastian Curcean
- Department of Radiation Oncology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Department of Radiation Oncology, Ion Chiricuta Institute of Oncology, Cluj-Napoca, Romania.,Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Lin Cheng
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom
| | - Simona Picchia
- Department of Radiology, Bordet Institute, Bruxelles, Belgium
| | - Nina Tunariu
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - David Collins
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom
| | - Matthew Blackledge
- Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom
| | - Sanjay Popat
- Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Mary O'Brien
- Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Anna Minchom
- Department of Medical Oncology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Martin O Leach
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom.,Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom
| | - Dow-Mu Koh
- Department of Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom.,Division of Radiotherapy and Imaging, Institute of Cancer Research, London, United Kingdom
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14
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Virostko J, Sorace AG, Slavkova KP, Kazerouni AS, Jarrett AM, DiCarlo JC, Woodard S, Avery S, Goodgame B, Patt D, Yankeelov TE. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting. Breast Cancer Res 2021; 23:110. [PMID: 34838096 PMCID: PMC8627106 DOI: 10.1186/s13058-021-01489-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 11/17/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. METHODS Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. RESULTS Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. CONCLUSIONS Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.
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Affiliation(s)
- John Virostko
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Department of Oncology, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kalina P Slavkova
- Department of Physics, University of Texas at Austin, Austin, TX, USA
| | - Anum S Kazerouni
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Julie C DiCarlo
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Stefanie Woodard
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Boone Goodgame
- Dell Seton Medical Center at the University of Texas, Austin, USA
| | | | - Thomas E Yankeelov
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78712, USA.
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA.
- Department of Oncology, University of Texas at Austin, Austin, TX, USA.
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA.
- Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA.
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA.
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15
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Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021; 16:5309-5338. [PMID: 34552262 PMCID: PMC9753909 DOI: 10.1038/s41596-021-00617-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/12/2021] [Indexed: 02/07/2023]
Abstract
This protocol describes a complete data acquisition, analysis and computational forecasting pipeline for employing quantitative MRI data to predict the response of locally advanced breast cancer to neoadjuvant therapy in a community-based care setting. The methodology has previously been successfully applied to a heterogeneous patient population. The protocol details how to acquire the necessary images followed by registration, segmentation, quantitative perfusion and diffusion analysis, model calibration, and prediction. The data collection portion of the protocol requires ~25 min of scanning, postprocessing requires 2-3 h, and the model calibration and prediction components require ~10 h per patient depending on tumor size. The response of individual breast cancer patients to neoadjuvant therapy is forecast by application of a biophysical, reaction-diffusion mathematical model to these data. Successful application of the protocol results in coregistered MRI data from at least two scan visits that quantifies an individual tumor's size, cellularity and vascular properties. This enables a spatially resolved prediction of how a particular patient's tumor will respond to therapy. Expertise in image acquisition and analysis, as well as the numerical solution of partial differential equations, is required to carry out this protocol.
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Affiliation(s)
- Angela M Jarrett
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - Anum S Kazerouni
- Departments of Biomedical Engineering, Austin, TX, USA
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | - John Virostko
- Livestrong Cancer Institutes, Austin, TX, USA
- Departments of Diagnostic Medicine, Austin, TX, USA
- Departments of Oncology, Austin, TX, USA
| | - Anna G Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, USA
- O'Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Julie C DiCarlo
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Hormuth
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
- Livestrong Cancer Institutes, Austin, TX, USA
| | - David A Ekrut
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA
| | | | - Boone Goodgame
- Departments of Oncology, Austin, TX, USA
- Departments of Internal Medicine, The University of Texas at Austin, Austin, Texas, USA
- Seton Hospital, Austin, TX, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, TX, USA
| | - Thomas E Yankeelov
- Oden Institute for Computational Engineering and Sciences, Austin, TX, USA.
- Livestrong Cancer Institutes, Austin, TX, USA.
- Departments of Biomedical Engineering, Austin, TX, USA.
- Departments of Diagnostic Medicine, Austin, TX, USA.
- Departments of Oncology, Austin, TX, USA.
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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16
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Lu N, Dong J, Fang X, Wang L, Jia W, Zhou Q, Wang L, Wei J, Pan Y, Han X. Predicting pathologic response to neoadjuvant chemotherapy in patients with locally advanced breast cancer using multiparametric MRI. BMC Med Imaging 2021; 21:155. [PMID: 34688263 PMCID: PMC8542288 DOI: 10.1186/s12880-021-00688-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/11/2021] [Indexed: 11/12/2022] Open
Abstract
Background This study aims to observe and analyze the effect of diffusion weighted magnetic resonance imaging (MRI) on the patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Methods Fifty patients (mean age, 48.7 years) with stage II–III breast cancer who underwent neoadjuvant chemotherapy and preoperative MRI between 2016 and 2020 were retrospectively evaluated. The associations between preoperative breast MRI findings/clinicopathological features and outcomes of neoadjuvant chemotherapy were assessed. Results Clinical stage at baseline (OR: 0.104, 95% confidence interval (CI) 0.021–0.516, P = 0.006) and standard apparent diffusion coefficient (ADC) change (OR: 9.865, 95% CI 1.024–95.021, P = 0.048) were significant predictive factors of the effects of neoadjuvant chemotherapy. The percentage increase of standard ADC value in pathologic complete response (pCR) group was larger than that in non-pCR group at first time point (P < 0.05). A correlation was observed between the change in standard ADC values and tumor diameter at first follow-up (r: 0.438, P < 0.05). Conclusions Our findings support that change in standard ADC values and clinical stage at baseline can predict the effects of neoadjuvant chemotherapy for patients with breast cancer in early stage. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00688-z.
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Affiliation(s)
- Nannan Lu
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Jie Dong
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.,Department of Medical Oncology, Anhui Provincial Hospital Affiliated To Anhui Medical University, Hefei, 230032, Anhui, China
| | - Xin Fang
- Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230031, Anhui, China
| | - Lufang Wang
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Wei Jia
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Qiong Zhou
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.,Department of Medical Oncology, Anhui Provincial Hospital Affiliated To Anhui Medical University, Hefei, 230032, Anhui, China
| | - Lingyu Wang
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Jie Wei
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Yueyin Pan
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China
| | - Xinghua Han
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Science and Medicine, University of Science and Technology of China, Lujiang Road 17, Hefei, 230001, Anhui, China.
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17
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Malyarenko DI, Newitt DC, Amouzandeh G, Wilmes LJ, Tan ET, Marinelli L, Devaraj A, Peeters JM, Giri S, Vom Endt A, Hylton NM, Partridge SC, Chenevert TL. Retrospective Correction of ADC for Gradient Nonlinearity Errors in Multicenter Breast DWI Trials: ACRIN6698 Multiplatform Feasibility Study. ACTA ACUST UNITED AC 2021; 6:86-92. [PMID: 32548284 PMCID: PMC7289257 DOI: 10.18383/j.tom.2019.00025] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The presented analysis of multisite, multiplatform clinical oncology trial data sought to enhance quantitative utility of the apparent diffusion coefficient (ADC) metric, derived from diffusion-weighted magnetic resonance imaging, by reducing technical interplatform variability owing to systematic gradient nonlinearity (GNL). This study tested the feasibility and effectiveness of a retrospective GNL correction (GNC) implementation for quantitative quality control phantom data, as well as in a representative subset of 60 subjects from the ACRIN 6698 breast cancer therapy response trial who were scanned on 6 different gradient systems. The GNL ADC correction based on a previously developed formalism was applied to trace-DWI using system-specific gradient-channel fields derived from vendor-provided spherical harmonic tables. For quantitative DWI phantom images acquired in typical breast imaging positions, the GNC improved interplatform accuracy from a median of 6% down to 0.5% and reproducibility of 11% down to 2.5%. Across studied trial subjects, GNC increased low ADC (<1 µm2/ms) tumor volume by 16% and histogram percentiles by 5%–8%, uniformly shifting percentile-dependent ADC thresholds by ∼0.06 µm2/ms. This feasibility study lays the grounds for retrospective GNC implementation in multiplatform clinical imaging trials to improve accuracy and reproducibility of ADC metrics used for breast cancer treatment response prediction.
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Affiliation(s)
| | - David C Newitt
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | | | - Lisa J Wilmes
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Ek T Tan
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY
| | | | | | | | | | | | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
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18
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Newitt DC, Amouzandeh G, Partridge SC, Marques HS, Herman BA, Ross BD, Hylton NM, Chenevert TL, Malyarenko DI. Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial. ACTA ACUST UNITED AC 2021; 6:177-185. [PMID: 32548294 PMCID: PMC7289237 DOI: 10.18383/j.tom.2020.00008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Mean tumor apparent diffusion coefficient (ADC) of breast cancer showed excellent repeatability but only moderate predictive power for breast cancer therapy response in the ACRIN 6698 multicenter imaging trial. Previous single-center studies have shown improved predictive performance for alternative ADC histogram metrics related to low ADC dense tumor volume. Using test/retest (TT/RT) 4 b-value diffusion-weighted imaging acquisitions from pretreatment or early-treatment time-points on 71 ACRIN 6698 patients, we evaluated repeatability for ADC histogram metrics to establish confidence intervals and inform predictive models for future therapy response analysis. Histograms were generated using regions of interest (ROIs) defined separately for TT and RT diffusion-weighted imaging. TT/RT repeatability and intra- and inter-reader reproducibility (on a 20-patient subset) were evaluated using wCV and Bland–Altman limits of agreement for histogram percentiles, low-ADC dense tumor volumes, and fractional volumes (normalized to total histogram volume). Pearson correlation was used to reveal connections between metrics and ROI variability across the sample cohort. Low percentiles (15th and 25th) were highly repeatable and reproducible, wCV < 8.1%, comparable to mean ADC values previously reported. Volumetric metrics had higher wCV values in all cases, with fractional volumes somewhat better but at least 3 times higher than percentile wCVs. These metrics appear most sensitive to ADC changes around a threshold of 1.2 μm2/ms. Volumetric results were moderately to strongly correlated with ROI size. In conclusion, Lower histogram percentiles have comparable repeatability to mean ADC, while ADC-thresholded volumetric measures currently have poor repeatability but may benefit from improvements in ROI techniques.
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Affiliation(s)
- David C Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | | | | | - Helga S Marques
- Brown University-Center for Statistical Sciences, ECOG-ACRIN Biostatistics Center, Providence, RI
| | - Benjamin A Herman
- Brown University-Center for Statistical Sciences, ECOG-ACRIN Biostatistics Center, Providence, RI
| | - Brian D Ross
- Department of Radiology, University of Michigan, Ann Arbor, MI
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
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19
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Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, Yankeelov TE. Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data. Cancers (Basel) 2021; 13:3008. [PMID: 34208448 PMCID: PMC8234316 DOI: 10.3390/cancers13123008] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/07/2021] [Accepted: 06/13/2021] [Indexed: 01/03/2023] Open
Abstract
Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2-3 mm3. Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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Affiliation(s)
- David A. Hormuth
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
| | - Caleb M. Phillips
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Chengyue Wu
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Ernesto A. B. F. Lima
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA
| | - Guillermo Lorenzo
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Civil Engineering and Architecture, University of Pavia, Via Ferrata 3, 27100 Pavia, Italy
| | - Prashant K. Jha
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
| | - Angela M. Jarrett
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
| | - J. Tinsley Oden
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, USA
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA; (C.M.P.); (C.W.); (E.A.B.F.L.); (G.L.); (P.K.J.); (J.T.O.); (T.E.Y.)
- Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA;
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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20
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Respiratory Motion Mitigation and Repeatability of Two Diffusion-Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer. ACTA ACUST UNITED AC 2021; 7:66-79. [PMID: 33704226 PMCID: PMC8048371 DOI: 10.3390/tomography7010007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 02/02/2021] [Indexed: 12/31/2022]
Abstract
Respiratory motion and increased susceptibility effects at high magnetic fields pose challenges for quantitative diffusion-weighted MRI (DWI) of a mouse abdomen on preclinical MRI systems. We demonstrate the first application of radial k-space-sampled (RAD) DWI of a mouse abdomen using a genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC) on a 4.7 T preclinical scanner equipped with moderate gradient capability. RAD DWI was compared with the echo-planar imaging (EPI)-based DWI method with similar voxel volumes and acquisition times over a wide range of b-values (0.64, 535, 1071, 1478, and 2141 mm2/s). The repeatability metrics are assessed in a rigorous test-retest study (n = 10 for each DWI protocol). The four-shot EPI DWI protocol leads to higher signal-to-noise ratio (SNR) in diffusion-weighted images with persisting ghosting artifacts, whereas the RAD DWI protocol produces relatively artifact-free images over all b-values examined. Despite different degrees of motion mitigation, both RAD DWI and EPI DWI allow parametric maps of apparent diffusion coefficients (ADC) to be produced, and the ADC of the PDAC tumor estimated by the two methods are 1.3 ± 0.24 and 1.5 ± 0.28 × 10-3 mm2/s, respectively (p = 0.075, n = 10), and those of a water phantom are 3.2 ± 0.29 and 2.8 ± 0.15 × 10-3 mm2/s, respectively (p = 0.001, n = 10). Bland-Altman plots and probability density function reveal good repeatability for both protocols, whose repeatability metrics do not differ significantly. In conclusion, RAD DWI enables a more effective respiratory motion mitigation but lower SNR, while the performance of EPI DWI is expected to improve with more advanced gradient hardware.
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21
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Ross BD, Chenevert TL, Meyer CR. Retrospective Registration in Molecular Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00080-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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22
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Lee SH, Shin HJ, Moon WK. Diffusion-Weighted Magnetic Resonance Imaging of the Breast: Standardization of Image Acquisition and Interpretation. Korean J Radiol 2020; 22:9-22. [PMID: 32901461 PMCID: PMC7772373 DOI: 10.3348/kjr.2020.0093] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 05/06/2020] [Accepted: 05/09/2020] [Indexed: 12/12/2022] Open
Abstract
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a rapid, unenhanced imaging technique that measures the motion of water molecules within tissues and provides information regarding the cell density and tissue microstructure. DW MRI has demonstrated the potential to improve the specificity of breast MRI, facilitate the evaluation of tumor response to neoadjuvant chemotherapy and can be employed in unenhanced MRI screening. However, standardization of the acquisition and interpretation of DW MRI is challenging. Recently, the European Society of Breast Radiology issued a consensus statement, which described the acquisition parameters and interpretation of DW MRI. The current article describes the basic principles, standardized acquisition protocols and interpretation guidelines, and the clinical applications of DW MRI in breast imaging.
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Affiliation(s)
- Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, Korea
| | - Hee Jung Shin
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Korea.
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23
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Syed AK, Whisenant JG, Barnes SL, Sorace AG, Yankeelov TE. Multiparametric Analysis of Longitudinal Quantitative MRI data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer. Cancers (Basel) 2020; 12:cancers12061682. [PMID: 32599906 PMCID: PMC7352623 DOI: 10.3390/cancers12061682] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/20/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer.
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Affiliation(s)
- Anum K Syed
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jennifer G Whisenant
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Stephanie L Barnes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
| | - Anna G Sorace
- Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
- O'Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Thomas E Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
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24
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Xu W, Chen X, Deng F, Zhang J, Zhang W, Tang J. Predictors of Neoadjuvant Chemotherapy Response in Breast Cancer: A Review. Onco Targets Ther 2020; 13:5887-5899. [PMID: 32606799 PMCID: PMC7320215 DOI: 10.2147/ott.s253056] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 05/18/2020] [Indexed: 12/17/2022] Open
Abstract
Neoadjuvant chemotherapy (NAC) largely increases operative chances and improves prognosis of the local advanced breast cancer patients. However, no specific means have been invented to predict the therapy responses of patients receiving NAC. Therefore, we focus on the alterations of tumor tissue-related microenvironments such as stromal tumor-infiltrating lymphocytes status, cyclin-dependent kinase expression, non-coding RNA transcription or other small molecular changes, in order to detect potentially predicted biomarkers which reflect the therapeutic efficacy of NAC in different subtypes of breast cancer. Further, possible mechanisms are also discussed to discover feasible treatment targets. Thus, these findings will be helpful to promote the prognosis of breast cancer patients who received NAC and summarized in this review.
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Affiliation(s)
- Weilin Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Xiu Chen
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Fei Deng
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Jian Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Wei Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
| | - Jinhai Tang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, People's Republic of China
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25
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Ha SM, Chang JM, Lee SH, Kim ES, Kim SY, Cho N, Moon WK. Diffusion-weighted MRI at 3.0 T for detection of occult disease in the contralateral breast in women with newly diagnosed breast cancer. Breast Cancer Res Treat 2020; 182:283-297. [PMID: 32447596 DOI: 10.1007/s10549-020-05697-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 05/18/2020] [Indexed: 12/15/2022]
Abstract
PURPOSE Diffusion-weighted magnetic resonance imaging (DW-MRI) offers unenhanced method to detect breast cancer without cost and safety concerns associated with dynamic contrast-enhanced (DCE) MRI. Our purpose was to evaluate the performance of DW-MRI at 3.0T in detection of clinically and mammographically occult contralateral breast cancer in patients with unilateral breast cancer. METHODS Between 2017 and 2018, 1130 patients (mean age 53.3 years; range 26-84 years) with newly diagnosed unilateral breast cancer who underwent breast MRI and had no abnormalities on clinical and mammographic examinations of contralateral breast were included. Three experienced radiologists independently reviewed DW-MRI (b = 0 and 1000 s/mm2) and DCE-MRI and assigned a BI-RADS category. Using histopathology or 1-year clinical follow-up, performance measures of DW-MRI were compared with DCE-MRI. RESULTS A total of 21 (1.9%, 21/1130) cancers were identified (12 ductal carcinoma in situ and 9 invasive ductal carcinoma; mean invasive tumor size, 8.0 mm) in the contralateral breast. Cancer detection rate of DW-MRI was 13-15 with mean of 14 per 1000 examinations (95% confidence interval [CI] 9-23 per 1000 examinations), which was lower than that of DCE-MRI (18-19 with mean of 18 per 1000 examinations, P = 0.01). A lower abnormal interpretation rate (14.0% versus 17.0%, respectively, P < 0.001) with higher specificity (87.3% versus 84.6%, respectively, P < 0.001) but lower sensitivity (77.8% versus 96.8%, respectively, P < 0.001) was noted for DW-MRI compared to DCE-MRI. CONCLUSIONS DW-MRI at 3.0T has the potential as a cost-effective tool for evaluation of contralateral breast in women with newly diagnosed breast cancer.
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Affiliation(s)
- Su Min Ha
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea.
| | - Su Hyun Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Eun Sil Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea
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26
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Baboli M, Zhang J, Kim SG. Advances in Diffusion and Perfusion MRI for Quantitative Cancer Imaging. CURRENT PATHOBIOLOGY REPORTS 2019; 7:129-141. [PMID: 33344067 PMCID: PMC7747414 DOI: 10.1007/s40139-019-00204-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PURPOSE OF REVIEW This article is to review recent technical developments and their clinical applications in cancer imaging quantitative measurement of cellular and vascular properties of the tumors. RECENT FINDINGS Rapid development of fast Magnetic Resonance Imaging (MRI) technologies over last decade brought new opportunities in quantitative MRI methods to measure both cellular and vascular properties of tumors simultaneously. SUMMARY Diffusion MRI (dMRI) and dynamic contrast enhanced (DCE)-MRI have become widely used to assess the tissue structural and vascular properties, respectively. However, the ultimate potential of these advanced imaging modalities has not been fully exploited. The dependency of dMRI on the diffusion weighting gradient strength and diffusion time can be utilized to measure tumor perfusion, cellular structure, and cellular membrane permeability. Similarly, DCE-MRI can be used to measure vascular and cellular membrane permeability along with cellular compartment volume fractions. To facilitate the understanding of these potentially important methods for quantitative cancer imaging, we discuss the basic concepts and recent developments, as well as future directions for further development.
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Affiliation(s)
- Mehran Baboli
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Jin Zhang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
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27
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Baltzer P, Mann RM, Iima M, Sigmund EE, Clauser P, Gilbert FJ, Martincich L, Partridge SC, Patterson A, Pinker K, Thibault F, Camps-Herrero J, Le Bihan D. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2019; 30:1436-1450. [PMID: 31786616 PMCID: PMC7033067 DOI: 10.1007/s00330-019-06510-3] [Citation(s) in RCA: 233] [Impact Index Per Article: 46.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 09/03/2019] [Accepted: 10/10/2019] [Indexed: 01/03/2023]
Abstract
The European Society of Breast Radiology (EUSOBI) established an International Breast DWI working group. The working group consists of clinical breast MRI experts, MRI physicists, and representatives from large vendors of MRI equipment, invited based upon proven expertise in breast MRI and/or in particular breast DWI, representing 25 sites from 16 countries. The aims of the working group are (a) to promote the use of breast DWI into clinical practice by issuing consensus statements and initiate collaborative research where appropriate; (b) to define necessary standards and provide practical guidance for clinical application of breast DWI; (c) to develop a standardized and translatable multisite multivendor quality assurance protocol, especially for multisite research studies; (d) to find consensus on optimal methods for image processing/analysis, visualization, and interpretation; and (e) to work collaboratively with system vendors to improve breast DWI sequences. First consensus recommendations, presented in this paper, include acquisition parameters for standard breast DWI sequences including specifications of b values, fat saturation, spatial resolution, and repetition and echo times. To describe lesions in an objective way, levels of diffusion restriction/hindrance in the breast have been defined based on the published literature on breast DWI. The use of a small ROI placed on the darkest part of the lesion on the ADC map, avoiding necrotic, noisy or non-enhancing lesion voxels is currently recommended. The working group emphasizes the need for standardization and quality assurance before ADC thresholds are applied. The working group encourages further research in advanced diffusion techniques and tailored DWI strategies for specific indications. Key Points • The working group considers breast DWI an essential part of a multiparametric breast MRI protocol and encourages its use. • Basic requirements for routine clinical application of breast DWI are provided, including recommendations on b values, fat saturation, spatial resolution, and other sequence parameters. • Diffusion levels in breast lesions are defined based on meta-analysis data and methods to obtain a reliable ADC value are detailed.
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Affiliation(s)
- Pascal Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Ritse M Mann
- Department of Radiology, Radboud University Medical Centre, Nijmegen, Netherlands. .,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, Netherlands.
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Eric E Sigmund
- Department of Radiology, New York University School of Medicine, NYU Langone Health, Ney York, NY, 10016, USA
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | | | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Andrew Patterson
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna/Vienna General Hospital, Wien, Austria.,MSKCC, New York, NY, 10065, USA
| | | | | | - Denis Le Bihan
- NeuroSpin, Frédéric Joliot Institute, Gif Sur Yvette, France
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Diffusion-Weighted Magnetic Resonance Imaging of the Breast: an Accurate Method for Measuring Early Response to Neoadjuvant Chemotherapy? CURRENT BREAST CANCER REPORTS 2019. [DOI: 10.1007/s12609-019-0311-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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29
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Li W, Newitt DC, Wilmes LJ, Jones EF, Arasu V, Gibbs J, La Yun B, Li E, Partridge SC, Kornak J, Esserman LJ, Hylton NM. Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL. J Magn Reson Imaging 2019; 50:1742-1753. [PMID: 31026118 DOI: 10.1002/jmri.26770] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 04/18/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The change in apparent diffusion coefficient (ADC) measured from diffusion-weighted imaging (DWI) has been shown to be predictive of pathologic complete response (pCR) for patients with locally invasive breast cancer undergoing neoadjuvant chemotherapy. PURPOSE To investigate the additive value of tumor ADC in a multicenter clinical trial setting. STUDY TYPE Retrospective analysis of multicenter prospective data. POPULATION In all, 415 patients who enrolled in the I-SPY 2 TRIAL from 2010 to 2014 were included. FIELD STRENGTH/SEQUENCE 1.5T or 3T MRI system using a fat-suppressed single-shot echo planar imaging sequence with b-values of 0 and 800 s/mm2 for DWI, followed by a T1-weighted sequence for dynamic contrast-enhanced MRI (DCE-MRI) performed at pre-NAC (T0), after 3 weeks of NAC (T1), mid-NAC (T2), and post-NAC (T3). ASSESSMENT Functional tumor volume and tumor ADC were measured at each MRI exam; pCR measured at surgery was assessed as the binary outcome. Breast cancer subtype was defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. STATISTICAL TESTS A logistic regression model was used to evaluate associations between MRI predictors with pCR. The cross-validated area under the curve (AUC) was calculated to assess the predictive performance of the model with and without ADC. RESULTS In all, 354 patients (128 HR+/HER2-, 60 HR+/HER2+, 34 HR-/HER2+, 132 HR-/HER2-) were included in the analysis. In the full cohort, adding ADC predictors increased the AUC from 0.76 to 0.78 at mid-NAC and from 0.76 to 0.81 at post-NAC. In HR/HER2 subtypes, the AUC increased from 0.52 to 0.65 at pre-NAC for HR+/HER2-, from 0.67 to 0.73 at mid-NAC and from 0.72 to 0.76 at post-NAC for HR+/HER2+, from 0.71 to 0.81 at post-NAC for triple negatives. DATA CONCLUSION The addition of ADC to standard functional tumor volume MRI showed improvement in the prediction of treatment response in HR+ and triple-negative breast cancer. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:1742-1753.
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Affiliation(s)
- Wen Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - David C Newitt
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Lisa J Wilmes
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Ella F Jones
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Vignesh Arasu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Jessica Gibbs
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
| | - Bo La Yun
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Radiology, Seoul National University Bundang Hospital, Seoul, Korea
| | - Elizabeth Li
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA.,Department of Biomedical Engineering, University of California, Davis, California, USA
| | | | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | -
- Quantum Leap Healthcare Collaborative, San Francisco, California, USA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, California, USA
| | - Nola M Hylton
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, California, USA
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30
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Wu L, Li J, Fu C, Kühn B, Wang X. Chemotherapy response of pancreatic cancer by diffusion-weighted imaging (DWI) and intravoxel incoherent motion DWI (IVIM-DWI) in an orthotopic mouse model. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 32:501-509. [DOI: 10.1007/s10334-019-00745-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 01/19/2019] [Accepted: 02/17/2019] [Indexed: 12/14/2022]
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31
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Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Romanoff J, Cimino L, Joe BN, Umphrey HR, Ojeda-Fournier H, Dogan B, Oh K, Abe H, Drukteinis JS, Esserman LJ, Hylton NM. Diffusion-weighted MRI Findings Predict Pathologic Response in Neoadjuvant Treatment of Breast Cancer: The ACRIN 6698 Multicenter Trial. Radiology 2018; 289:618-627. [PMID: 30179110 PMCID: PMC6283325 DOI: 10.1148/radiol.2018180273] [Citation(s) in RCA: 156] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 07/12/2018] [Accepted: 07/18/2018] [Indexed: 01/06/2023]
Abstract
Purpose To determine if the change in tumor apparent diffusion coefficient (ADC) at diffusion-weighted (DW) MRI is predictive of pathologic complete response (pCR) to neoadjuvant chemotherapy for breast cancer. Materials and Methods In this prospective multicenter study, 272 consecutive women with breast cancer were enrolled at 10 institutions (from August 2012 to January 2015) and were randomized to treatment with 12 weekly doses of paclitaxel (with or without an experimental agent), followed by 12 weeks of treatment with four cycles of anthracycline. Each woman underwent breast DW MRI before treatment, at early treatment (3 weeks), at midtreatment (12 weeks), and after treatment. Percentage change in tumor ADC from that before treatment (ΔADC) was measured at each time point. Performance for predicting pCR was assessed by using the area under the receiver operating characteristic curve (AUC) for the overall cohort and according to tumor hormone receptor (HR)/human epidermal growth factor receptor 2 (HER2) disease subtype. Results The final analysis included 242 patients with evaluable serial imaging data, with a mean age of 48 years ± 10 (standard deviation); 99 patients had HR-positive (hereafter, HR+)/HER2-negative (hereafter, HER2-) disease, 77 patients had HR-/HER2- disease, 42 patients had HR+/HER2+ disease, and 24 patients had HR-/HER2+ disease. Eighty (33%) of 242 patients experienced pCR. Overall, ΔADC was moderately predictive of pCR at midtreatment/12 weeks (AUC = 0.60; 95% confidence interval [CI]: 0.52, 0.68; P = .017) and after treatment (AUC = 0.61; 95% CI: 0.52, 0.69; P = .013). Across the four disease subtypes, midtreatment ΔADC was predictive only for HR+/HER2- tumors (AUC = 0.76; 95% CI: 0.62, 0.89; P < .001). In a test subset, a model combining tumor subtype and midtreatment ΔADC improved predictive performance (AUC = 0.72; 95% CI: 0.61, 0.83) over ΔADC alone (AUC = 0.57; 95% CI: 0.44, 0.70; P = .032.). Conclusion After 12 weeks of therapy, change in breast tumor apparent diffusion coefficient at MRI predicts complete pathologic response to neoadjuvant chemotherapy. © RSNA, 2018 Online supplemental material is available for this article.
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Affiliation(s)
- Savannah C. Partridge
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Zheng Zhang
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - David C. Newitt
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Jessica E. Gibbs
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Thomas L. Chenevert
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Mark A. Rosen
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Patrick J. Bolan
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Helga S. Marques
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Justin Romanoff
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Lisa Cimino
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Bonnie N. Joe
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Heidi R. Umphrey
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Haydee Ojeda-Fournier
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Basak Dogan
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Karen Oh
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Hiroyuki Abe
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Jennifer S. Drukteinis
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Laura J. Esserman
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - Nola M. Hylton
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
| | - For the ACRIN 6698 Trial Team and I-SPY 2 Trial Investigators
- From the Department of Radiology, University of Washington, 825
Eastlake Ave E, G2-600, Seattle, WA 98109 (S.C.P.); Department of Biostatistics
(Z.Z.) and Center for Statistical Sciences (Z.Z., H.S.M., J.R.), Brown
University, Providence, RI; American College of Radiology Imaging Network
(ACRIN), Reston, Va (Z.Z., H.S.M., J.R.); Department of Radiology and Biomedical
Imaging, University of California, San Francisco, San Francisco, Calif (D.C.N.,
J.E.G., B.N.J., L.J.E., N.M.H.); Department of Radiology/MRI, University of
Michigan, Ann Arbor, Mich (T.L.C.); Department of Radiology, University of
Pennsylvania, Philadelphia, Pa (M.A.R.); Department of Radiology, Center for
Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn
(P.J.B.); American College of Radiology and ECOG-ACRIN Cancer Research Group,
Reston, Va (L.C.); Department of Radiology, University of Alabama, Birmingham,
Birmingham, Ala (H.R.U.); Department of Radiology, University of California, San
Diego, San Diego, Calif (H.O.); Department of Radiology, University of Texas MD
Anderson Cancer Center, Houston, Tex and the University of Texas Southwestern
Medical Center, Dallas, Tex (B.D.); Department of Radiology, Oregon Health and
Science University, Portland, Ore (K.O.); Department of Radiology, University of
Chicago, Chicago, Ill (H.A.); and Department of Diagnostic Radiology, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Fla and Department of
Women’s Imaging, St Joseph’s Women’s Hospital, Tampa, Fla (J.S.D.)
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Newitt DC, Zhang Z, Gibbs JE, Partridge SC, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Aliu S, Li W, Cimino L, Joe BN, Umphrey H, Ojeda-Fournier H, Dogan B, Oh K, Abe H, Drukteinis J, Esserman LJ, Hylton NM. Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial. J Magn Reson Imaging 2018; 49:1617-1628. [PMID: 30350329 DOI: 10.1002/jmri.26539] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 09/20/2018] [Accepted: 09/22/2018] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker. PURPOSE To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures. STUDY TYPE Prospective. SUBJECTS In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer. FIELD STRENGTH/SEQUENCE DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T. ASSESSMENT A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient. STATISTICAL TESTS Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients. RESULTS In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10-3 mm2 /sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96). DATA CONCLUSION Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.
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Affiliation(s)
- David C Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Zheng Zhang
- Department of Biostatistics, Brown University, Providence, Rhode Island, USA.,Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA.,American College of Radiology Imaging Network (ACRIN), Philadelphia, Pennsylvania, USA
| | - Jessica E Gibbs
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | | | - Thomas L Chenevert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Mark A Rosen
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Patrick J Bolan
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota, USA
| | - Helga S Marques
- Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA.,American College of Radiology Imaging Network (ACRIN), Philadelphia, Pennsylvania, USA
| | - Sheye Aliu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Lisa Cimino
- American College of Radiology & ECOG-ACRIN Cancer Research Group, Philadelphia, Pennsylvania, USA
| | - Bonnie N Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Heidi Umphrey
- Department of Radiology, University of Alabama, Birmingham, Alabama, USA
| | | | - Basak Dogan
- Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Diagnostic Radiology, University of Texas Southwestern Medical Center, Houston, Texas, USA
| | - Karen Oh
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Hiroyuki Abe
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Jennifer Drukteinis
- H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA.,Department of Women's Imaging, St. Joseph's Women's Hospital, Tampa, Florida, USA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, California, USA
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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DCE-MRI and parametric imaging in monitoring response to neoadjuvant chemotherapy in breast carcinoma: a preliminary report. Pol J Radiol 2018; 83:e220-e228. [PMID: 30627239 PMCID: PMC6323583 DOI: 10.5114/pjr.2018.76271] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2017] [Accepted: 06/29/2017] [Indexed: 12/30/2022] Open
Abstract
Purpose Neoadjuvant chemotherapy is recommended in patients with locally advanced breast cancer. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables evaluation of the tumour neovasculature that occurs prior to any volume change, which helps identify early treatment failures and allows prompt implementation of second-line therapy. Material and methods We conducted a prospective study in 14 patients with histopathologically proven breast cancer. DCE-MRI data were acquired using multisection, T1-weighted, 3D vibe sequences with fat suppression before, during, and after IV bolus injection (0.1 mmol/kg body weight, Gadoversetamide, Optimark). Post-processing of dynamic contrast perfusion data was done with the vendor’s Tissue 4D software to generate various dynamic contrast parameters, i.e. Ktrans, Kep, Ve, initial area under the time signal curve (IAUC), apparent diffusion coefficient (ADC), and enhancement curve. Patients underwent MRI examinations at baseline, and then after two cycles, and finally at completion of chemotherapy. Results Based on Sataloff criteria for pathological responses, four patients out of 14 were responders, and 10 were non-responders. At the 2nd MRI examination, IAUC was significantly smaller in responders than in non-responders (p = 0.023). When the results of the first and second MRI examinations were compared, Kep decreased from baseline to the second MRI (p = 0.03) in non-responders and in responders (p = 0.04). This change was statistically significant in both groups. The ADC values increased significantly in responders from baseline to the third MRI (p = 0.012). Conclusions In our study, IAUC and ADC were the only parameters that reliably differentiated responders from non-responders after two and three cycles of chemotherapy.
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Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging 2018; 48:10.1002/jmri.26011. [PMID: 29570895 PMCID: PMC6151298 DOI: 10.1002/jmri.26011] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Accepted: 03/02/2018] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer. PURPOSE/HYPOTHESIS To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and T1 -mapping of the breast in community radiology practices. STUDY TYPE Prospective. SUBJECTS/PHANTOM Ice-water DW-MRI and T1 gel phantoms were used to assess accuracy. Normal subjects (n = 3) and phantoms across three sites (one academic, two community) were used to assess reproducibility. Test-retest analysis at one site in normal subjects (n = 12) was used to assess repeatability. FIELD STRENGTH/SEQUENCE 3T Siemens Skyra MRI quantitative DW-MRI and T1 -mapping. ASSESSMENT Quantitative DW-MRI and T1 -mapping parametric maps of phantoms and fibroglandular and adipose tissue of the breast. STATISTICAL TESTS Average values of breast tissue were quantified and Bland-Altman analysis was performed to assess the repeatability of the MRI techniques, while the Friedman test assessed reproducibility. RESULTS ADC measurements were reproducible across sites, with an average difference of 1.6% in an ice-water phantom and 7.0% in breast fibroglandular tissue. T1 measurements in gel phantoms had an average difference of 2.8% across three sites, whereas breast fibroglandular and adipose tissue had 8.4% and 7.5% average differences, respectively. In the repeatability study, we found no bias between first and second scanning sessions (P = 0.1). The difference between repeated measurements was independent of the mean for each MRI metric (P = 0.156, P = 0.862, P = 0.197 for ADC, T1 of fibroglandular tissue, and T1 of adipose tissue, respectively). DATA CONCLUSION Community radiology practices can perform repeatable, reproducible, and accurate quantitative T1 -mapping and DW-MRI. This has the potential to dramatically expand the number of sites that can participate in multisite clinical trials and increase clinical translation of quantitative MRI techniques for cancer response assessment. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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Affiliation(s)
- Anna G. Sorace
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, Texas, USA
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA
| | - Chengyue Wu
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA
| | - Stephanie L. Barnes
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
| | - Angela M. Jarrett
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
| | - Sarah Avery
- Austin Radiological Association, Austin, Texas, USA
| | | | - Boone Goodgame
- Seton Hospital, Austin, Texas, USA
- Department of Internal Medicine, University of Texas at Austin, Austin, Texas, USA
| | - Jeffery J. Luci
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA
- Department of Neuroscience, University of Texas at Austin, Austin, Texas, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Richard G. Abramson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Thomas E. Yankeelov
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, Texas, USA
- Department of Biomedical Engineering, University of Texas at Austin, Austin, Texas, USA
- Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA
| | - John Virostko
- Department of Diagnostic Medicine, University of Texas at Austin, Austin, Texas, USA
- Livestrong Cancer Institutes, University of Texas at Austin, Austin, Texas, USA
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deSouza NM, Winfield JM, Waterton JC, Weller A, Papoutsaki MV, Doran SJ, Collins DJ, Fournier L, Sullivan D, Chenevert T, Jackson A, Boss M, Trattnig S, Liu Y. Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives. Eur Radiol 2018; 28:1118-1131. [PMID: 28956113 PMCID: PMC5811587 DOI: 10.1007/s00330-017-4972-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 05/24/2017] [Accepted: 06/28/2017] [Indexed: 12/18/2022]
Abstract
For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology. KEY POINTS • Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.
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Affiliation(s)
- N. M. deSouza
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - J. M. Winfield
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - J. C. Waterton
- Manchester Academic Health Sciences Institute, University of Manchester, Manchester, UK
| | - A. Weller
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - M.-V. Papoutsaki
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - S. J. Doran
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - D. J. Collins
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - L. Fournier
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Radiology Department, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - D. Sullivan
- Duke Comprehensive Cancer Institute, Durham, NC USA
| | - T. Chenevert
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI USA
| | - A. Jackson
- Manchester Academic Health Sciences Institute, University of Manchester, Manchester, UK
| | - M. Boss
- Applied Physics Division, National Institute of Standards and Technology (NIST), Boulder, CO USA
| | - S. Trattnig
- Department of Biomedical Imaging and Image guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Y. Liu
- European Organisation for Research and Treatment of Cancer, Headquarters, Brussels, Belgium
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Lin Q, Wang J, Li Q, Lin C, Guo Z, Zheng W, Yan C, Li A, Zhou J. Ultrasonic RF time series for early assessment of the tumor response to chemotherapy. Oncotarget 2017; 9:2668-2677. [PMID: 29416800 PMCID: PMC5788668 DOI: 10.18632/oncotarget.23625] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 12/15/2017] [Indexed: 11/25/2022] Open
Abstract
Ultrasound radio-frequency (RF) time series have been shown to carry tissue typing information. To evaluate the potential of RF time series for early prediction of tumor response to chemotherapy, 50MCF-7 breast cancer-bearing nude mice were randomized to receive cisplatin and paclitaxel (treatment group; n = 26) or sterile saline (control group; n = 24). Sequential ultrasound imaging was performed on days 0, 3, 6, and 8 of treatment to simultaneously collect B-mode images and RF data. Six RF time series features, slope, intercept, S1, S2, S3, and S4, were extracted during RF data analysis and contrasted with microstructural tumor changes on histopathology. Chemotherapy administration reduced tumor growth relative to control on days 6 and 8. Compared with day 0, intercept, S1, and S2 were increased while slope was decreased on days 3, 6, and 8 in the treatment group. Compared with the control group, intercept, S1, S2, S3, and S4 were increased, and slope was decreased, on days 3, 6, and 8 in the treatment group. Tumor cell density decreased significantly in the latter on day 3. We conclude that ultrasonic RF time series analysis provides a simple way to noninvasively assess the early tumor response to chemotherapy.
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Affiliation(s)
- Qingguang Lin
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Jianwei Wang
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Qing Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Chunyi Lin
- School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, P.R. China
| | - Zhixing Guo
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Wei Zheng
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Cuiju Yan
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Anhua Li
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
| | - Jianhua Zhou
- Department of Ultrasound, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, P.R. China
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Virostko J, Hainline A, Kang H, Arlinghaus LR, Abramson RG, Barnes SL, Blume JD, Avery S, Patt D, Goodgame B, Yankeelov TE, Sorace AG. Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis. J Med Imaging (Bellingham) 2017; 5:011011. [PMID: 29201942 PMCID: PMC5701084 DOI: 10.1117/1.jmi.5.1.011011] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Accepted: 11/06/2017] [Indexed: 12/11/2022] Open
Abstract
This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI (p<0.001). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.
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Affiliation(s)
- John Virostko
- University of Texas at Austin, Department of Diagnostics, Austin, Texas, United States.,University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
| | - Allison Hainline
- Vanderbilt University, Department of Biostatistics, Nashville, Tennessee, United States
| | - Hakmook Kang
- Vanderbilt University, Department of Biostatistics, Nashville, Tennessee, United States.,Vanderbilt University, Center for Quantitative Sciences, Nashville, Tennessee, United States
| | - Lori R Arlinghaus
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Richard G Abramson
- Vanderbilt University, Center for Quantitative Sciences, Nashville, Tennessee, United States.,Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Stephanie L Barnes
- University of Texas at Austin, Institute for Computational and Engineering Sciences, Austin, Texas, United States.,University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Jeffrey D Blume
- Vanderbilt University, Department of Biostatistics, Nashville, Tennessee, United States
| | - Sarah Avery
- Austin Radiological Association, Austin, Texas, United States
| | - Debra Patt
- Texas Oncology, Austin, Texas, United States
| | - Boone Goodgame
- Seton Hospital, Austin, Texas, United States.,University of Texas at Austin, Department of Medicine, Austin, Texas, United States
| | - Thomas E Yankeelov
- University of Texas at Austin, Department of Diagnostics, Austin, Texas, United States.,University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States.,University of Texas at Austin, Institute for Computational and Engineering Sciences, Austin, Texas, United States.,University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas, United States
| | - Anna G Sorace
- University of Texas at Austin, Department of Diagnostics, Austin, Texas, United States.,University of Texas at Austin, Livestrong Cancer Institutes, Austin, Texas, United States
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Abstract
Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.
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Affiliation(s)
- Savannah C Partridge
- *Department of Radiology, Breast Imaging Section, Seattle Cancer Care Alliance, University of Washington, Seattle, WA †University of Massachusetts Memorial Medical Center, Worcester, MA
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Sorace AG, Harvey S, Syed A, Yankeelov TE. Imaging Considerations and Interprofessional Opportunities in the Care of Breast Cancer Patients in the Neoadjuvant Setting. Semin Oncol Nurs 2017; 33:425-439. [PMID: 28927763 DOI: 10.1016/j.soncn.2017.08.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To discuss standard-of-care and emerging imaging techniques employed for screening and detection, diagnosis and staging, monitoring response to therapy, and guiding cancer treatments. DATA SOURCES Published journal articles indexed in the National Library of Medicine database and relevant websites. CONCLUSION Imaging plays a fundamental role in the care of cancer patients and specifically, breast cancer patients in the neoadjuvant setting, providing an excellent opportunity for interprofessional collaboration between oncologists, researchers, radiologists, and oncology nurses. Quantitative imaging strategies to assess cellular, molecular, and vascular characteristics within the tumor is needed to better evaluate initial diagnosis and treatment response. IMPLICATIONS FOR NURSING PRACTICE Nurses caring for patients in all settings must continue to seek education on emerging imaging techniques. Oncology nurses provide education about the test, ensure the patient has appropriate pre-testing instructions, and manage patient expectations about timing of results availability.
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Lausch A, Yeung TPC, Chen J, Law E, Wang Y, Urbini B, Donelli F, Manco L, Fainardi E, Lee TY, Wong E. A generalized parametric response mapping method for analysis of multi-parametric imaging: A feasibility study with application to glioblastoma. Med Phys 2017; 44:6074-6084. [PMID: 28875538 DOI: 10.1002/mp.12562] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/25/2017] [Accepted: 08/25/2017] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Parametric response map (PRM) analysis of functional imaging has been shown to be an effective tool for early prediction of cancer treatment outcomes and may also be well-suited toward guiding personalized adaptive radiotherapy (RT) strategies such as sub-volume boosting. However, the PRM method was primarily designed for analysis of longitudinally acquired pairs of single-parameter image data. The purpose of this study was to demonstrate the feasibility of a generalized parametric response map analysis framework, which enables analysis of multi-parametric data while maintaining the key advantages of the original PRM method. METHODS MRI-derived apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) maps acquired at 1 and 3-months post-RT for 19 patients with high-grade glioma were used to demonstrate the algorithm. Images were first co-registered and then standardized using normal tissue image intensity values. Tumor voxels were then plotted in a four-dimensional Cartesian space with coordinate values equal to a voxel's image intensity in each of the image volumes and an origin defined as the multi-parametric mean of normal tissue image intensity values. Voxel positions were orthogonally projected onto a line defined by the origin and a pre-determined response vector. The voxels are subsequently classified as positive, negative or nil, according to whether projected positions along the response vector exceeded a threshold distance from the origin. The response vector was selected by identifying the direction in which the standard deviation of tumor image intensity values was maximally different between responding and non-responding patients within a training dataset. Voxel classifications were visualized via familiar three-class response maps and then the fraction of tumor voxels associated with each of the classes was investigated for predictive utility analogous to the original PRM method. Independent PRM and MPRM analyses of the contrast-enhancing lesion (CEL) and a 1 cm shell of surrounding peri-tumoral tissue were performed. Prediction using tumor volume metrics was also investigated. Leave-one-out cross validation (LOOCV) was used in combination with permutation testing to assess preliminary predictive efficacy and estimate statistically robust P-values. The predictive endpoint was overall survival (OS) greater than or equal to the median OS of 18.2 months. RESULTS Single-parameter PRM and multi-parametric response maps (MPRMs) were generated for each patient and used to predict OS via the LOOCV. Tumor volume metrics (P ≥ 0.071 ± 0.01) and single-parameter PRM analyses (P ≥ 0.170 ± 0.01) were not found to be predictive of OS within this study. MPRM analysis of the peri-tumoral region but not the CEL was found to be predictive of OS with a classification sensitivity, specificity and accuracy of 80%, 100%, and 89%, respectively (P = 0.001 ± 0.01). CONCLUSIONS The feasibility of a generalized MPRM analysis framework was demonstrated with improved prediction of overall survival compared to the original single-parameter method when applied to a glioblastoma dataset. The proposed algorithm takes the spatial heterogeneity in multi-parametric response into consideration and enables visualization. MPRM analysis of peri-tumoral regions was shown to have predictive potential supporting further investigation of a larger glioblastoma dataset.
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Affiliation(s)
- Anthony Lausch
- Department of Medical Biophysics, Western University, London, ON, Canada, N6A 3K7
| | | | - Jeff Chen
- Department of Medical Biophysics, Western University, London, ON, Canada, N6A 3K7.,Department of Physics and Engineering, London Regional Cancer Program, London Health Sciences Centre, London, ON, Canada, N7A 4L6
| | - Elton Law
- Imaging, Robarts Research Institute, London, ON, Canada, N6A 5B7
| | - Yong Wang
- Imaging, Robarts Research Institute, London, ON, Canada, N6A 5B7
| | - Benedetta Urbini
- Oncology Unit, Specialized Medical Department, Azienda Ospedaliero-Universitaria, Arcispedale S. Anna, Ferrara, 44121, Italy
| | - Filippo Donelli
- Section of Diagnostic Imaging, Department of Morphology, Surgery and Experimental Medicine, University of Ferrara, Ferrara, 44121, Italy
| | - Luigi Manco
- School in Medical Physics, University of Bologna, Bologna, 40126, Italy
| | - Enrico Fainardi
- Neuroradiology Unit, Department of Diagnostic Imaging, Azienda Ospedaliero-Universitaria Careggi, Florence, 50139, Italy
| | - Ting-Yim Lee
- Department of Medical Biophysics, Western University, London, ON, Canada, N6A 3K7.,Imaging, Robarts Research Institute, London, ON, Canada, N6A 5B7
| | - Eugene Wong
- Department of Medical Biophysics, Western University, London, ON, Canada, N6A 3K7
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Cho GY, Gennaro L, Sutton EJ, Zabor EC, Zhang Z, Giri D, Moy L, Sodickson DK, Morris EA, Sigmund EE, Thakur SB. Intravoxel incoherent motion (IVIM) histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients. Eur J Radiol Open 2017; 4:101-107. [PMID: 28856177 PMCID: PMC5565789 DOI: 10.1016/j.ejro.2017.07.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2017] [Accepted: 07/16/2017] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVE To examine the prognostic capabilities of intravoxel incoherent motion (IVIM) metrics and their ability to predict response to neoadjuvant treatment (NAT). Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI. METHODS This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions). Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12-14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC) from monoexponential decay, IVIM mean values (tissue diffusivity Dt, perfusion fraction fp, and pseudodiffusivity Dp) and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT), excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests. RESULTS Average, kurtosis, and skewness of pseudodiffusion Dp significantly differentiated RECIST responders from nonresponders. ADC and Dt values generally increased (∼70%) and VTT% values generally decreased (∼20%) post-treatment. CONCLUSION Dp metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/Dt parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT.
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Affiliation(s)
- Gene Y Cho
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York School of Medicine, New York, NY, 10016, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, 10016, USA
| | - Lucas Gennaro
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Emily C Zabor
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Zhigang Zhang
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Dilip Giri
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York School of Medicine, New York, NY, 10016, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, 10016, USA
| | - Daniel K Sodickson
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York School of Medicine, New York, NY, 10016, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, 10016, USA
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Eric E Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York School of Medicine, New York, NY, 10016, USA.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, NY, 10016, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
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43
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Dave RV, Millican-Slater R, Dodwell D, Horgan K, Sharma N. Neoadjuvant chemotherapy with MRI monitoring for breast cancer. Br J Surg 2017; 104:1177-1187. [PMID: 28657689 DOI: 10.1002/bjs.10544] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Revised: 10/03/2016] [Accepted: 02/19/2017] [Indexed: 01/06/2023]
Abstract
BACKGROUND Neoadjuvant chemotherapy (NACT) is increasingly being offered to patients with breast cancer. No survival benefit has been demonstrated for NACT, but it may serve to reduce tumour size and improve prognosis through the attainment of a pathological complete response (pCR). The role and mode of MRI monitoring during NACT remain unclear. METHODS Patients managed with NACT at a UK centre over 7 years were studied using a prospectively maintained database, which also included details of MRI. Clinicopathological and radiological predictors of NACT response were analysed in a univariable setting and survival analysis was undertaken using the Kaplan-Meier method. RESULTS A total of 278 patients underwent surgery following NACT, of whom 200 (71·9 per cent) had residual invasive disease and 78 (28·1 per cent) achieved a pCR. Attaining a pCR improved survival significantly compared with that of patients with residual invasive disease (mean 77·1 versus 66·0 months; P = 0·004) and resulted in significantly fewer recurrences (6·0 versus 24·3 per cent; P = 0·001). The pCR rate varied significantly among molecular subgroups of breast cancer (P < 0·001): luminal A, 6 per cent; luminal B/human epidermal growth factor 2 receptor (Her2)-negative, 21 per cent; luminal B/Her2-positive, 35 per cent, Her2-positive/non-luminal, 72 per cent; and triple-negative breast cancer (TNBC), 32 per cent. High-grade disease (G3) correlated with an increased rate of pCR. A radiological response seen on the mid-treatment MRI was predictive of pCR (sensitivity 77·6 per cent, but specificity only 53·3 per cent), as was complete radiological response at final MRI (specificity 97·6 per cent, but sensitivity only 32·2 per cent). CONCLUSION NACT allows identification of patient subgroups within TNBC and Her2-positive cohorts with a good prognosis. MRI can be used to identify patients who are responding to treatment.
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Affiliation(s)
- R V Dave
- Department of Breast Surgery, St James's University Hospital, Leeds, UK
| | | | - D Dodwell
- Department of Breast Oncology, St James's University Hospital, Leeds, UK
| | - K Horgan
- Department of Breast Surgery, St James's University Hospital, Leeds, UK
| | - N Sharma
- Department of Breast Imaging, St James's University Hospital, Leeds, UK
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Diffusion weighted imaging in early prediction of neoadjuvant chemotherapy response in breast cancer. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2017. [DOI: 10.1016/j.ejrnm.2017.03.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
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45
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Galbán CJ, Hoff BA, Chenevert TL, Ross BD. Diffusion MRI in early cancer therapeutic response assessment. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3458. [PMID: 26773848 PMCID: PMC4947029 DOI: 10.1002/nbm.3458] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 11/09/2015] [Accepted: 11/12/2015] [Indexed: 05/05/2023]
Abstract
Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
| | | | | | - B. D. Ross
- Correspondence to: B. D. Ross, University of Michigan School of Medicine, Center for Molecular Imaging and Department of Radiology, Biomedical Sciences Research Building, 109 Zina Pitcher Place, Ann Arbor, MI 48109, USA.
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Partridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE. Diffusion-weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 2016; 45:337-355. [PMID: 27690173 DOI: 10.1002/jmri.25479] [Citation(s) in RCA: 215] [Impact Index Per Article: 26.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 08/29/2016] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.
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Affiliation(s)
- Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Noam Nissan
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Habib Rahbar
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Averi E Kitsch
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Breast Imaging, Seattle Cancer Care Alliance, Seattle, Washington, USA
| | - Eric E Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
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Abramson RG, Arlinghaus LR, Dula AN, Quarles CC, Stokes AM, Weis JA, Whisenant JG, Chekmenev EY, Zhukov I, Williams JM, Yankeelov TE. MR Imaging Biomarkers in Oncology Clinical Trials. Magn Reson Imaging Clin N Am 2016; 24:11-29. [PMID: 26613873 DOI: 10.1016/j.mric.2015.08.002] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.
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Affiliation(s)
- Richard G Abramson
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Lori R Arlinghaus
- Department of Radiology and Radiological Sciences, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Adrienne N Dula
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - C Chad Quarles
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Cancer Biology, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Ashley M Stokes
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Jennifer G Whisenant
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Eduard Y Chekmenev
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biochemistry, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Igor Zhukov
- National Research Nuclear University MEPhI, Kashirskoye highway, 31, Moscow 115409, Russia
| | - Jason M Williams
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA
| | - Thomas E Yankeelov
- Department of Radiology and Radiological Sciences, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Biomedical Engineering, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Cancer Biology, Institute of Imaging Science, Vanderbilt University, 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA; Department of Physics, Institute of Imaging Science, Vanderbilt University, VUIIS 1161 21st Avenue South, AA 1105 MCN, Nashville, TN 37232-2310, USA.
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Even-Sapir E, Golan O, Menes T, Weinstein Y, Lerman H. Breast Imaging Utilizing Dedicated Gamma Camera and (99m)Tc-MIBI: Experience at the Tel Aviv Medical Center and Review of the Literature Breast Imaging. Semin Nucl Med 2016; 46:286-93. [PMID: 27237439 DOI: 10.1053/j.semnuclmed.2016.01.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The scope of the current article is the clinical role of gamma cameras dedicated for breast imaging and (99m)Tc-MIBI tumor-seeking tracer, as both a screening modality among a healthy population and as a diagnostic modality in patients with breast cancer. Such cameras are now commercially available. The technology utilizing a camera composed of a NaI (Tl) detector is termed breast-specific gamma imaging. The technology of dual-headed camera composed of semiconductor cadmium zinc telluride detectors that directly converts gamma-ray energy into electronic signals is termed molecular breast imaging. Molecular breast imaging system has been installed at the Department of Nuclear medicine at the Tel Aviv Sourasky Medical Center, Tel Aviv in 2009. The article reviews the literature well as our own experience.
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Affiliation(s)
- Einat Even-Sapir
- Department of Nuclear Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Orit Golan
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel; Breast Imaging Unit, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel
| | - Tehillah Menes
- Department of Surgery, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel; Breast surgery unit, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel; Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yuliana Weinstein
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel; Breast Imaging Unit, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel
| | - Hedva Lerman
- Department of Nuclear Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv University, Tel Aviv, Israel
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Keenan KE, Peskin AP, Wilmes LJ, Aliu SO, Jones EF, Li W, Kornak J, Newitt DC, Hylton NM. Variability and bias assessment in breast ADC measurement across multiple systems. J Magn Reson Imaging 2016; 44:846-55. [PMID: 27008431 DOI: 10.1002/jmri.25237] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2015] [Accepted: 02/29/2016] [Indexed: 12/19/2022] Open
Abstract
PURPOSE To assess the ability of a recent, anatomically designed breast phantom incorporating T1 and diffusion elements to serve as a quality control device for quantitative comparison of apparent diffusion coefficient (ADC) measurements calculated from diffusion-weighted MRI (DWI) within and across MRI systems. MATERIALS AND METHODS A bilateral breast phantom incorporating multiple T1 and diffusion tissue mimics and a geometric distortion array was imaged with DWI on 1.5 Tesla (T) and 3.0T scanners from two different manufacturers, using three different breast coils (three configurations total). Multiple measurements were acquired to assess the bias and variability of different diffusion weighted single-shot echo-planar imaging sequences on the scanner-coil systems. RESULTS The repeatability of ADC measurements was mixed: the standard deviation relative to baseline across scanner-coil-sequences ranged from low variability (0.47, 95% confidence interval [CI]: 0.22-1.00) to high variability (1.69, 95% CI: 0.17-17.26), depending on material, with the lowest and highest variability from the same scanner-coil-sequence. Assessment of image distortion showed that right/left measurements of the geometric distortion array were 1 to 16% larger on the left coil side compared with the right coil side independent of scanner-coil systems, diffusion weighting, and phase-encoding direction. CONCLUSION This breast phantom can be used to measure scanner-coil-sequence bias and variability for DWI. When establishing a multisystem study, this breast phantom may be used to minimize protocol differences (e.g., due to available sequences or shimming technique), to correct for bias that cannot be minimized, and to weigh results from each system depending on respective variability. J. Magn. Reson. Imaging 2016. J. MAGN. RESON. IMAGING 2016;44:846-855.
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Affiliation(s)
- Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA.
| | - Adele P Peskin
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Lisa J Wilmes
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Sheye O Aliu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Ella F Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California, USA
| | - David C Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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Luker GD, Nguyen HM, Hoff BA, Galbán CJ, Hernando D, Chenevert TL, Talpaz M, Ross BD. A Pilot Study of Quantitative MRI Parametric Response Mapping of Bone Marrow Fat for Treatment Assessment in Myelofibrosis. ACTA ACUST UNITED AC 2016; 2:67-78. [PMID: 27213182 PMCID: PMC4872873 DOI: 10.18383/j.tom.2016.00115] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Myelofibrosis (MF) is a hematologic neoplasm arising as a primary disease or secondary to other myeloproliferative neoplasms (MPNs). Both primary and secondary MF are uniquely associated with progressive bone marrow fibrosis, displacing normal hematopoietic cells from the marrow space and disrupting normal production of mature blood cells. Activation of the JAK2 signaling pathway in hematopoietic stem cells commonly causes MF, and ruxolitinib, a drug targeting this pathway, is the treatment of choice for many patients. However, current measures of disease status in MF do not necessarily predict response to treatment with ruxolitinib or other drugs in MF. Bone marrow biopsies are invasive and prone to sampling error, while measurements of spleen volume only indirectly reflect bone marrow status. Toward the goal of developing an imaging biomarker for treatment response in MF, we present preliminary results from a prospective clinical study evaluating parametric response mapping (PRM) of quantitative Dixon MRI bone marrow fat fraction maps in four MF patients treated with ruxolitinib. PRM allows for the voxel-wise identification of significant change in quantitative imaging readouts over time, in this case the bone marrow fat content. We identified heterogeneous response patterns of bone marrow fat among patients and within different bone marrow sites in the same patient. We also observed discordance between changes in bone marrow fat fraction and reductions in spleen volume, the standard imaging metric for treatment efficacy. This study provides initial support for PRM analysis of quantitative MRI of bone marrow fat to monitor response to therapy in MF, setting the stage for larger studies to further develop and validate this method as a complementary imaging biomarker for this disease.
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Affiliation(s)
- Gary D Luker
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI, USA
| | - Huong Marie Nguyen
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Benjamin A Hoff
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Craig J Galbán
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Thomas L Chenevert
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
| | - Moshe Talpaz
- Division of Hematology/Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Brian D Ross
- Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, MI, USA; Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA
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