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Onishi N, Bareng TJ, Gibbs J, Li W, Price ER, Joe BN, Kornak J, Esserman LJ, Newitt DC, Hylton NM. Effect of Longitudinal Variation in Tumor Volume Estimation for MRI-guided Personalization of Breast Cancer Neoadjuvant Treatment. Radiol Imaging Cancer 2023; 5:e220126. [PMID: 37505107 PMCID: PMC10413289 DOI: 10.1148/rycan.220126] [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: 10/06/2022] [Revised: 05/02/2023] [Accepted: 06/03/2023] [Indexed: 07/29/2023]
Abstract
Purpose To investigate the impact of longitudinal variation in functional tumor volume (FTV) underestimation and overestimation in predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Materials and Methods Women with breast cancer who were enrolled in the prospective I-SPY 2 TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) from May 2010 to November 2016 were eligible for this retrospective analysis. Participants underwent four MRI examinations during NAC treatment. FTV was calculated based on automated segmentation. Baseline FTV before treatment (FTV0) and the percentage of FTV change at early treatment and inter-regimen time points relative to baseline (∆FTV1 and ∆FTV2, respectively) were classified into high-standard or standard groups based on visual assessment of FTV under- and overestimation. Logistic regression models predicting pCR using single predictors (FTV0, ∆FTV1, and ∆FTV2) and multiple predictors (all three) were developed using bootstrap resampling with out-of-sample data evaluation with the area under the receiver operating characteristic curve (AUC) independently in each group. Results This study included 432 women (mean age, 49.0 years ± 10.6 [SD]). In the FTV0 model, the high-standard and standard groups showed similar AUCs (0.61 vs 0.62). The high-standard group had a higher estimated AUC compared with the standard group in the ∆FTV1 (0.74 vs 0.63), ∆FTV2 (0.79 vs 0.62), and multiple predictor models (0.85 vs 0.64), with a statistically significant difference for the latter two models (P = .03 and P = .01, respectively). Conclusion The findings in this study suggest that longitudinal variation in FTV estimation needs to be considered when using early FTV change as an MRI-based criterion for breast cancer treatment personalization. Keywords: Breast, Cancer, Dynamic Contrast-enhanced, MRI, Tumor Response ClinicalTrials.gov registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Ram in this issue.
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Affiliation(s)
| | | | - Jessica Gibbs
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Wen Li
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Elissa R. Price
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Bonnie N. Joe
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - John Kornak
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Laura J. Esserman
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - David C. Newitt
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
| | - Nola M. Hylton
- From the Department of Radiology and Biomedical Imaging (N.O.,
T.J.B., J.G., W.L., E.R.P., B.N.J., D.C.N., N.M.H.), Department of Epidemiology
and Biostatistics (J.K.), and Department of Surgery (L.J.E.), University of
California San Francisco, 550 16th Street, San Francisco, CA 94158
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Lin P, Wan WJ, Kang T, Qin LF, Meng QX, Wu XX, Qin HY, Lin YQ, He Y, Yang H. Molecular hallmarks of breast multiparametric magnetic resonance imaging during neoadjuvant chemotherapy. LA RADIOLOGIA MEDICA 2023; 128:171-183. [PMID: 36680710 PMCID: PMC9860227 DOI: 10.1007/s11547-023-01595-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023]
Abstract
PURPOSE To identify molecular basis of four parameters obtained from dynamic contrast-enhanced magnetic resonance imaging, including functional tumor volume (FTV), longest diameter (LD), sphericity, and contralateral background parenchymal enhancement (BPE). MATERIAL AND METHODS Pretreatment-available gene expression profiling and different treatment timepoints MRI features were integrated for Spearman correlation analysis. MRI feature-related genes were submitted to hypergeometric distribution-based gene functional enrichment analysis to identify related Kyoto Encyclopedia of Genes and Genomes annotation. Gene set variation analysis was utilized to assess the infiltration of distinct immune cells, which were used to determine relationships between immune phenotypes and medical imaging phenotypes. The clinical significance of MRI and relevant molecular features were analyzed to identify their prediction performance of neoadjuvant chemotherapy (NAC) and prognostic impact. RESULTS Three hundred and eighty-three patients were included for integrative analysis of MRI features and molecular information. FTV, LD, and sphericity measurements were most positively significantly correlated with proliferation-, signal transmission-, and immune-related pathways, respectively. However, BPE did not show marked correlation relationships with gene expression alteration status. FTV, LD and sphericity all showed significant positively or negatively correlated with some immune-related processes and immune cell infiltration levels. Sphericity decreased at 3 cycles after treatment initiation was also markedly negatively related to baseline sphericity measurements and immune signatures. Its decreased status could act as a predictor for prediction of response to NAC. CONCLUSION Different MRI features capture different tumor molecular characteristics that could explain their corresponding clinical significance.
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Affiliation(s)
- Peng Lin
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China ,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi People’s Republic of China
| | - Wei-Jun Wan
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Tong Kang
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Lian-feng Qin
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Qiu-xue Meng
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Xiao-xin Wu
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Hong-yan Qin
- grid.256607.00000 0004 1798 2653Department of Medical Imaging, Guangxi Medical University, Nanning, Guangxi People’s Republic of China
| | - Yi-qun Lin
- grid.12955.3a0000 0001 2264 7233Department of Radiology, Dongnan Hospital of Ximen University, School of Medicine, Xiamen University, Xiamen, Fujian People’s Republic of China
| | - Yun He
- grid.412594.f0000 0004 1757 2961Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi People’s Republic of China ,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi People’s Republic of China
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China. .,Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Image, Nanning, Guangxi, People's Republic of China.
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Hoff BA, Lemasson B, Chenevert TL, Luker GD, Tsien CI, Amouzandeh G, Johnson TD, Ross BD. Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma. Acad Radiol 2021; 28:1711-1720. [PMID: 32928633 DOI: 10.1016/j.acra.2020.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/11/2020] [Accepted: 08/13/2020] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Glioblastoma image evaluation utilizes Magnetic Resonance Imaging contrast-enhanced, T1-weighted, and noncontrast T2-weighted fluid-attenuated inversion recovery (FLAIR) acquisitions. Disease progression assessment relies on changes in tumor diameter, which correlate poorly with survival. To improve treatment monitoring in glioblastoma, we investigated serial voxel-wise comparison of anatomically-aligned FLAIR signal as an early predictor of GBM progression. MATERIALS AND METHODS We analyzed longitudinal normalized FLAIR images (rFLAIR) from 52 subjects using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased (PRMrFLAIR+), decreased (PRMrFLAIR-), or unchanged (PRMrFLAIR0) rFLAIR intensity. We determined response by rFLAIR between pretreatment and 10 weeks posttreatment. Risk of disease progression in a subset of subjects (N = 26) with stable disease or partial response as defined by Response Assessment in Neuro-Oncology (RANO) criteria was assessed by PRMrFLAIR between weeks 10 and 20 and continuously until the PRMrFLAIR+ exceeded a defined threshold. RANO defined criteria were compared with PRM-derived outcomes for tumor progression detection. RESULTS Patient stratification for progression-free survival (PFS) and overall survival (OS) was achieved at week 10 using RANO criteria (PFS: p <0.0001; OS: p <0.0001), relative change in FLAIR-hyperintense volume (PFS: p = 0.0011; OS: p <0.0001), and PRMrFLAIR+ (PFS: p <0.01; OS: p <0.001). PRMrFLAIR+ also stratified responding patients' progression between weeks 10 and 20 (PFS: p <0.05; OS: p = 0.01) while changes in FLAIR-volume measurements were not predictive. As a continuous evaluation, PRMrFLAIR+ exceeding 10% stratified patients for PFA after 5.6 months (p<0.0001), while RANO criteria did not stratify patients until 15.4 months (p <0.0001). CONCLUSION PRMrFLAIR may provide an early biomarker of disease progression in glioblastoma.
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Liefaard MC, Lips EH, Wesseling J, Hylton NM, Lou B, Mansi T, Pusztai L. The Way of the Future: Personalizing Treatment Plans Through Technology. Am Soc Clin Oncol Educ Book 2021; 41:1-12. [PMID: 33793316 DOI: 10.1200/edbk_320593] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.
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Affiliation(s)
- Marte C Liefaard
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Esther H Lips
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jelle Wesseling
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA
| | - Bin Lou
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Tommaso Mansi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ
| | - Lajos Pusztai
- Yale Cancer Center, Yale School of Medicine, New Haven, CT
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Musall BC, Abdelhafez AH, Adrada BE, Candelaria RP, Mohamed RMM, Boge M, Le-Petross H, Arribas E, Lane DL, Spak DA, Leung JWT, Hwang KP, Son JB, Elshafeey NA, Mahmoud HS, Wei P, Sun J, Zhang S, White JB, Ravenberg EE, Litton JK, Damodaran S, Thompson AM, Moulder SL, Yang WT, Pagel MD, Rauch GM, Ma J. Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer. J Magn Reson Imaging 2021; 54:251-260. [PMID: 33586845 DOI: 10.1002/jmri.27557] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/26/2021] [Accepted: 01/27/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. PURPOSE To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). STUDY TYPE Prospective. POPULATION/SUBJECTS Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. FIELD STRENGTH/SEQUENCE A 3.0 T/3D fast spoiled gradient echo-based DCE MRI ASSESSMENT: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. STATISTICAL TESTS Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. RESULTS About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05). DATA CONCLUSION FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: 4.
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Affiliation(s)
- Benjamin C Musall
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Abeer H Abdelhafez
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Beatriz E Adrada
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rosalind P Candelaria
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Rania M M Mohamed
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Medine Boge
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Huong Le-Petross
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elsa Arribas
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Deanna L Lane
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David A Spak
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jessica W T Leung
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jong Bum Son
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Nabil A Elshafeey
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hagar S Mahmoud
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jia Sun
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shu Zhang
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason B White
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Elizabeth E Ravenberg
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jennifer K Litton
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Senthil Damodaran
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Stacy L Moulder
- Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Wei T Yang
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark D Pagel
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gaiane M Rauch
- Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Abstract
The National Cancer Institute's Quantitative Imaging Network (QIN) has thrived over the past 12 years with an emphasis on the development of image-based decision support software tools for improving measurements of imaging metrics. An overarching goal has been to develop advanced tools that could be translated into clinical trials to provide for improved prediction of response to therapeutic interventions. This article provides an overview of the successes in development and translation of new algorithms into the clinical workflow by the many research teams of the Quantitative Imaging Network.
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