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Bennani-Baiti BI, Weber M, Bernathova M, Clauser P, Kapetas P, Pinker K, Woitek R, Helbich T, Baltzer PTA. Pilot study: A simple CAD-based tool to detect breast cancer on MRI of the breast. Magn Reson Imaging 2024; 110:1-6. [PMID: 38479541 DOI: 10.1016/j.mri.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/06/2024] [Accepted: 03/10/2024] [Indexed: 04/01/2024]
Abstract
PURPOSE This pilot-study aims to assess, whether quantitatively assessed enhancing breast tissue as a percentage of the entire breast volume can serve as an indicator of breast cancer at breast MRI and whether the contrast-agent employed affects diagnostic efficacy. MATERIALS This retrospective IRB-approved study, included 39 consecutive patients, that underwent two subsequent breast MRI exams for suspicious findings at conventional imaging with 0.1 mmol/kg gadobenic and gadoteric acid. Two independent readers, blinded to the histopathological outcome, assessed unenhanced and early post-contrast images using computer-assisted software (Brevis, Siemens Healthcare). Diagnostic performance was statistically determined for percentage of ipsilateral voxel volume enhancement and for percentage of contralateral enhancing voxel volume subtracted from ipsilateral enhancing voxel volume after crosstabulation with the dichotomized histological outcome (benign/malignant). RESULTS Ipsilateral enhancing voxel volume versus histopathological outcome resulted in an AUC of 0.707 and 0.687 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.778 and 0.773 for gadoteric acid, reader 1 and 2, respectively. Accounting for background parenchymal enhancement by subtracting contralateral enhancing volume from ipsilateral enhancing voxel volume versus histolopathological outcome resulted in an AUC of 0.793 and 0.843 for gadobenic acid, reader 1 and 2, respectively and in an AUC of 0.692 and 0.662 for gadoteric acid, reader 1 and 2, respectively. Pairwise testing yielded no statistically significant difference both between readers and between contrast agents employed (p > 0.05). CONCLUSION Our proposed CAD algorithm, which quantitatively assesses enhancing breast tissue as a percentage of the entire breast volume, allows indicating the presence of breast cancer.
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Affiliation(s)
- Barbara I Bennani-Baiti
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria; Department of Radiology, University Hospital Krems, Mitterweg 10, Krems 3500, Austria; Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1090, Austria.
| | - Michael Weber
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria
| | - Maria Bernathova
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria
| | - Paola Clauser
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria
| | - Panagiotis Kapetas
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria; Department of Radiology, 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
| | - Ramona Woitek
- Medical Image Analysis and AI (MIAAI), Danube Private University, Krems 3500, Austria
| | - Thomas Helbich
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria
| | - Pascal T A Baltzer
- Karl Landsteiner University of Health Sciences, Dr. Karl-Dorrek-Straße 30, Krems 3500, Austria
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Daimiel Naranjo I, Bhowmik A, Basukala D, Lo Gullo R, Mazaheri Y, Kapetas P, Eskreis-Winkler S, Pinker K, Thakur SB. Assessment of Hypoxia in Breast Cancer: Emerging Functional MR Imaging and Spectroscopy Techniques and Clinical Applications. J Magn Reson Imaging 2024. [PMID: 38703143 DOI: 10.1002/jmri.29424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Breast cancer is one of the most prevalent forms of cancer affecting women worldwide. Hypoxia, a condition characterized by insufficient oxygen supply in tumor tissues, is closely associated with tumor aggressiveness, resistance to therapy, and poor clinical outcomes. Accurate assessment of tumor hypoxia can guide treatment decisions, predict therapy response, and contribute to the development of targeted therapeutic interventions. Over the years, functional magnetic resonance imaging (fMRI) and magnetic resonance spectroscopy (MRS) techniques have emerged as promising noninvasive imaging options for evaluating hypoxia in cancer. Such techniques include blood oxygen level-dependent (BOLD) MRI, oxygen-enhanced MRI (OE) MRI, chemical exchange saturation transfer (CEST) MRI, and proton MRS (1H-MRS). These may help overcome the limitations of the routinely used dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) techniques, contributing to better diagnosis and understanding of the biological features of breast cancer. This review aims to provide a comprehensive overview of the emerging functional MRI and MRS techniques for assessing hypoxia in breast cancer, along with their evolving clinical applications. The integration of these techniques in clinical practice holds promising implications for breast cancer management. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Department of Radiology, HM Hospitales, Madrid, Spain
- School of Medicine, Universidad CEU San Pablo, Madrid, Spain
| | - Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Dibash Basukala
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, New York, USA
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yousef Mazaheri
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Panagiotis Kapetas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Lo Gullo R, Brunekreef J, Marcus E, Han LK, Eskreis-Winkler S, Thakur SB, Mann R, Groot Lipman K, Teuwen J, Pinker K. AI Applications to Breast MRI: Today and Tomorrow. J Magn Reson Imaging 2024. [PMID: 38581127 DOI: 10.1002/jmri.29358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/07/2024] [Accepted: 03/09/2024] [Indexed: 04/08/2024] Open
Abstract
In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Joren Brunekreef
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Eric Marcus
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Lynn K Han
- Weill Cornell Medical College, New York-Presbyterian Hospital, New York City, New York, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Ritse Mann
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Kevin Groot Lipman
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Jonas Teuwen
- AI for Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
- Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
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Amir T, Pinker K, Sevilimedu V, Hughes M, Keating DT, Sung JS, Jochelson MS. Contrast-Enhanced Mammography for Women with Palpable Breast Abnormalities. Acad Radiol 2024; 31:1231-1238. [PMID: 37949703 DOI: 10.1016/j.acra.2023.10.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023]
Abstract
RATIONALE AND OBJECTIVES To examine the role of contrast-enhanced mammography (CEM) in the work-up of palpable breast abnormalities. MATERIALS AND METHODS In this single-center combination prospective-retrospective study, women with palpable breast abnormalities underwent CEM evaluation prospectively, comprising the acquisition of low energy (LE) images and recombined images (RI) which depict enhancement, followed by targeted ultrasound (US). Two independent readers retrospectively reviewed the imaging and assigned BI-RADS assessment based on LE alone, LE plus US, RI with LE plus US (CEM plus US), and RI alone. Pathology results or 1-year follow-up imaging served as the reference standard. RESULTS 237 women with 262 palpable abnormalities were included (mean age, 51 years). Of the 262 palpable abnormalities, 116/262 (44%) had no imaging correlate and 242/262 (92%) were benign. RI alone had better specificity compared to LE plus US (Reader 1, 94% versus 89% (p = 0.009); Reader 2, 93% versus 88% (p = 0.03)), better positive predictive value (Reader 1, 52% versus 42% (p = 0.04); Reader 2, 53% versus 42% (p = 0.04)), and better accuracy (Reader 1, 93% versus 89% (p = 0.05); Reader 2, 93% versus 90% (p = 0.06)). CEM plus US was not significantly different in performance metrics versus LE plus US. CONCLUSION RI had better specificity compared to LE in combination with US. There was no difference in performance between CEM plus US and LE plus US, likely reflecting the weight US carries in radiologist decision-making. However, the results indicate that the absence of enhancement on RI in the setting of palpable lesions may help avoid benign biopsies.
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Affiliation(s)
- Tali Amir
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.)
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, 10017, USA (V.S.)
| | - Mary Hughes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.)
| | - Delia T Keating
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.)
| | - Janice S Sung
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.)
| | - Maxine S Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, New York, 10065, USA (T.A., K.P., M.H., D.T.K., J.S.S., M.S.J.).
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5
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Cozzi A, Pinker K, Hidber A, Zhang T, Bonomo L, Lo Gullo R, Christianson B, Curti M, Rizzo S, Del Grande F, Mann RM, Schiaffino S, Panzer A. BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study. Radiology 2024; 311:e232133. [PMID: 38687216 PMCID: PMC11070611 DOI: 10.1148/radiol.232133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 05/02/2024]
Abstract
Background The performance of publicly available large language models (LLMs) remains unclear for complex clinical tasks. Purpose To evaluate the agreement between human readers and LLMs for Breast Imaging Reporting and Data System (BI-RADS) categories assigned based on breast imaging reports written in three languages and to assess the impact of discordant category assignments on clinical management. Materials and Methods This retrospective study included reports for women who underwent MRI, mammography, and/or US for breast cancer screening or diagnostic purposes at three referral centers. Reports with findings categorized as BI-RADS 1-5 and written in Italian, English, or Dutch were collected between January 2000 and October 2023. Board-certified breast radiologists and the LLMs GPT-3.5 and GPT-4 (OpenAI) and Bard, now called Gemini (Google), assigned BI-RADS categories using only the findings described by the original radiologists. Agreement between human readers and LLMs for BI-RADS categories was assessed using the Gwet agreement coefficient (AC1 value). Frequencies were calculated for changes in BI-RADS category assignments that would affect clinical management (ie, BI-RADS 0 vs BI-RADS 1 or 2 vs BI-RADS 3 vs BI-RADS 4 or 5) and compared using the McNemar test. Results Across 2400 reports, agreement between the original and reviewing radiologists was almost perfect (AC1 = 0.91), while agreement between the original radiologists and GPT-4, GPT-3.5, and Bard was moderate (AC1 = 0.52, 0.48, and 0.42, respectively). Across human readers and LLMs, differences were observed in the frequency of BI-RADS category upgrades or downgrades that would result in changed clinical management (118 of 2400 [4.9%] for human readers, 611 of 2400 [25.5%] for Bard, 573 of 2400 [23.9%] for GPT-3.5, and 435 of 2400 [18.1%] for GPT-4; P < .001) and that would negatively impact clinical management (37 of 2400 [1.5%] for human readers, 435 of 2400 [18.1%] for Bard, 344 of 2400 [14.3%] for GPT-3.5, and 255 of 2400 [10.6%] for GPT-4; P < .001). Conclusion LLMs achieved moderate agreement with human reader-assigned BI-RADS categories across reports written in three languages but also yielded a high percentage of discordant BI-RADS categories that would negatively impact clinical management. © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Andri Hidber
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Tianyu Zhang
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Luca Bonomo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Roberto Lo Gullo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Blake Christianson
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Marco Curti
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Stefania Rizzo
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | - Filippo Del Grande
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
| | | | | | - Ariane Panzer
- From the Imaging Institute of Southern Switzerland (IIMSI), Ente
Ospedaliero Cantonale, Via Tesserete 46, 6900 Lugano, Switzerland (A.C., L.B.,
M.C., S.R., F.D.G., S.S.); Breast Imaging Service, Department of Radiology,
Memorial Sloan Kettering Cancer Center, New York, NY (K.P., R.L.G., B.C.);
Faculty of Biomedical Sciences, Università della Svizzera Italiana,
Lugano, Switzerland (A.H., S.R., F.D.G., S.S.); Department of Radiology,
Netherlands Cancer Institute, Amsterdam, the Netherlands (T.Z., R.M.M.);
Department of Diagnostic Imaging, Radboud University Medical Center, Nijmegen,
the Netherlands (T.Z., R.M.M.); and GROW Research Institute for Oncology and
Reproduction, Maastricht University, Maastricht, the Netherlands (T.Z.)
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Eskreis-Winkler S, Jochelson MS, Pinker K, Sung JS, Comstock C. Caution on the CONTRRAST Trial. Radiology 2024; 311:e233188. [PMID: 38591972 PMCID: PMC11070605 DOI: 10.1148/radiol.233188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, 300 E 66th St, New York, NY 10065
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, 300 E 66th St, New York, NY 10065
| | - Katja Pinker
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, 300 E 66th St, New York, NY 10065
| | - Janice S. Sung
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, 300 E 66th St, New York, NY 10065
| | - Christopher Comstock
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New
York, 300 E 66th St, New York, NY 10065
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Bäuerle T, Dietzel M, Pinker K, Bonekamp D, Zhang KS, Schlemmer HP, Bannas P, Cyran CC, Eisenblätter M, Hilger I, Jung C, Schick F, Wegner F, Kiessling F. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. ROFO-FORTSCHR RONTG 2024; 196:354-362. [PMID: 37944934 DOI: 10.1055/a-2175-4446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
BACKGROUND Imaging biomarkers are quantitative parameters from imaging modalities, which are collected noninvasively, allow conclusions about physiological and pathophysiological processes, and may consist of single (monoparametric) or multiple parameters (bi- or multiparametric). METHOD This review aims to present the state of the art for the quantification of multimodal and multiparametric imaging biomarkers. Here, the use of biomarkers using artificial intelligence will be addressed and the clinical application of imaging biomarkers in breast and prostate cancers will be explained. For the preparation of the review article, an extensive literature search was performed based on Pubmed, Web of Science and Google Scholar. The results were evaluated and discussed for consistency and generality. RESULTS AND CONCLUSION Different imaging biomarkers (multiparametric) are quantified based on the use of complementary imaging modalities (multimodal) from radiology, nuclear medicine, or hybrid imaging. From these techniques, parameters are determined at the morphological (e. g., size), functional (e. g., vascularization or diffusion), metabolic (e. g., glucose metabolism), or molecular (e. g., expression of prostate specific membrane antigen, PSMA) level. The integration and weighting of imaging biomarkers are increasingly being performed with artificial intelligence, using machine learning algorithms. In this way, the clinical application of imaging biomarkers is increasing, as illustrated by the diagnosis of breast and prostate cancers. KEY POINTS · Imaging biomarkers are quantitative parameters to detect physiological and pathophysiological processes.. · Imaging biomarkers from multimodality and multiparametric imaging are integrated using artificial intelligence algorithms.. · Quantitative imaging parameters are a fundamental component of diagnostics for all tumor entities, such as for mammary and prostate carcinomas.. CITATION FORMAT · Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 - 362.
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Affiliation(s)
- Tobias Bäuerle
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Matthias Dietzel
- Institute of Radiology, University Medical Center Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - David Bonekamp
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | - Kevin S Zhang
- Department of Radiology, German Cancer Research Center, Heidelberg, Germany
| | | | - Peter Bannas
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Clemens C Cyran
- Institute of Radiology, University Medical Center München (LMU), München, Germany
| | - Michel Eisenblätter
- Diagnostische und Interventionelle Radiologie, Universitätsklinikum OWL, Universität Bielefeld Campus Klinikum Lippe, 32756 Detmold, Germany
| | - Ingrid Hilger
- Experimental Radiology, University Medical Center Jena, Germany
| | - Caroline Jung
- Institute of Diagnostic and Interventional Radiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Schick
- Experimental Radiology, University Medical Center Tübingen, Germany
| | - Franz Wegner
- Department of Radiology, University Hospital Schleswig-Holstein Campus Lübeck, Germany
| | - Fabian Kiessling
- Experimental Molecular Imaging, University Medical Center Aachen, Germany
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8
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Parikh R, Feigin KN, Sevilimedu V, Huayanay J, Pinker K, Horvat JV. Comparison of Axillary Lymph Nodes on Breast MRI Before and After COVID-19 Booster Vaccination. Acad Radiol 2024; 31:755-760. [PMID: 37037711 PMCID: PMC10017388 DOI: 10.1016/j.acra.2023.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023]
Abstract
RATIONALE AND OBJECTIVES Vaccine-related lymphadenopathy is a frequent finding following initial coronavirus disease 2019 (COVID-19) vaccination, but the frequency after COVID-19 booster vaccination is still unknown. In this study we compare axillary lymph node morphology on breast MRI before and after COVID-19 booster vaccination. MATERIALS AND METHODS This retrospective, single-center, IRB-approved study included patients who underwent breast MRI between October 2021 and December 2021 after the COVID-19 booster vaccination. The axillary lymph node with the greatest cortical thickness ipsilateral to the side of vaccination was measured on MRI after booster vaccination and before initial COVID-19 vaccination. Comparisons were made between patients with and without increase in cortical thickness of ≥ 0.2 cm. Continuous covariates were compared using Wilcoxon rank-sum test and categorical covariates were compared using Fisher's exact test. Multiple comparison adjustment was made using the Benjamini-Hochberg procedure. RESULTS All 128 patients were included. Twenty-four of 128 (19%) displayed an increase in lymph node cortical thickness of ≥ 0.2 cm. Patients who received the booster more recently were more likely to present cortical thickening, with a median of 9 days (IQR 5, 20) vs. 36 days (IQR 18, 59) (p < 0.001). Age (p = 0.5) and type of vaccine (p = 0.7) were not associated with thickening. No ipsilateral breast cancer or malignant lymphadenopathy were diagnosed on follow-up. CONCLUSION Axillary lymphadenopathy on breast MRI following COVID-19 booster vaccination is a frequent finding, especially in the first 3 weeks after vaccination. Additional evaluation or follow-up may be omitted in patients with low concern for malignancy.
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Affiliation(s)
- Rooshi Parikh
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St., New York, NY 10065, USA; The City University of New York (CUNY) School of Medicine, New York, New York
| | - Kimberly N Feigin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St., New York, NY 10065, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Jorge Huayanay
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St., New York, NY 10065, USA
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St., New York, NY 10065, USA
| | - Joao V Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St., New York, NY 10065, USA.
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9
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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Pinker K. Implementing AI in breast imaging: challenges to turn the gadget into gain. Eur Radiol 2024; 34:2093-2095. [PMID: 37667145 DOI: 10.1007/s00330-023-10205-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/07/2023] [Accepted: 08/17/2023] [Indexed: 09/06/2023]
Affiliation(s)
- Katja Pinker
- Department of Radiology - Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66Th Street, Room 707, New York, NY, 10065, USA.
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11
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Dishner KA, McRae-Posani B, Bhowmik A, Jochelson MS, Holodny A, Pinker K, Eskreis-Winkler S, Stember JN. A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research. J Magn Reson Imaging 2024; 59:450-480. [PMID: 37888298 PMCID: PMC10873125 DOI: 10.1002/jmri.29101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Katharine A. Dishner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- SUNY Downstate College of Medicine, Brooklyn, NY 11203
| | - Bala McRae-Posani
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Weill Cornell Medicine, New York, NY 10065
| | - Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrei Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY 10065
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - Joseph N. Stember
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
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12
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Steyerova P, Kaidar-Person O, Pinker K, Dubsky P. Breast cancer: evaluating the axilla before, during, and after therapy-new challenges. Eur Radiol 2024:10.1007/s00330-024-10621-x. [PMID: 38276979 DOI: 10.1007/s00330-024-10621-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/08/2024] [Accepted: 01/14/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Petra Steyerova
- Breast Cancer Screening and Diagnostic Centre, Department of Radiology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic.
| | - Orit Kaidar-Person
- Breast Cancer Radiation Therapy Unit, Sheba Medical Center, Ramat Gan, Israel
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Peter Dubsky
- Breast Center, Hirslanden Clinic St Anna, 6006, Lucerne, Switzerland
- Faculty of Health Sciences and Medicine, University of Lucerne, Lucerne, Switzerland
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13
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Hirsch L, Huang Y, Makse HA, Martinez DF, Hughes M, Eskreis-Winkler S, Pinker K, Morris E, Parra LC, Sutton EJ. [WITHDRAWN] Predicting breast cancer with AI for individual risk-adjusted MRI screening and early detection. ArXiv 2024:arXiv:2312.00067v2. [PMID: 38076513 PMCID: PMC10705586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2023]
Abstract
This paper has been withdrawn by Lukas Hirsch. Major revisions and rewriting in progress.
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14
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Bartsch SJ, Brožová K, Ehret V, Friske J, Fürböck C, Kenner L, Laimer-Gruber D, Helbich TH, Pinker K. Non-Contrast-Enhanced Multiparametric MRI of the Hypoxic Tumor Microenvironment Allows Molecular Subtyping of Breast Cancer: A Pilot Study. Cancers (Basel) 2024; 16:375. [PMID: 38254864 PMCID: PMC10813988 DOI: 10.3390/cancers16020375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/05/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
Tumor neoangiogenesis is an important hallmark of cancer progression, triggered by alternating selective pressures from the hypoxic tumor microenvironment. Non-invasive, non-contrast-enhanced multiparametric MRI combining blood-oxygen-level-dependent (BOLD) MRI, which depicts blood oxygen saturation, and intravoxel-incoherent-motion (IVIM) MRI, which captures intravascular and extravascular diffusion, can provide insights into tumor oxygenation and neovascularization simultaneously. Our objective was to identify imaging markers that can predict hypoxia-induced angiogenesis and to validate our findings using multiplexed immunohistochemical analyses. We present an in vivo study involving 36 female athymic nude mice inoculated with luminal A, Her2+, and triple-negative breast cancer cells. We used a high-field 9.4-tesla MRI system for imaging and subsequently analyzed the tumors using multiplex immunohistochemistry for CD-31, PDGFR-β, and Hif1-α. We found that the hyperoxic-BOLD-MRI-derived parameter ΔR2* discriminated luminal A from Her2+ and triple-negative breast cancers, while the IVIM-derived parameter fIVIM discriminated luminal A and Her2+ from triple-negative breast cancers. A comprehensive analysis using principal-component analysis of both multiparametric MRI- and mpIHC-derived data highlighted the differences between triple-negative and luminal A breast cancers. We conclude that multiparametric MRI combining hyperoxic BOLD MRI and IVIM MRI, without the need for contrast agents, offers promising non-invasive markers for evaluating hypoxia-induced angiogenesis.
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Affiliation(s)
- Silvester J. Bartsch
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Klára Brožová
- Department of Experimental and Laboratory Animal Pathology, Clinical Institute of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Viktoria Ehret
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, 1090 Vienna, Austria
| | - Joachim Friske
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Christoph Fürböck
- Computational Imaging Research Laboratory, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Lukas Kenner
- Department of Experimental and Laboratory Animal Pathology, Clinical Institute of Pathology, Medical University of Vienna, 1090 Vienna, Austria
- Unit of Laboratory Animal Pathology, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
- Comprehensive Cancer Center, Medical University Vienna, 1090 Vienna, Austria
- Christian Doppler Laboratory for Applied Metabolomics, Medical University Vienna, 1090 Vienna, Austria
- Center for Biomarker Research in Medicine (CBmed), 8010 Graz, Austria
| | - Daniela Laimer-Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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15
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Schiaffino S, Cozzi A, Clauser P, Giannotti E, Marino MA, van Nijnatten TJA, Baltzer PAT, Lobbes MBI, Mann RM, Pinker K, Fuchsjäger MH, Pijnappel RM. Current use and future perspectives of contrast-enhanced mammography (CEM): a survey by the European Society of Breast Imaging (EUSOBI). Eur Radiol 2024:10.1007/s00330-023-10574-7. [PMID: 38227202 DOI: 10.1007/s00330-023-10574-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 12/08/2023] [Accepted: 12/16/2023] [Indexed: 01/17/2024]
Abstract
OBJECTIVES To perform a survey among members of the European Society of Breast Imaging (EUSOBI) regarding the use of contrast-enhanced mammography (CEM). METHODS A panel of nine board-certified radiologists developed a 29-item online questionnaire, distributed to all EUSOBI members (inside and outside Europe) from January 25 to March 10, 2023. CEM implementation, examination protocols, reporting strategies, and current and future CEM indications were investigated. Replies were exploratively analyzed with descriptive and non-parametric statistics. RESULTS Among 434 respondents (74.9% from Europe), 50% (217/434) declared to use CEM, 155/217 (71.4%) seeing less than 200 CEMs per year. CEM use was associated with academic settings and high breast imaging workload (p < 0.001). The lack of CEM adoption was most commonly due to the perceived absence of a clinical need (65.0%) and the lack of resources to acquire CEM-capable systems (37.3%). CEM protocols varied widely, but most respondents (61.3%) had already adopted the 2022 ACR CEM BI-RADS® lexicon. CEM use in patients with contraindications to MRI was the most common current indication (80.6%), followed by preoperative staging (68.7%). Patients with MRI contraindications also represented the most commonly foreseen CEM indication (88.0%), followed by the work-up of inconclusive findings at non-contrast examinations (61.5%) and supplemental imaging in dense breasts (53.0%). Respondents declaring CEM use and higher CEM experience gave significantly more current (p = 0.004) and future indications (p < 0.001). CONCLUSIONS Despite a trend towards academic high-workload settings and its prevalent use in patients with MRI contraindications, CEM use and progressive experience were associated with increased confidence in the technique. CLINICAL RELEVANCE STATEMENT In this first survey on contrast-enhanced mammography (CEM) use and perspectives among the European Society of Breast Imaging (EUSOBI) members, the perceived absence of a clinical need chiefly drove the 50% CEM adoption rate. CEM adoption and progressive experience were associated with more extended current and future indications. KEY POINTS • Among the 434 members of the European Society of Breast Imaging who completed this survey, 50% declared to use contrast-enhanced mammography in clinical practice. • Due to the perceived absence of a clinical need, contrast-enhanced mammography (CEM) is still prevalently used as a replacement for MRI in patients with MRI contraindications. • The number of current and future CEM indications marked by respondents was associated with their degree of CEM experience.
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Affiliation(s)
- Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland.
| | - Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Elisabetta Giannotti
- Cambridge Breast Unit, Addenbrooke's Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Università degli Studi di Messina, Messina, Italy
| | - Thiemo J A van Nijnatten
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht, The Netherlands
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Vienna, Austria
| | - Marc B I Lobbes
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
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16
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Nissan N, Ochoa-Albiztegui RE, Fruchtman H, Gluskin J, Eskreis-Winkler S, Horvat JV, Kosmidou I, Meng A, Pinker K, Jochelson MS. Breast MRI in patients with implantable loop recorder: initial experience. Eur Radiol 2024; 34:155-164. [PMID: 37555957 DOI: 10.1007/s00330-023-10025-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/10/2023] [Accepted: 06/13/2023] [Indexed: 08/10/2023]
Abstract
OBJECTIVES To investigate the feasibility of breast MRI exams and guided biopsies in patients with an implantable loop recorder (ILR) as well as the impact ILRs may have on image interpretation. MATERIALS AND METHODS This retrospective study examined breast MRIs of patients with ILR, from April 2008 to September 2022. Radiological reports and electronic medical records were reviewed for demographic characteristics, safety concerns, and imaging findings. MR images were analyzed and compared statistically for artifact quantification on the various pulse sequences. RESULTS Overall, 40/82,778 (0.049%) MRIs during the study period included ILR. All MRIs were completed without early termination. No patient-related or device-related adverse events occurred. ILRs were most commonly located in the left lower-inner quadrant (64.6%). The main artifact was a signal intensity (SI) void in a dipole formation in the ILR bed with or without areas of peripheral high SI. Artifacts appeared greatest in the cranio-caudal axis (p < 0.001), followed by the anterior-posterior axis (p < 0.001), and then the right-left axis. High peripheral rim-like SI artifacts appeared on the post-contrast and subtracted T1-weighted images, mimicking suspicious enhancement. Artifacts were most prominent on diffusion-weighted (p < 0.001), followed by T2-weighted and T1-weighted images. In eight patients, suspicious findings were found on MRI, resulting in four additional malignant lesions. Of six patients with left breast cancer, the tumor was completely visible in five cases and partially obscured in one. CONCLUSION Breast MRI is feasible and safe among patients with ILR and may provide a significant diagnostic value, albeit with localized, characteristic artifacts. CLINICAL RELEVANCE STATEMENT Indicated breast MRI exams and guided biopsies can be safely performed in patients with implantable loop recorder. Nevertheless, radiologists should be aware of associated limitations including limited assessment of the inner left breast and pseudo-enhancement artifacts. KEY POINTS • Breast MRI in patients with an implantable loop recorder is an infrequent, feasible, and safe procedure. • Despite limited breast visualization of the implantable loop recorder bed and characteristic artifacts, MRI depicted additional lesions in 8/40 (20%) of cases, half of which were malignant. • Breast MRI in patients with an implantable loop recorder should be performed when indicated, taking into consideration typical associated artifacts.
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Affiliation(s)
- Noam Nissan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | | | - Hila Fruchtman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Jill Gluskin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Joao V Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Ioanna Kosmidou
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Alicia Meng
- Department of Radiology, 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
| | - Maxine S Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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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 DOI: 10.2214/ajr.23.29933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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, 300 E 66th St, New York, NY 10065
| | - Savannah C Partridge
- Department of Radiology, University of Washington School of Medicine, University of Washington, Seattle, WA
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY 10065
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 300 E 66th St, New York, NY 10065
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Cozzi A, Di Leo G, Houssami N, Gilbert FJ, Helbich TH, Álvarez Benito M, Balleyguier C, Bazzocchi M, Bult P, Calabrese M, Camps Herrero J, Cartia F, Cassano E, Clauser P, de Lima Docema MF, Depretto C, Dominelli V, Forrai G, Girometti R, Harms SE, Hilborne S, Ienzi R, Lobbes MBI, Losio C, Mann RM, Montemezzi S, Obdeijn IM, Aksoy Ozcan U, Pediconi F, Pinker K, Preibsch H, Raya Povedano JL, Rossi Saccarelli C, Sacchetto D, Scaperrotta GP, Schlooz M, Szabó BK, Taylor DB, Ulus SÖ, Van Goethem M, Veltman J, Weigel S, Wenkel E, Zuiani C, Sardanelli F. Preoperative breast MRI positively impacts surgical outcomes of needle biopsy-diagnosed pure DCIS: a patient-matched analysis from the MIPA study. Eur Radiol 2023:10.1007/s00330-023-10409-5. [PMID: 37999727 DOI: 10.1007/s00330-023-10409-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 09/16/2023] [Accepted: 10/11/2023] [Indexed: 11/25/2023]
Abstract
OBJECTIVES To investigate the influence of preoperative breast MRI on mastectomy and reoperation rates in patients with pure ductal carcinoma in situ (DCIS). METHODS The MIPA observational study database (7245 patients) was searched for patients aged 18-80 years with pure unilateral DCIS diagnosed at core needle or vacuum-assisted biopsy (CNB/VAB) and planned for primary surgery. Patients who underwent preoperative MRI (MRI group) were matched (1:1) to those who did not receive MRI (noMRI group) according to 8 confounding covariates that drive referral to MRI (age; hormonal status; familial risk; posterior-to-nipple diameter; BI-RADS category; lesion diameter; lesion presentation; surgical planning at conventional imaging). Surgical outcomes were compared between the matched groups with nonparametric statistics after calculating odds ratios (ORs). RESULTS Of 1005 women with pure unilateral DCIS at CNB/VAB (507 MRI group, 498 noMRI group), 309 remained in each group after matching. First-line mastectomy rate in the MRI group was 20.1% (62/309 patients, OR 2.03) compared to 11.0% in the noMRI group (34/309 patients, p = 0.003). The reoperation rate was 10.0% in the MRI group (31/309, OR for reoperation 0.40) and 22.0% in the noMRI group (68/309, p < 0.001), with a 2.53 OR of avoiding reoperation in the MRI group. The overall mastectomy rate was 23.3% in the MRI group (72/309, OR 1.40) and 17.8% in the noMRI group (55/309, p = 0.111). CONCLUSIONS Compared to those going directly to surgery, patients with pure DCIS at CNB/VAB who underwent preoperative MRI had a higher OR for first-line mastectomy but a substantially lower OR for reoperation. CLINICAL RELEVANCE STATEMENT When confounding factors behind MRI referral are accounted for in the comparison of patients with CNB/VAB-diagnosed pure unilateral DCIS, preoperative MRI yields a reduction of reoperations that is more than twice as high as the increase in overall mastectomies. KEY POINTS • Confounding factors cause imbalance when investigating the influence of preoperative MRI on surgical outcomes of pure DCIS. • When patient matching is applied to women with pure unilateral DCIS, reoperation rates are significantly reduced in women who underwent preoperative MRI. • The reduction of reoperations brought about by preoperative MRI is more than double the increase in overall mastectomies.
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Affiliation(s)
- Andrea Cozzi
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
- Imaging Institute of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Nehmat Houssami
- The Daffodil Centre, Faculty of Medicine and Health, The University of Sydney (Joint Venture with Cancer Council NSW), Sydney, Australia
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Thomas H Helbich
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Division of Molecular and Structural Preclinical Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Corinne Balleyguier
- Department of Radiology, Institut Gustave Roussy, Villejuif, France
- Biomaps, UMR1281 INSERM, CEA, CNRS, Université Paris-Saclay, Villejuif, France
| | - Massimo Bazzocchi
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Peter Bult
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Massimo Calabrese
- Unit of Oncological and Breast Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Julia Camps Herrero
- Department of Radiology, Hospital Universitario de La Ribera, Alzira, Spain
- Ribera Salud Hospitals, Valencia, Spain
| | - Francesco Cartia
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Paola Clauser
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Catherine Depretto
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gábor Forrai
- Department of Radiology, MHEK Teaching Hospital, Semmelweis University, Budapest, Hungary
- Department of Radiology, Duna Medical Center, GE-RAD Kft, Budapest, Hungary
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Steven E Harms
- Breast Center of Northwest Arkansas, Fayetteville, AR, USA
| | - Sarah Hilborne
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Raffaele Ienzi
- Department of Radiology, Di.Bi.MED, Policlinico Universitario Paolo Giaccone Università degli Studi di Palermo, Palermo, Italy
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Claudio Losio
- Department of Breast Radiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefania Montemezzi
- Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy
| | - Inge-Marie Obdeijn
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Umit Aksoy Ozcan
- Department of Radiology, Acıbadem Atasehir Hospital, Istanbul, Turkey
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma "La Sapienza", Rome, Italy
| | - Katja Pinker
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | | | | | - Daniela Sacchetto
- Kiwifarm S.R.L., La Morra, Italy
- Disaster Medicine Service 118, ASL CN1, Levaldigi, Italy
| | | | - Margrethe Schlooz
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Botond K Szabó
- Department of Radiology, Barking Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Donna B Taylor
- Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia
- Department of Radiology, Royal Perth Hospital, Perth, Australia
| | - Sila Ö Ulus
- Department of Radiology, Acıbadem Atasehir Hospital, Istanbul, Turkey
| | - Mireille Van Goethem
- Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Department of Radiology, Multidisciplinary Breast Clinic, Antwerp University Hospital, University of Antwerp, Antwerp, Belgium
| | - Jeroen Veltman
- Maatschap Radiologie Oost-Nederland, Oldenzaal, The Netherlands
| | - Stefanie Weigel
- Clinic for Radiology and Reference Center for Mammography, University of Münster, Münster, Germany
| | - Evelyn Wenkel
- Department of Radiology, University Hospital of Erlangen, Erlangen, Germany
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy.
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
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Mendes Serrão E, Klug M, Moloney BM, Jhaveri A, Lo Gullo R, Pinker K, Luker G, Haider MA, Shinagare AB, Liu X. Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know. Radiol Imaging Cancer 2023; 5:e220153. [PMID: 37921555 DOI: 10.1148/rycan.220153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Eva Mendes Serrão
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Maximiliano Klug
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Brian M Moloney
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Aaditeya Jhaveri
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Roberto Lo Gullo
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Katja Pinker
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Gary Luker
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Masoom A Haider
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Atul B Shinagare
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
| | - Xiaoyang Liu
- From the Joint Department of Medical Imaging, University Medical Imaging Toronto, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2 (E.M.S., A.J., M.A.H., X.L.); Division of Diagnostic Imaging, Sheba Medical Center, Tel Aviv University, Tel Aviv, Israel (M.K.); Department of Radiology, The Christie NHS Trust, Manchester, England (B.M.M.); Department of Radiology, Breast Imaging Service, Memorial Sloan-Kettering Cancer Center, Weill Medical College of Cornell University, New York, NY (R.L.G., K.P.); Center for Molecular Imaging, Department of Radiology, University of Michigan, Ann Arbor, Mich (G.L.); Lunenfeld Tanenbaum Research Institute, Sinai Health System, Mount Sinai Hospital, Toronto, Ontario, Canada (M.A.H.); and Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (A.B.S.)
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Romeo V, Kapetas P, Clauser P, Rasul S, Cuocolo R, Caruso M, Helbich TH, Baltzer PAT, Pinker K. Simultaneous 18F-FDG PET/MRI Radiomics and Machine Learning Analysis of the Primary Breast Tumor for the Preoperative Prediction of Axillary Lymph Node Status in Breast Cancer. Cancers (Basel) 2023; 15:5088. [PMID: 37894455 PMCID: PMC10604950 DOI: 10.3390/cancers15205088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/08/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
In this prospective study, 117 female patients (mean age = 53 years) with 127 histologically proven breast cancer lesions (lymph node (LN) positive = 85, LN negative = 42) underwent simultaneous 18F-FDG PET/MRI of the breast. Quantitative parameters were calculated from dynamic contrast-enhanced (DCE) imaging (tumor Mean Transit Time, Volume Distribution, Plasma Flow), diffusion-weighted imaging (DWI) (tumor ADCmean), and PET (tumor SUVmax, mean and minimum, SUVmean of ipsilateral breast parenchyma). Manual whole-lesion segmentation was also performed on DCE, T2-weighted, DWI, and PET images, and radiomic features were extracted. The dataset was divided into a training (70%) and a test set (30%). Multi-step feature selection was performed, and a support vector machine classifier was trained and tested for predicting axillary LN status. 13 radiomic features from DCE, DWI, T2-weighted, and PET images were selected for model building. The classifier obtained an accuracy of 79.8 (AUC = 0.798) in the training set and 78.6% (AUC = 0.839), with sensitivity and specificity of 67.9% and 100%, respectively, in the test set. A machine learning-based radiomics model comprising 18F-FDG PET/MRI radiomic features extracted from the primary breast cancer lesions allows high accuracy in non-invasive identification of axillary LN metastasis.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80138 Naples, Italy; (V.R.); (M.C.)
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Sazan Rasul
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria;
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84081 Baronissi, Italy;
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80131 Naples, Italy
| | - Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, 80138 Naples, Italy; (V.R.); (M.C.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
- Department of Biomedical Imaging and Image-guided Therapy, Division of Structural Preclinical Imaging, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General and Pediatric Radiology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Wien, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
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Eskreis-Winkler S, Sung JS, Dixon L, Monga N, Jindal R, Simmons A, Thakur S, Sevilimedu V, Sutton E, Comstock C, Feigin K, Pinker K. High-Temporal/High-Spatial Resolution Breast Magnetic Resonance Imaging Improves Diagnostic Accuracy Compared With Standard Breast Magnetic Resonance Imaging in Patients With High Background Parenchymal Enhancement. J Clin Oncol 2023; 41:4747-4755. [PMID: 37561962 PMCID: PMC10602549 DOI: 10.1200/jco.22.00635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 01/05/2023] [Accepted: 06/16/2023] [Indexed: 08/12/2023] Open
Abstract
PURPOSE To compare breast magnetic resonance imaging (MRI) diagnostic performance using a standard high-spatial resolution protocol versus a simultaneous high-temporal/high-spatial resolution (HTHS) protocol in women with high levels of background parenchymal enhancement (BPE). MATERIALS AND METHODS We conducted a retrospective study of contrast-enhanced breast MRIs performed at our institution before and after the introduction of the HTHS protocol. We compared diagnostic performance of the HTHS and standard protocol by comparing cancer detection rate (CDR) and positive predictive value of biopsy (PPV3) among women with high BPE (ie, marked or moderate). RESULTS Among women with high BPE, the HTHS protocol demonstrated increased CDR (23.6 per 1,000 patients v 7.9 per 1,000 patients; P = 0. 013) and increased PPV3 (16.0% v 6.3%; P = .021) compared with the standard protocol. This corresponded to a 9.8% (95% CI, 1.29 to 18.3) decrease in the proportion of unnecessary biopsies among high-BPE patients and an additional cancer yield of 15.7 per 1,000 patients (95% CI, 1.3 to 18.3). CONCLUSION Among women with high BPE, HTHS MRI improved diagnostic performance, leading to an additional cancer yield of 15.7 cancers per 1,000 women and concomitantly decreasing unnecessary biopsies by 9.8%. A multisite prospective trial is warranted to confirm these findings and to pave the way for more widespread clinical implementation.
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Affiliation(s)
| | - Janice S. Sung
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Linden Dixon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Natasha Monga
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ragni Jindal
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Sunitha Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Elizabeth Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Kimberly Feigin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
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22
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Sevilimedu V, Pangan G, Pinker K. A Review of the Estimation of Sensitivity and Specificity in the Context of Time-Dependent Outcomes. J Magn Reson Imaging 2023:10.1002/jmri.29083. [PMID: 37850689 PMCID: PMC11024058 DOI: 10.1002/jmri.29083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
Time is an essential element in the field of survival analysis. Time to an event is essential in adjudicating diagnostic utility and determining the effectiveness of treatment. However, time-to-event outcomes-in a sense-are complicated to deal with, since they involve both a binary component, which is the outcome itself (death or recurrence) and a continuous component, which is the time for the occurrence of the outcome. In such scenarios, simpler metrics such as sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are insufficient to establish the utility of a diagnostic marker. Given the above constraint, this review discusses established ways in which sensitivity and specificity can be determined and are therefore sufficient in establishing utility in the context of time-dependent outcomes. This review also discusses how studies investigating the sensitivity and specificity of a diagnostic marker in the context of time-dependent outcomes can be improved through the use of existing user-friendly statistical software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Varadan Sevilimedu
- Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
| | - Giuseppe Pangan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
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23
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Burgess MT, Gluskin J, Pinker K. From bedside to portable and wearable: development of a conformable ultrasound patch for deep breast tissue imaging. Mol Oncol 2023; 17:1947-1949. [PMID: 37766480 PMCID: PMC10552885 DOI: 10.1002/1878-0261.13531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 09/29/2023] Open
Abstract
A breakthrough study from Du et al. has developed a wearable, ultrasound imaging patch for standardized and reproducible breast tissue imaging. The technology utilizes a honeycomb patch design to facilitate guided movement of the ultrasound array, enabling comprehensive, multiangle breast imaging. The system was validated in vitro and in vivo with a single human subject and has the potential for early-stage breast cancer detection. This study addressed the current limitations of wearable ultrasound technologies, including imaging over large, curvilinear organs and integration of superior piezoelectric materials for high-performance ultrasound arrays. The transition of ultrasound from the bedside to portable and wearable devices will pave the way for integration with big data collection, such as artificial intelligence (AI)-based diagnosis and personalized ultrasonographic profile generation, for rapid and objective measurements. This advancement is especially important in the context of breast cancer, where early diagnosis and assessment of medical therapy responses are paramount to patient care.
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Affiliation(s)
- Mark T. Burgess
- Department of Medical PhysicsMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Jill Gluskin
- Department of Radiology, Breast Imaging ServiceMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
| | - Katja Pinker
- Department of Radiology, Breast Imaging ServiceMemorial Sloan Kettering Cancer CenterNew YorkNYUSA
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24
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Abstract
New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, Naples 80138, Italy.
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, 160 East 34th Street, New York, NY 10016, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY 10065, USA
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25
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Bhowmik A, Monga N, Belen K, Varela K, Sevilimedu V, Thakur SB, Martinez DF, Sutton EJ, Pinker K, Eskreis-Winkler S. Automated Triage of Screening Breast MRI Examinations in High-Risk Women Using an Ensemble Deep Learning Model. Invest Radiol 2023; 58:710-719. [PMID: 37058323 DOI: 10.1097/rli.0000000000000976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
OBJECTIVES The aim of the study is to develop and evaluate the performance of a deep learning (DL) model to triage breast magnetic resonance imaging (MRI) findings in high-risk patients without missing any cancers. MATERIALS AND METHODS In this retrospective study, 16,535 consecutive contrast-enhanced MRIs performed in 8354 women from January 2013 to January 2019 were collected. From 3 New York imaging sites, 14,768 MRIs were used for the training and validation data set, and 80 randomly selected MRIs were used for a reader study test data set. From 3 New Jersey imaging sites, 1687 MRIs (1441 screening MRIs and 246 MRIs performed in recently diagnosed breast cancer patients) were used for an external validation data set. The DL model was trained to classify maximum intensity projection images as "extremely low suspicion" or "possibly suspicious." Deep learning model evaluation (workload reduction, sensitivity, specificity) was performed on the external validation data set, using a histopathology reference standard. A reader study was performed to compare DL model performance to fellowship-trained breast imaging radiologists. RESULTS In the external validation data set, the DL model triaged 159/1441 of screening MRIs as "extremely low suspicion" without missing a single cancer, yielding a workload reduction of 11%, a specificity of 11.5%, and a sensitivity of 100%. The model correctly triaged 246/246 (100% sensitivity) of MRIs in recently diagnosed patients as "possibly suspicious." In the reader study, 2 readers classified MRIs with a specificity of 93.62% and 91.49%, respectively, and missed 0 and 1 cancer, respectively. On the other hand, the DL model classified MRIs with a specificity of 19.15% and missed 0 cancers, highlighting its potential use not as an independent reader but as a triage tool. CONCLUSIONS Our automated DL model triages a subset of screening breast MRIs as "extremely low suspicion" without misclassifying any cancer cases. This tool may be used to reduce workload in standalone mode, to shunt low suspicion cases to designated radiologists or to the end of the workday, or to serve as base model for other downstream AI tools.
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26
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Bartsch SJ, Ehret V, Friske J, Fröhlich V, Laimer-Gruber D, Helbich TH, Pinker K. Hyperoxic BOLD-MRI-Based Characterization of Breast Cancer Molecular Subtypes Is Independent of the Supplied Amount of Oxygen: A Preclinical Study. Diagnostics (Basel) 2023; 13:2946. [PMID: 37761313 PMCID: PMC10530249 DOI: 10.3390/diagnostics13182946] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Hyperoxic BOLD-MRI targeting tumor hypoxia may provide imaging biomarkers that represent breast cancer molecular subtypes without the use of injected contrast agents. However, the diagnostic performance of hyperoxic BOLD-MRI using different levels of oxygen remains unclear. We hypothesized that molecular subtype characterization with hyperoxic BOLD-MRI is feasible independently of the amount of oxygen. Twenty-three nude mice that were inoculated into the flank with luminal A (n = 9), Her2+ (n = 5), and triple-negative (n = 9) human breast cancer cells were imaged using a 9.4 T Bruker BioSpin system. During BOLD-MRI, anesthesia was supplemented with four different levels of oxygen (normoxic: 21%; hyperoxic: 41%, 71%, 100%). The change in the spin-spin relaxation rate in relation to the normoxic state, ΔR2*, dependent on the amount of erythrocyte-bound oxygen, was calculated using in-house MATLAB code. ΔR2* was significantly different between luminal A and Her2+ as well as between luminal A and triple-negative breast cancer, reflective of the less aggressive luminal A breast cancer's ability to better deliver oxygen-rich hemoglobin to its tissue. Differences in ΔR2* between subtypes were independent of the amount of oxygen, with robust distinction already achieved with 41% oxygen. In conclusion, hyperoxic BOLD-MRI may be used as a biomarker for luminal A breast cancer identification without the use of exogenous contrast agents.
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Affiliation(s)
- Silvester J. Bartsch
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria; (S.J.B.); (J.F.); (D.L.-G.); (T.H.H.)
| | - Viktoria Ehret
- Department of Internal Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, 1090 Vienna, Austria;
| | - Joachim Friske
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria; (S.J.B.); (J.F.); (D.L.-G.); (T.H.H.)
| | - Vanessa Fröhlich
- Fachhochschule Wiener Neustadt GmbH, University of Applied Sciences, 2700 Wiener Neustadt, Austria;
| | - Daniela Laimer-Gruber
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria; (S.J.B.); (J.F.); (D.L.-G.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria; (S.J.B.); (J.F.); (D.L.-G.); (T.H.H.)
| | - Katja Pinker
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Structural and Molecular Preclinical Imaging, Medical University of Vienna, 1090 Vienna, Austria; (S.J.B.); (J.F.); (D.L.-G.); (T.H.H.)
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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27
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Cozzi A, Di Leo G, Houssami N, Gilbert FJ, Helbich TH, Álvarez Benito M, Balleyguier C, Bazzocchi M, Bult P, Calabrese M, Camps Herrero J, Cartia F, Cassano E, Clauser P, de Lima Docema MF, Depretto C, Dominelli V, Forrai G, Girometti R, Harms SE, Hilborne S, Ienzi R, Lobbes MBI, Losio C, Mann RM, Montemezzi S, Obdeijn IM, Ozcan UA, Pediconi F, Pinker K, Preibsch H, Raya Povedano JL, Rossi Saccarelli C, Sacchetto D, Scaperrotta GP, Schlooz M, Szabó BK, Taylor DB, Ulus ÖS, Van Goethem M, Veltman J, Weigel S, Wenkel E, Zuiani C, Sardanelli F. Screening and diagnostic breast MRI: how do they impact surgical treatment? Insights from the MIPA study. Eur Radiol 2023; 33:6213-6225. [PMID: 37138190 PMCID: PMC10415233 DOI: 10.1007/s00330-023-09600-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 01/19/2023] [Accepted: 02/22/2023] [Indexed: 05/05/2023]
Abstract
OBJECTIVES To report mastectomy and reoperation rates in women who had breast MRI for screening (S-MRI subgroup) or diagnostic (D-MRI subgroup) purposes, using multivariable analysis for investigating the role of MRI referral/nonreferral and other covariates in driving surgical outcomes. METHODS The MIPA observational study enrolled women aged 18-80 years with newly diagnosed breast cancer destined to have surgery as the primary treatment, in 27 centres worldwide. Mastectomy and reoperation rates were compared using non-parametric tests and multivariable analysis. RESULTS A total of 5828 patients entered analysis, 2763 (47.4%) did not undergo MRI (noMRI subgroup) and 3065 underwent MRI (52.6%); of the latter, 2441/3065 (79.7%) underwent MRI with preoperative intent (P-MRI subgroup), 510/3065 (16.6%) D-MRI, and 114/3065 S-MRI (3.7%). The reoperation rate was 10.5% for S-MRI, 8.2% for D-MRI, and 8.5% for P-MRI, while it was 11.7% for noMRI (p ≤ 0.023 for comparisons with D-MRI and P-MRI). The overall mastectomy rate (first-line mastectomy plus conversions from conserving surgery to mastectomy) was 39.5% for S-MRI, 36.2% for P-MRI, 24.1% for D-MRI, and 18.0% for noMRI. At multivariable analysis, using noMRI as reference, the odds ratios for overall mastectomy were 2.4 (p < 0.001) for S-MRI, 1.0 (p = 0.957) for D-MRI, and 1.9 (p < 0.001) for P-MRI. CONCLUSIONS Patients from the D-MRI subgroup had the lowest overall mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). This analysis offers an insight into how the initial indication for MRI affects the subsequent surgical treatment of breast cancer. KEY POINTS • Of 3065 breast MRI examinations, 79.7% were performed with preoperative intent (P-MRI), 16.6% were diagnostic (D-MRI), and 3.7% were screening (S-MRI) examinations. • The D-MRI subgroup had the lowest mastectomy rate (24.1%) among MRI subgroups and the lowest reoperation rate (8.2%) together with P-MRI (8.5%). • The S-MRI subgroup had the highest mastectomy rate (39.5%) which aligns with higher-than-average risk in this subgroup, with a reoperation rate (10.5%) not significantly different to that of all other subgroups.
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Affiliation(s)
- Andrea Cozzi
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Nehmat Houssami
- The Daffodil Centre, Faculty of Medicine and Health, The University of Sydney (Joint Venture with Cancer Council NSW), Sydney, Australia
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | | | - Corinne Balleyguier
- Department of Radiology, Institut Gustave Roussy, Villejuif, France
- BioMaps (UMR1281), INSERM, CEA, CNRS, Université Paris-Saclay, Villejuif, France
| | - Massimo Bazzocchi
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Peter Bult
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Massimo Calabrese
- Unit of Oncological and Breast Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Francesco Cartia
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | | | - Catherine Depretto
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gábor Forrai
- Department of Radiology, MHEK Teaching Hospital, Semmelweis University, Budapest, Hungary
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Steven E Harms
- Breast Center of Northwest Arkansas, Fayetteville, AR, USA
| | - Sarah Hilborne
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Raffaele Ienzi
- Department of Radiology, Di.Bi.MED, Policlinico Universitario Paolo Giaccone, Università degli Studi di Palermo, Palermo, Italy
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudio Losio
- Department of Breast Radiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefania Montemezzi
- Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy
| | - Inge-Marie Obdeijn
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Umit A Ozcan
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma "La Sapienza", Rome, Italy
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | | | | | - Daniela Sacchetto
- Kiwifarm S.r.l, La Morra, Italy
- Disaster Medicine Service 118, ASL CN1, Saluzzo, Italy
- CRIMEDIM, Research Center in Emergency and Disaster Medicine, Università degli Studi del Piemonte Orientale "Amedeo Avogadro", Novara, Italy
| | | | - Margrethe Schlooz
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Botond K Szabó
- Department of Radiology, Barking Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Donna B Taylor
- Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia
- Department of Radiology, Royal Perth Hospital, Perth, Australia
| | - Özden S Ulus
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Mireille Van Goethem
- Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Department of Radiology, Multidisciplinary Breast Clinic, Antwerp University Hospital, University of Antwerp, Antwerpen, Belgium
| | - Jeroen Veltman
- Maatschap Radiologie Oost-Nederland, Oldenzaal, The Netherlands
| | - Stefanie Weigel
- Institute of Clinical Radiology and Reference Center for Mammography, University of Münster, Münster, Germany
| | - Evelyn Wenkel
- Department of Radiology, University Hospital of Erlangen, Erlangen, Germany
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine, Ospedale Universitario S. Maria della Misericordia, Università degli Studi di Udine, Udine, Italy
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy.
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
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28
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Bickel H, Clauser P, Pinker K, Helbich T, Biondic I, Brkljacic B, Dietzel M, Ivanac G, Krug B, Moschetta M, Neuhaus V, Preidler K, Baltzer P. Introduction of a breast apparent diffusion coefficient category system (ADC-B) derived from a large multicenter MRI database. Eur Radiol 2023; 33:5400-5410. [PMID: 37166495 PMCID: PMC10326122 DOI: 10.1007/s00330-023-09675-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/27/2023] [Accepted: 03/14/2023] [Indexed: 05/12/2023]
Abstract
OBJECTIVES To develop an intuitive and generally applicable system for the reporting, assessment, and documentation of ADC to complement standard BI-RADS criteria. METHODS This was a multicentric, retrospective analysis of 11 independently conducted institutional review board-approved studies from seven institutions performed between 2007 and 2019. Breast Apparent Diffusion coefficient (ADC-B) categories comprised ADC-B0 (ADC non-diagnostic), ADC-B1 (no enhancing lesion), and ADC-B2-5. The latter was defined by plotting ADC versus cumulative malignancy rates. Statistics comprised ANOVA with post hoc testing and ROC analysis. p values ≤ 0.05 were considered statistically significant. RESULTS A total of 1625 patients (age: 55.9 years (± 13.8)) with 1736 pathologically verified breast lesions were included. The mean ADC (× 10-3 mm2/s) differed significantly between benign (1.45, SD .40) and malignant lesions (.95, SD .39), and between invasive (.92, SD .22) and in situ carcinomas (1.18, SD .30) (p < .001). The following ADC-B categories were identified: ADC-B0-ADC cannot be assessed; ADC-B1-no contrast-enhancing lesion; ADC-B2-ADC ≥ 1.9 (cumulative malignancy rate < 0.1%); ADC-B3-ADC 1.5 to < 1.9 (0.1-1.7%); ADC-B4-ADC 1.0 to < 1.5 (10-24.5%); and ADC-B5-ADC < 1.0 (> 24.5%). At the latter threshold, a positive predictive value of 95.8% (95% CI 0.94-0.97) for invasive versus non-invasive breast carcinomas was reached. CONCLUSIONS The breast apparent diffusion coefficient system (ADC-B) provides a simple and widely applicable categorization scheme for assessment, documentation, and reporting of apparent diffusion coefficient values in contrast-enhancing breast lesions on MRI. CLINICAL RELEVANCE STATEMENT The ADC-B system, based on diverse MRI examinations, is clinically relevant for stratifying breast cancer risk via apparent diffusion coefficient measurements, and complements BI-RADS for improved clinical decision-making and patient outcomes. KEY POINTS • The breast apparent diffusion coefficient category system (ADC-B) is a simple tool for the assessment, documentation, and reporting of ADC values in contrast-enhancing breast lesions on MRI. • The categories comprise ADC-B0 for non-diagnostic examinations, ADC-B1 for examinations without an enhancing lesion, and ADC-B2-5 for enhancing lesions with an increasing malignancy rate. • The breast apparent diffusion coefficient category system may be used to complement BI-RADS in clinical decision-making.
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Affiliation(s)
- Hubert Bickel
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Paola Clauser
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Katja Pinker
- Evelyn H. Lauder Breast Center, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY, 10065, USA
| | - Thomas Helbich
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Iva Biondic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Boris Brkljacic
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Matthias Dietzel
- Dpt. of Radiology, University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | - Gordana Ivanac
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Avenija Gojka Šuška 6, 10 000, Zagreb, Croatia
| | - Barbara Krug
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Marco Moschetta
- Dpt. of Emergency and Organ Transplantation-Breast Care Unit, Aldo Moro University of Bari Medical School, Piazza Giulio Cesare 11, 70124, Bari, Italy
| | - Victor Neuhaus
- Dpt. of Diagnostic and Interventional Radiology, University Hospital Cologne, Kerpener Str. 62, 50937, Cologne, Germany
| | - Klaus Preidler
- Diagnosezentrum Meidling, Meidlinger Hauptstr. 7 - 9, 1120, Vienna, Austria
| | - Pascal Baltzer
- Dpt. of Biomedical Imaging and Image Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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29
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Schiaffino S, Pinker K, Cozzi A, Magni V, Athanasiou A, Baltzer PAT, Camps Herrero J, Clauser P, Fallenberg EM, Forrai G, Fuchsjäger MH, Gilbert FJ, Helbich T, Kilburn-Toppin F, Kuhl CK, Lesaru M, Mann RM, Panizza P, Pediconi F, Sardanelli F, Sella T, Thomassin-Naggara I, Zackrisson S, Pijnappel RM. European Society of Breast Imaging (EUSOBI) guidelines on the management of axillary lymphadenopathy after COVID-19 vaccination: 2023 revision. Insights Imaging 2023; 14:126. [PMID: 37466753 DOI: 10.1186/s13244-023-01453-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/14/2023] [Indexed: 07/20/2023] Open
Abstract
Axillary lymphadenopathy is a common side effect of COVID-19 vaccination, leading to increased imaging-detected asymptomatic and symptomatic unilateral axillary lymphadenopathy. This has threatened to negatively impact the workflow of breast imaging services, leading to the release of ten recommendations by the European Society of Breast Imaging (EUSOBI) in August 2021. Considering the rapidly changing scenario and data scarcity, these initial recommendations kept a highly conservative approach. As of 2023, according to newly acquired evidence, EUSOBI proposes the following updates, in order to reduce unnecessary examinations and avoid delaying necessary examinations. First, recommendation n. 3 has been revised to state that breast examinations should not be delayed or rescheduled because of COVID-19 vaccination, as evidence from the first pandemic waves highlights how delayed or missed screening tests have a negative effect on breast cancer morbidity and mortality, and that there is a near-zero risk of subsequent malignant findings in asymptomatic patients who have unilateral lymphadenopathy and no suspicious breast findings. Second, recommendation n. 7 has been revised to simplify follow-up strategies: in patients without breast cancer history and no imaging findings suspicious for cancer, symptomatic and asymptomatic imaging-detected unilateral lymphadenopathy on the same side of recent COVID-19 vaccination (within 12 weeks) should be classified as a benign finding (BI-RADS 2) and no further work-up should be pursued. All other recommendations issued by EUSOBI in 2021 remain valid.
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Affiliation(s)
- Simone Schiaffino
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Katja Pinker
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Andrea Cozzi
- Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland
| | - Veronica Magni
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | | | - Pascal A T Baltzer
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Paola Clauser
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Eva M Fallenberg
- Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, Technical University of Munich (TUM), Munich, Germany
| | - Gabor Forrai
- Department of Radiology, Duna Medical Center, Budapest, Hungary
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Thomas Helbich
- Division of General and Paediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Christiane K Kuhl
- University Hospital of Aachen, Rheinisch-Westfälische Technische Hochschule, Aachen, Germany
| | - Mihai Lesaru
- Radiology and Imaging Laboratory, Fundeni Institute, Bucharest, Romania
| | - Ritse M Mann
- Department of Radiology, Radboud University Medical Centre, Nijmegen, The Netherlands
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Pietro Panizza
- Breast Imaging Unit, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma "La Sapienza", Rome, Italy
| | - Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Tamar Sella
- Department of Diagnostic Imaging, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | | | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
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Kaidar-Person O, Pfob A, Gentilini OD, Borisch B, Bosch A, Cardoso MJ, Curigliano G, De Boniface J, Denkert C, Hauser N, Heil J, Knauer M, Kühn T, Lee HB, Loibl S, Mannhart M, Meattini I, Montagna G, Pinker K, Poulakaki F, Rubio IT, Sager P, Steyerova P, Tausch C, Tramm T, Vrancken Peeters MJ, Wyld L, Yu JH, Weber WP, Poortmans P, Dubsky P. The Lucerne Toolbox 2 to optimise axillary management for early breast cancer: a multidisciplinary expert consensus. EClinicalMedicine 2023; 61:102085. [PMID: 37528842 PMCID: PMC10388578 DOI: 10.1016/j.eclinm.2023.102085] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/20/2023] [Accepted: 06/20/2023] [Indexed: 08/03/2023] Open
Abstract
Clinical axillary lymph node management in early breast cancer has evolved from being merely an aspect of surgical management and now includes the entire multidisciplinary team. The second edition of the "Lucerne Toolbox", a multidisciplinary consortium of European cancer societies and patient representatives, addresses the challenges of clinical axillary lymph node management, from diagnosis to local therapy of the axilla. Five working packages were developed, following the patients' journey and addressing specific clinical scenarios. Panellists voted on 72 statements, reaching consensus (agreement of 75% or more) in 52.8%, majority (51%-74% agreement) in 43.1%, and no decision in 4.2%. Based on the votes, targeted imaging and standardized pathology of lymph nodes should be a prerequisite to planning local and systemic therapy, axillary lymph node dissection can be replaced by sentinel lymph node biopsy ( ± targeted approaches) in a majority of scenarios; and positive patient outcomes should be driven by both low recurrence risks and low rates of lymphoedema.
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Affiliation(s)
- Orit Kaidar-Person
- Breast Cancer Radiation Therapy Unit, Sheba Medical Center, Ramat Gan, Israel
- Department of Radiation Oncology (Maastro), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
| | - André Pfob
- Department of Obstetrics & Gynecology, Heidelberg University Hospital, Germany
- National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Bettina Borisch
- Department of Histopathology, University of Geneva, 1202 Geneva, Switzerland
| | - Ana Bosch
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Hematology, Oncology and Radiation Physics, Skåne University Hospital, Lasarettsgatan 23A, 22241, Lund, Sweden
| | - Maria João Cardoso
- Breast Unit, Champalimaud Foundation and University of Lisbon Faculty of Medicine, Lisbon, Portugal
| | - Giuseppe Curigliano
- Division of New Drugs and Early Drug Development, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141 Milano MI, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Jana De Boniface
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Surgery, Breast Centre, Capio St Göran's Hospital, Stockholm, Sweden
| | - Carsten Denkert
- Institute of Pathology, Philipps-University Marburg and University Hospital Marburg, Marburg, Germany
| | - Nik Hauser
- Breast Center, Hirslanden Clinic Aarau, Frauenarztzentrum Aargau AG, Baden, Switzerland
| | - Jörg Heil
- Department of Obstetrics & Gynecology, Heidelberg University Hospital, Germany
- Breast Center Heidelberg, Klinik St. Elisabeth, Heidelberg, Germany
| | - Michael Knauer
- Breast Center Eastern Switzerland, St. Gallen, Switzerland
| | - Thorsten Kühn
- Department of Gynecology and Obstetrics, University of Ulm, Germany
| | - Han-Byoel Lee
- Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, Republic of Korea
| | - Sibylle Loibl
- German Breast Group (GBG), C/o GBG Forschungs GmbH 63263 - Neu-Isenberg/, Germany
- Centre for Haematology and Oncology Bethanien, Frankfurt, Germany
| | | | - Icro Meattini
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, University of Florence, Florence, Italy
- Department of Experimental and Clinical Biomedical Sciences “M. Serio”, University of Florence, Florence, Italy
| | - Giacomo Montagna
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Isabel T. Rubio
- Breast Surgical Oncology, Clinica Universidad de Navarra, Madrid, Spain
| | - Patrizia Sager
- Breast Center Bern-Biel, Hirslanden Clinic Salem, Bern, Switzerland
| | - Petra Steyerova
- Breast Cancer Screening and Diagnostic Center, Clinic of Radiology, General University Hospital in Prague, Prague, Czech Republic
| | | | - Trine Tramm
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Marie-Jeanne Vrancken Peeters
- Department of Surgical Oncology Netherlands Cancer Institute, Antoni van Leeuwenhoek & Amsterdam University Medical Center, Netherlands
| | - Lynda Wyld
- Department of Oncology and Metabolism, The University of Sheffield, The Medical School, Sheffield, UK
| | - Jong Han Yu
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Walter Paul Weber
- Breast Center, University Hospital Basel, Basel, Switzerland
- University of Basel, Basel, Switzerland
| | - Philip Poortmans
- Department of Radiation Oncology, Iridium Netwerk, Antwerp 2610, Belgium
- University of Antwerp, Faculty of Medicine and Health Sciences, Antwerp, Belgium
| | - Peter Dubsky
- Breast Center, Hirslanden Clinic St Anna, 6006, Lucerne, Switzerland
- University of Lucerne, Faculty of Health Sciences and Medicine, Lucerne, Switzerland
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Marino MA, Avendano D, Pinker K. Response to "Comment on the value of multiparametric MRI in breast non-mass lesions". Eur J Radiol 2023; 163:110805. [PMID: 37086705 DOI: 10.1016/j.ejrad.2023.110805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 03/27/2023] [Indexed: 04/24/2023]
Affiliation(s)
- Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy.
| | - Daly Avendano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, New York, NY, USA, Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Nuevo Leon, Mexico
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Thakur SB, Chakraborthy J, Pinker K. Editorial for "TP53 Mutation Estimation Based on Radiomics Analysis for Breast Cancer". J Magn Reson Imaging 2023; 57:1104-1105. [PMID: 35762927 DOI: 10.1002/jmri.28324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 11/11/2022] Open
Affiliation(s)
- Sunitha B Thakur
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Jayasree Chakraborthy
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Romeo V, Helbich TH, Pinker K. Breast PET/MRI Hybrid Imaging and Targeted Tracers. J Magn Reson Imaging 2023; 57:370-386. [PMID: 36165348 PMCID: PMC10074861 DOI: 10.1002/jmri.28431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (18 F-FDG, 18 F-NaF, choline, 18 F-FES, 18 F-FES, 89 Zr-trastuzumab, choline derivatives, 18 F-FLT, and 68 Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria
| | - Katja Pinker
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Marino MA, Avendano D, Sevilimedu V, Thakur S, Martinez D, Lo Gullo R, Horvat JV, Helbich TH, Baltzer PAT, Pinker K. Limited value of multiparametric MRI with dynamic contrast-enhanced and diffusion-weighted imaging in non-mass enhancing breast tumors. Eur J Radiol 2022; 156:110523. [PMID: 36122521 PMCID: PMC10014485 DOI: 10.1016/j.ejrad.2022.110523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/14/2022] [Accepted: 09/09/2022] [Indexed: 11/23/2022]
Abstract
PURPOSE To investigate the diagnostic value of multiparametric MRI (mpMRI) including dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in non-mass enhancing breast tumors. METHOD Patients who underwent mpMRI, who were diagnosed with a suspicious non-mass enhancement (NME) on DCE-MRI (BI-RADS 4/5), and who subsequently underwent image-guided biopsy were retrospectively included. Two radiologists independently evaluated all NMEs, on both DCE-MR images and high-b-value DW images. Different mpMRI reading approaches were evaluated: 1) with a fixed apparent diffusion coefficient (ADC) threshold (<1.3 malignant, ≥1.3 benign) based on the recommendation by the European Society of Breast Imaging (EUSOBI); 2) with a fixed ADC threshold (<1.5 malignant, ≥1.5 benign) based on recently published trial data; 3) with an ADC threshold adapted to the assigned BI-RADS classification using a previously published reading method; and 4) with individually determined best thresholds for each reader. RESULTS The final study sample consisted of 66 lesions in 66 patients. DCE-MRI alone had the highest sensitivity for breast cancer detection (94.8-100 %), outperforming all mpMRI reading approaches (R1 74.4-87.1 %, R2 71.7-94.8 %) and DWI alone (R1 74.4 %, R2 79.4 %). The adapted approach achieved the best specificity for both readers (85.1 %), resulting in the best diagnostic accuracy for R1 (86.5 %) but a moderate diagnostic accuracy for R2 (77.2 %). CONCLUSION mpMRI has limited added diagnostic value to DCE-MRI in the assessment of NME.
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Affiliation(s)
- Maria Adele Marino
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Biomedical Sciences and Morphologic and Functional Imaging, University of Messina, Messina, Italy
| | - Daly Avendano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Tecnologico de Monterrey, School of Medicine and Health Sciences, Monterrey, Nuevo Leon, Mexico
| | - Varadan Sevilimedu
- Memorial Sloan Kettering Cancer Center, Department of Epidemiology and Biostatistics, New York, NY, USA
| | - Sunitha Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Danny Martinez
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Joao V Horvat
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY, USA.
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Eskreis-Winkler S, Sutton EJ, D’Alessio D, Gallagher K, Saphier N, Stember J, Martinez DF, Morris EA, Pinker K. Breast MRI Background Parenchymal Enhancement Categorization Using Deep Learning: Outperforming the Radiologist. J Magn Reson Imaging 2022; 56:1068-1076. [PMID: 35167152 PMCID: PMC9376189 DOI: 10.1002/jmri.28111] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations. PURPOSE To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations. STUDY TYPE Retrospective. POPULATION Consecutive high-risk patients (i.e. >20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal). FIELD STRENGTH/SEQUENCE A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging. ASSESSMENT Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards. STATISTICAL TESTS Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025). RESULTS The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs. DATA CONCLUSION Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY STAGE: 3.
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Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Elizabeth J. Sutton
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Donna D’Alessio
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Katherine Gallagher
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Nicole Saphier
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | - Joseph Stember
- Department of Radiology, Neuroradiology Service, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Danny F Martinez
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
| | | | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY 10065, USA
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Mann RM, Athanasiou A, Baltzer PAT, Camps-Herrero J, Clauser P, Fallenberg EM, Forrai G, Fuchsjäger MH, Helbich TH, Killburn-Toppin F, Lesaru M, Panizza P, Pediconi F, Pijnappel RM, Pinker K, Sardanelli F, Sella T, Thomassin-Naggara I, Zackrisson S, Gilbert FJ, Kuhl CK. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 2022; 32:4036-4045. [PMID: 35258677 PMCID: PMC9122856 DOI: 10.1007/s00330-022-08617-6] [Citation(s) in RCA: 115] [Impact Index Per Article: 57.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 01/21/2022] [Accepted: 01/27/2022] [Indexed: 02/07/2023]
Abstract
Breast density is an independent risk factor for the development of breast cancer and also decreases the sensitivity of mammography for screening. Consequently, women with extremely dense breasts face an increased risk of late diagnosis of breast cancer. These women are, therefore, underserved with current mammographic screening programs. The results of recent studies reporting on contrast-enhanced breast MRI as a screening method in women with extremely dense breasts provide compelling evidence that this approach can enable an important reduction in breast cancer mortality for these women and is cost-effective. Because there is now a valid option to improve breast cancer screening, the European Society of Breast Imaging (EUSOBI) recommends that women should be informed about their breast density. EUSOBI thus calls on all providers of mammography screening to share density information with the women being screened. In light of the available evidence, in women aged 50 to 70 years with extremely dense breasts, the EUSOBI now recommends offering screening breast MRI every 2 to 4 years. The EUSOBI acknowledges that it may currently not be possible to offer breast MRI immediately and everywhere and underscores that quality assurance procedures need to be established, but urges radiological societies and policymakers to act on this now. Since the wishes and values of individual women differ, in screening the principles of shared decision-making should be embraced. In particular, women should be counselled on the benefits and risks of mammography and MRI-based screening, so that they are capable of making an informed choice about their preferred screening method. KEY POINTS: • The recommendations in Figure 1 summarize the key points of the manuscript.
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Affiliation(s)
- Ritse M Mann
- Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, Netherlands.
- The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, Netherlands.
| | - Alexandra Athanasiou
- Breast Imaging Department, MITERA Hospital, 6, Erithrou Stavrou Str. 151 23 Marousi, Athens, Greece
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Julia Camps-Herrero
- Hospitales Ribera Salud, Avda.Cortes Valencianas, 58, 46015, Valencia, Spain
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Eva M Fallenberg
- Department of Diagnostic and Interventional Radiology, School of Medicine &; Klinikum Rechts der Isar, Technical University of Munich, Munich (TUM), Ismaninger Str. 22, 81675, München, Germany
| | - Gabor Forrai
- Department of Radiology, Duna Medical Center, Budapest, Hungary
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Auenbruggerplatz 9, 8036, Graz, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
| | - Fleur Killburn-Toppin
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Hills road, Cambridge, CB20QQ, UK
| | - Mihai Lesaru
- Radiology and Imaging Laboratory, Carol Davila University, Bucharest, Romania
| | - Pietro Panizza
- Breast Imaging Unit, IRCCS Ospedale San Raffaele,, Via Olgettina 60, 20132, Milan, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Viale Regina Elena, 324, 00161, Rome, Italy
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands
- Dutch Expert Centre for Screening (LRCB), Wijchenseweg 101, 6538 SW, Nijmegen, Netherlands
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Wien, Austria
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 E 66th Street, New York, NY, 10065, USA
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
| | - Tamar Sella
- Department of Diagnostic Imaging, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Isabelle Thomassin-Naggara
- Department of Radiology, Sorbonne Université, APHP, Hôpital Tenon, 4, rue de la Chine, 75020, Paris, France
| | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Faculty of Medicine, Lund University, Skåne University Hospital Malmö, SE-205 02, Malmö, Sweden
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge Biomedical Campus, Hills road, Cambridge, CB20QQ, UK
| | - Christiane K Kuhl
- University Hospital of Aachen, Rheinisch-Westfälische Technische Hochschule, Pauwelsstraße30, 52074, Aachen, Germany
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Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Thakur SB, Jochelson MS, Thakur N, Baltzer PAT, Helbich TH, Pinker K. Breast Lesion Classification with Multiparametric Breast MRI Using Radiomics and Machine Learning: A Comparison with Radiologists' Performance. Cancers (Basel) 2022; 14:cancers14071743. [PMID: 35406514 PMCID: PMC8997089 DOI: 10.3390/cancers14071743] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/21/2022] [Accepted: 03/25/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Currently, breast contrast-enhanced MRI is the most sensitive imaging technique for breast cancer detection; however, its specificity is low given the common characteristics shared by benign breast lesions and some cancers. This leads to a high number of false-positive cases and, therefore, unnecessary biopsies. Multiparametric MRI including diffusion-weighted imaging assists in this task by increasing the specificity for breast lesion discrimination. Nevertheless, interpretation of breast MRI is still highly dependent on the reader’s level of experience. Our work combines radiomic features extracted from multiparametric MRI to generate predictive models for breast cancer differentiation. Additionally, decision support models were compared with the performance of two breast dedicated radiologists for lesion differentiation. Our work proves the potential of multiparametric radiomics coupled with machine learning to be implemented in clinical practice for lesion differentiation on breast MRI. AI algorithms show value to assist less experienced readers, improving the accuracy for breast lesion discrimination. Abstract This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
- Correspondence: (I.D.N.); (P.G.)
| | - Peter Gibbs
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Correspondence: (I.D.N.); (P.G.)
| | - Jeffrey S. Reiner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Roberto Lo Gullo
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Sunitha B. Thakur
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
| | - Nikita Thakur
- Touro College of Osteopathic Medicine, Middletown, NY 10940, USA;
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, 1090 Wien, Austria; (P.A.T.B.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, 1090 Wien, Austria; (P.A.T.B.); (T.H.H.)
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, Breast Imaging Service, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (K.P.)
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Fardanesh R, Thakur SB, Sevilimedu V, Horvat JV, Gullo RL, Reiner JS, Eskreis-Winkler S, Thakur N, Pinker K. Differentiation Between Benign and Metastatic Breast Lymph Nodes Using Apparent Diffusion Coefficients. Front Oncol 2022; 12:795265. [PMID: 35280791 PMCID: PMC8905522 DOI: 10.3389/fonc.2022.795265] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/28/2022] [Indexed: 11/24/2022] Open
Abstract
The aim of this study was to determine the range of apparent diffusion coefficient (ADC) values for benign axillary lymph nodes in contrast to malignant axillary lymph nodes, and to define the optimal ADC thresholds for three different ADC parameters (minimum, maximum, and mean ADC) in differentiating between benign and malignant lymph nodes. This retrospective study included consecutive patients who underwent breast MRI from January 2017–December 2020. Two-year follow-up breast imaging or histopathology served as the reference standard for axillary lymph node status. Area under the receiver operating characteristic curve (AUC) values for minimum, maximum, and mean ADC (min ADC, max ADC, and mean ADC) for benign vs malignant axillary lymph nodes were determined using the Wilcoxon rank sum test, and optimal ADC thresholds were determined using Youden’s Index. The final study sample consisted of 217 patients (100% female, median age of 52 years (range, 22–81), 110 with benign axillary lymph nodes and 107 with malignant axillary lymph nodes. For benign axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.522–2.712 for mean ADC, 0.774–3.382 for max ADC, and 0.071–2.409 for min ADC; for malignant axillary lymph nodes, ADC values (×10−3 mm2/s) ranged from 0.796–1.080 for mean ADC, 1.168–1.592 for max ADC, and 0.351–0.688 for min ADC for malignant axillary lymph nodes. While there was a statistically difference in all ADC parameters (p<0.001) between benign and malignant axillary lymph nodes, boxplots illustrate overlaps in ADC values, with the least overlap occurring with mean ADC, suggesting that this is the most useful ADC parameter for differentiating between benign and malignant axillary lymph nodes. The mean ADC threshold that resulted in the highest diagnostic accuracy for differentiating between benign and malignant lymph nodes was 1.004×10−3 mm2/s, yielding an accuracy of 75%, sensitivity of 71%, specificity of 79%, positive predictive value of 77%, and negative predictive value of 74%. This mean ADC threshold is lower than the European Society of Breast Imaging (EUSOBI) mean ADC threshold of 1.300×10−3 mm2/s, therefore suggesting that the EUSOBI threshold which was recently recommended for breast tumors should not be extrapolated to evaluate the axillary lymph nodes.
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Affiliation(s)
- Reza Fardanesh
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sunitha B Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Joao V Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jeffrey S Reiner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Sarah Eskreis-Winkler
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Nikita Thakur
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States.,Touro College of Osteopathic Medicine, Middletown, NY, United States
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Bitencourt A, Iima M, Langs G, Pinker K. Editorial: Impact of Breast MRI on Breast Cancer Treatment and Prognosis. Front Oncol 2022; 12:825101. [PMID: 35359360 PMCID: PMC8963269 DOI: 10.3389/fonc.2022.825101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 02/14/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Almir Bitencourt
- Imaging Department, A.C.Camargo Cancer Center, São Paulo, Brazil
- Breast Imaging Section, DASA, São Paulo, Brazil
- *Correspondence: Almir Bitencourt,
| | - 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
| | - Georg Langs
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Hirsch L, Huang Y, Luo S, Rossi Saccarelli C, Lo Gullo R, Daimiel Naranjo I, Bitencourt AGV, Onishi N, Ko ES, Leithner D, Avendano D, Eskreis-Winkler S, Hughes M, Martinez DF, Pinker K, Juluru K, El-Rowmeim AE, Elnajjar P, Morris EA, Makse HA, Parra LC, Sutton EJ. Radiologist-Level Performance by Using Deep Learning for Segmentation of Breast Cancers on MRI Scans. Radiol Artif Intell 2022; 4:e200231. [PMID: 35146431 PMCID: PMC8823456 DOI: 10.1148/ryai.200231] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/13/2021] [Accepted: 11/04/2021] [Indexed: 11/17/2022]
Abstract
Purpose To develop a deep network architecture that would achieve fully automated
radiologist-level segmentation of cancers at breast MRI. Materials and Methods In this retrospective study, 38 229 examinations (composed of
64 063 individual breast scans from 14 475 patients) were
performed in female patients (age range, 12–94 years; mean age,
52 years ± 10 [standard deviation]) who presented between 2002
and 2014 at a single clinical site. A total of 2555 breast cancers were
selected that had been segmented on two-dimensional (2D) images by
radiologists, as well as 60 108 benign breasts that served as
examples of noncancerous tissue; all these were used for model training.
For testing, an additional 250 breast cancers were segmented
independently on 2D images by four radiologists. Authors selected among
several three-dimensional (3D) deep convolutional neural network
architectures, input modalities, and harmonization methods. The outcome
measure was the Dice score for 2D segmentation, which was compared
between the network and radiologists by using the Wilcoxon signed rank
test and the two one-sided test procedure. Results The highest-performing network on the training set was a 3D U-Net with
dynamic contrast-enhanced MRI as input and with intensity normalized for
each examination. In the test set, the median Dice score of this network
was 0.77 (interquartile range, 0.26). The performance of the network was
equivalent to that of the radiologists (two one-sided test procedures
with radiologist performance of 0.69–0.84 as equivalence bounds,
P < .001 for both; n =
250). Conclusion When trained on a sufficiently large dataset, the developed 3D U-Net
performed as well as fellowship-trained radiologists in detailed 2D
segmentation of breast cancers at routine clinical MRI. Keywords: MRI, Breast, Segmentation, Supervised Learning,
Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine
Learning Algorithms Published under a CC BY 4.0 license. Supplemental material is available for this
article.
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Affiliation(s)
- Lukas Hirsch
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Yu Huang
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Shaojun Luo
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Carolina Rossi Saccarelli
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Roberto Lo Gullo
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Isaac Daimiel Naranjo
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Almir G V Bitencourt
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Natsuko Onishi
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Eun Sook Ko
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Doris Leithner
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Daly Avendano
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Sarah Eskreis-Winkler
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Mary Hughes
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Danny F Martinez
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Katja Pinker
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Krishna Juluru
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Amin E El-Rowmeim
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Pierre Elnajjar
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Elizabeth A Morris
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Hernan A Makse
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Lucas C Parra
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
| | - Elizabeth J Sutton
- Department of Biomedical Engineering (L.H., Y.H., L.C.P.) and the Benjamin Levich Institute and Department of Physics (S.L., H.A.M.), the City College of the City University of New York, 160 Convent Ave, New York, NY 10031; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065 (Y.H., C.R.S., R.L.G., I.D.N., A.G.V.B., N.O., E.S.K., D.L., D.A., S.E.W., M.H., D.F.M., K.P., K.J., A.E.E., P.E., E.A.M., E.J.S.); Department of Imaging, A.C. Camargo Cancer Center, São Paulo, Brazil (A.G.V.B.); Department of Radiology, University of California, San Francisco, San Francisco, Calif (N.O.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea (E.S.K.); and Department of Breast Imaging, Breast Cancer Center TecSalud, ITESM Monterrey, Monterrey, Mexico (D.A.)
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Lo Gullo R, Wen H, Reiner JS, Hoda R, Sevilimedu V, Martinez DF, Thakur SB, Jochelson MS, Gibbs P, Pinker K. Assessing PD-L1 Expression Status Using Radiomic Features from Contrast-Enhanced Breast MRI in Breast Cancer Patients: Initial Results. Cancers (Basel) 2021; 13:cancers13246273. [PMID: 34944898 PMCID: PMC8699819 DOI: 10.3390/cancers13246273] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/03/2021] [Accepted: 12/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.
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Affiliation(s)
- Roberto Lo Gullo
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Hannah Wen
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Jeffrey S. Reiner
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Raza Hoda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (H.W.); (R.H.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10017, USA;
| | - Danny F. Martinez
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Sunitha B. Thakur
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
| | - Peter Gibbs
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Katja Pinker
- Breast Imaging Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (R.L.G.); (J.S.R.); (D.F.M.); (S.B.T.); (M.S.J.); (P.G.)
- Correspondence: ; Tel.: +1-646-888-5200
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Sardanelli F, Trimboli RM, Houssami N, Gilbert FJ, Helbich TH, Álvarez Benito M, Balleyguier C, Bazzocchi M, Bult P, Calabrese M, Camps Herrero J, Cartia F, Cassano E, Clauser P, Cozzi A, de Andrade DA, de Lima Docema MF, Depretto C, Dominelli V, Forrai G, Girometti R, Harms SE, Hilborne S, Ienzi R, Lobbes MBI, Losio C, Mann RM, Montemezzi S, Obdeijn IM, Ozcan UA, Pediconi F, Pinker K, Preibsch H, Raya Povedano JL, Sacchetto D, Scaperrotta GP, Schiaffino S, Schlooz M, Szabó BK, Taylor DB, Ulus ÖS, Van Goethem M, Veltman J, Weigel S, Wenkel E, Zuiani C, Di Leo G. Magnetic resonance imaging before breast cancer surgery: results of an observational multicenter international prospective analysis (MIPA). Eur Radiol 2021; 32:1611-1623. [PMID: 34643778 PMCID: PMC8831264 DOI: 10.1007/s00330-021-08240-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/20/2021] [Accepted: 08/02/2021] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Preoperative breast magnetic resonance imaging (MRI) can inform surgical planning but might cause overtreatment by increasing the mastectomy rate. The Multicenter International Prospective Analysis (MIPA) study investigated this controversial issue. METHODS This observational study enrolled women aged 18-80 years with biopsy-proven breast cancer, who underwent MRI in addition to conventional imaging (mammography and/or breast ultrasonography) or conventional imaging alone before surgery as routine practice at 27 centers. Exclusion criteria included planned neoadjuvant therapy, pregnancy, personal history of any cancer, and distant metastases. RESULTS Of 5896 analyzed patients, 2763 (46.9%) had conventional imaging only (noMRI group), and 3133 (53.1%) underwent MRI that was performed for diagnosis, screening, or unknown purposes in 692/3133 women (22.1%), with preoperative intent in 2441/3133 women (77.9%, MRI group). Patients in the MRI group were younger, had denser breasts, more cancers ≥ 20 mm, and a higher rate of invasive lobular histology than patients who underwent conventional imaging alone (p < 0.001 for all comparisons). Mastectomy was planned based on conventional imaging in 22.4% (MRI group) versus 14.4% (noMRI group) (p < 0.001). The additional planned mastectomy rate in the MRI group was 11.3%. The overall performed first- plus second-line mastectomy rate was 36.3% (MRI group) versus 18.0% (noMRI group) (p < 0.001). In women receiving conserving surgery, MRI group had a significantly lower reoperation rate (8.5% versus 11.7%, p < 0.001). CONCLUSIONS Clinicians requested breast MRI for women with a higher a priori probability of receiving mastectomy. MRI was associated with 11.3% more mastectomies, and with 3.2% fewer reoperations in the breast conservation subgroup. KEY POINTS • In 19% of patients of the MIPA study, breast MRI was performed for screening or diagnostic purposes. • The current patient selection to preoperative breast MRI implies an 11% increase in mastectomies, counterbalanced by a 3% reduction of the reoperation rate. • Data from the MIPA study can support discussion in tumor boards when preoperative MRI is under consideration and should be shared with patients to achieve informed decision-making.
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Affiliation(s)
- Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy. .,Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy.
| | - Rubina M Trimboli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Nehmat Houssami
- Sydney School of Public Health, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Fiona J Gilbert
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | | | | | - Massimo Bazzocchi
- Institute of Radiology, Department of Medicine, Università degli Studi di Udine, Udine, Italy
| | - Peter Bult
- Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Massimo Calabrese
- Unit of Breast Radiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | | | - Francesco Cartia
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | | | | | - Catherine Depretto
- Unit of Breast Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Valeria Dominelli
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Gábor Forrai
- Department of Radiology, MHEK Teaching Hospital, Semmelweis University, Budapest, Hungary
| | - Rossano Girometti
- Institute of Radiology, Department of Medicine, Università degli Studi di Udine, Udine, Italy
| | - Steven E Harms
- Breast Center of Northwest Arkansas, Fayetteville, AR, USA
| | - Sarah Hilborne
- Department of Radiology, School of Clinical Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Raffaele Ienzi
- Department of Radiology, Di.Bi.MED, Università degli Studi di Palermo, Policlinico Universitario Paolo Giaccone, Palermo, Italy
| | - Marc B I Lobbes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudio Losio
- Department of Breast Radiology, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Ritse M Mann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefania Montemezzi
- Department of Radiology, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy
| | - Inge-Marie Obdeijn
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Umit A Ozcan
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi di Roma "La Sapienza", Rome, Italy
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Heike Preibsch
- Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen, Tübingen, Germany
| | | | - Daniela Sacchetto
- Kiwifarm S.R.L, La Morra, Italy.,Disaster Medicine Service 118, ASL CN1, Saluzzo, Italy.,CRIMEDIM, Research Center in Emergency and Disaster Medicine, Università degli Studi del Piemonte Orientale "Amedeo Avogadro", Novara, Italy
| | | | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
| | - Margrethe Schlooz
- Department of Surgery, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Botond K Szabó
- Department of Radiology, Barking Havering and Redbridge University Hospitals NHS Trust, London, UK
| | - Donna B Taylor
- Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, Australia.,Department of Radiology, Royal Perth Hospital, Perth, Australia
| | - Özden S Ulus
- Unit of Radiology, Acıbadem Mehmet Ali Aydınlar University School of Medicine, İstanbul, Turkey
| | - Mireille Van Goethem
- Gynecological Oncology Unit, Department of Obstetrics and Gynecology, Department of Radiology, Multidisciplinary Breast Clinic, Antwerp University Hospital, University of Antwerp, Antwerpen, Belgium
| | - Jeroen Veltman
- Maatschap Radiologie Oost-Nederland, Oldenzaal, The Netherlands
| | - Stefanie Weigel
- Institute of Clinical Radiology and Reference Center for Mammography, University of Münster, Münster, Germany
| | - Evelyn Wenkel
- Department of Radiology, University Hospital of Erlangen, Erlangen, Germany
| | - Chiara Zuiani
- Institute of Radiology, Department of Medicine, Università degli Studi di Udine, Udine, Italy
| | - Giovanni Di Leo
- Unit of Radiology, IRCCS Policlinico San Donato, Via Rodolfo Morandi 30, 20097, San Donato Milanese, Italy
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Meyer‐Base A, Morra L, Tahmassebi A, Lobbes M, Meyer‐Base U, Pinker K. AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer. J Magn Reson Imaging 2021; 54:686-702. [PMID: 32864782 PMCID: PMC8451829 DOI: 10.1002/jmri.27332] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 07/30/2020] [Accepted: 07/31/2020] [Indexed: 12/11/2022] Open
Abstract
Computer-aided diagnosis (CAD) systems have become an important tool in the assessment of breast tumors with magnetic resonance imaging (MRI). CAD systems can be used for the detection and diagnosis of breast tumors as a "second opinion" review complementing the radiologist's review. CAD systems have many common parts, such as image preprocessing, tumor feature extraction, and data classification that are mostly based on machine-learning (ML) techniques. In this review article, we describe applications of ML-based CAD systems in MRI covering the detection of diagnostically challenging lesions of the breast such as nonmass enhancing (NME) lesions, and furthermore discuss how multiparametric MRI and radiomics can be applied to the study of NME, including prediction of response to neoadjuvant chemotherapy (NAC). Since ML has been widely used in the medical imaging community, we provide an overview about the state-of-the-art and novel techniques applied as classifiers to CAD systems. The differences in the CAD systems in MRI of the breast for several standard and novel applications for NME are explained in detail to provide important examples, illustrating: 1) CAD for detection and diagnosis, 2) CAD in multiparametric imaging, 3) CAD in NAC, and 4) breast cancer radiomics. We aim to provide a comparison between these CAD applications and to illustrate a global view on intelligent CAD systems based on machine and deep learning in MRI of the breast. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Anke Meyer‐Base
- Department of Scientific ComputingFlorida State UniversityTallahasseeFloridaUSA
- Department of Radiology, Maastricht Medical CenterUniversity of MaastrichtMaastrichtNetherlands
| | - Lia Morra
- Department of Control and Computer EngineeringPolitecnico di TorinoTorinoItaly
| | | | - Marc Lobbes
- Department of Radiology, Maastricht Medical CenterUniversity of MaastrichtMaastrichtNetherlands
- GROW School for Oncology and Developmental BiologyMaastrichtNetherlands
- Zuyderland Medical Center, dep of Medical ImagingSittard‐GeleenNetherlands
| | - Uwe Meyer‐Base
- Department of Electrical and Computer EngineeringFlorida A&M University and Florida State UniversityTallahasseeFloridaUSA
| | - Katja Pinker
- Department of Radiology, Breast Imaging ServiceMemorial Sloan‐Kettering Cancer CenterNew YorkNew YorkUSA
- Department of Biomedical Imaging and Image‐Guided Therapy, Division of Molecular and Gender ImagingMedical University of ViennaViennaAustria
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Bitencourt A, Daimiel Naranjo I, Lo Gullo R, Rossi Saccarelli C, Pinker K. AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021; 142:109882. [PMID: 34392105 PMCID: PMC8387447 DOI: 10.1016/j.ejrad.2021.109882] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 12/22/2022]
Abstract
Significant advances in imaging analysis and the development of high-throughput methods that can extract and correlate multiple imaging parameters with different clinical outcomes have led to a new direction in medical research. Radiomics and artificial intelligence (AI) studies are rapidly evolving and have many potential applications in breast imaging, such as breast cancer risk prediction, lesion detection and classification, radiogenomics, and prediction of treatment response and clinical outcomes. AI has been applied to different breast imaging modalities, including mammography, ultrasound, and magnetic resonance imaging, in different clinical scenarios. The application of AI tools in breast imaging has an unprecedented opportunity to better derive clinical value from imaging data and reshape the way we care for our patients. The aim of this study is to review the current knowledge and future applications of AI-enhanced breast imaging in clinical practice.
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Affiliation(s)
- Almir Bitencourt
- Department of Imaging, A.C.Camargo Cancer Center, Sao Paulo, SP, Brazil; Dasa, Sao Paulo, SP, Brazil
| | - Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Guy's and St. Thomas' NHS Trust, Great Maze Pond, London, UK
| | - Roberto Lo Gullo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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Schiaffino S, Pinker K, Magni V, Cozzi A, Athanasiou A, Baltzer PAT, Camps Herrero J, Clauser P, Fallenberg EM, Forrai G, Fuchsjäger MH, Helbich TH, Kilburn-Toppin F, Kuhl CK, Lesaru M, Mann RM, Panizza P, Pediconi F, Pijnappel RM, Sella T, Thomassin-Naggara I, Zackrisson S, Gilbert FJ, Sardanelli F. Axillary lymphadenopathy at the time of COVID-19 vaccination: ten recommendations from the European Society of Breast Imaging (EUSOBI). Insights Imaging 2021; 12:119. [PMID: 34417642 PMCID: PMC8378785 DOI: 10.1186/s13244-021-01062-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/17/2021] [Indexed: 01/12/2023] Open
Abstract
Unilateral axillary lymphadenopathy is a frequent mild side effect of COVID-19 vaccination. European Society of Breast Imaging (EUSOBI) proposes ten recommendations to standardise its management and reduce unnecessary additional imaging and invasive procedures: (1) in patients with previous history of breast cancer, vaccination should be performed in the contralateral arm or in the thigh; (2) collect vaccination data for all patients referred to breast imaging services, including patients undergoing breast cancer staging and follow-up imaging examinations; (3) perform breast imaging examinations preferentially before vaccination or at least 12 weeks after the last vaccine dose; (4) in patients with newly diagnosed breast cancer, apply standard imaging protocols regardless of vaccination status; (5) in any case of symptomatic or imaging-detected axillary lymphadenopathy before vaccination or at least 12 weeks after, examine with appropriate imaging the contralateral axilla and both breasts to exclude malignancy; (6) in case of axillary lymphadenopathy contralateral to the vaccination side, perform standard work-up; (7) in patients without breast cancer history and no suspicious breast imaging findings, lymphadenopathy only ipsilateral to the vaccination side within 12 weeks after vaccination can be considered benign or probably-benign, depending on clinical context; (8) in patients without breast cancer history, post-vaccination lymphadenopathy coupled with suspicious breast finding requires standard work-up, including biopsy when appropriate; (9) in patients with breast cancer history, interpret and manage post-vaccination lymphadenopathy considering the timeframe from vaccination and overall nodal metastatic risk; (10) complex or unclear cases should be managed by the multidisciplinary team.
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Affiliation(s)
- Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy
| | - Katja Pinker
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Wien, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Veronica Magni
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
| | | | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Wien, Austria
| | | | - Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Wien, Austria
| | - Eva M Fallenberg
- Department of Diagnostic and Interventional Radiology, School of Medicine & Klinikum Rechts der Isar, Technical University of Munich (TUM) , München , Germany
| | - Gábor Forrai
- Department of Radiology, Duna Medical Center, Budapest, Hungary
| | - Michael H Fuchsjäger
- Division of General Radiology, Department of Radiology, Medical University Graz, Graz, Austria
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of General and Pediatric Radiology, Research Group: Molecular and Gender Imaging, Medical University of Vienna, Wien, Austria
| | | | - Christiane K Kuhl
- University Hospital of Aachen, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Mihai Lesaru
- Radiology and Imaging Laboratory, Fundeni Institute, Bucharest, Romania
| | - Ritse M Mann
- Department of Medical Imaging, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.,Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Pietro Panizza
- Breast Imaging Unit, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological, and Pathological Sciences , Università degli Studi di Roma "La Sapienza" , Rome, Italy
| | - Ruud M Pijnappel
- Department of Imaging, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Tamar Sella
- Department of Diagnostic Imaging, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | | | - Sophia Zackrisson
- Diagnostic Radiology, Department of Translational Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy. .,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy.
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Daimiel Naranjo I, Gibbs P, Reiner JS, Lo Gullo R, Sooknanan C, Thakur SB, Jochelson MS, Sevilimedu V, Morris EA, Baltzer PAT, Helbich TH, Pinker K. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics (Basel) 2021; 11:diagnostics11060919. [PMID: 34063774 PMCID: PMC8223779 DOI: 10.3390/diagnostics11060919] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/11/2021] [Accepted: 05/18/2021] [Indexed: 12/12/2022] Open
Abstract
The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018-March 2020; Medical University Vienna, from January 2011-August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7-99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70-0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75-0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77-0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0-88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.
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Affiliation(s)
- Isaac Daimiel Naranjo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Radiology, Breast Imaging Service, Guy’s and St. Thomas’ NHS Trust, Great Maze Pond, London SE1 9RT, UK
- Correspondence: (I.D.N.); (P.G.)
| | - Peter Gibbs
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Correspondence: (I.D.N.); (P.G.)
| | - Jeffrey S. Reiner
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Roberto Lo Gullo
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Caleb Sooknanan
- Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute, New York, NY 10065, USA;
| | - Sunitha B. Thakur
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Maxine S. Jochelson
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA;
| | - Elizabeth A. Morris
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
| | - Pascal A. T. Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Thomas H. Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (J.S.R.); (R.L.G.); (S.B.T.); (M.S.J.); (E.A.M.); (K.P.)
- Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Structural Preclinical Imaging, Medical University of Vienna, Wien 1090, Austria; (P.A.T.B.); (T.H.H.)
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Clauser P, Krug B, Bickel H, Dietzel M, Pinker K, Neuhaus VF, Marino MA, Moschetta M, Troiano N, Helbich TH, Baltzer PAT. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy. Clin Cancer Res 2021; 27:1941-1948. [PMID: 33446565 PMCID: PMC8406278 DOI: 10.1158/1078-0432.ccr-20-3037] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/13/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE Diffusion-weighted imaging with the calculation of an apparent diffusion coefficient (ADC) has been proposed as a quantitative biomarker on contrast-enhanced MRI (CE-MRI) of the breast. There is a need to approve a generalizable ADC cutoff. The purpose of this study was to evaluate whether a predefined ADC cutoff allows downgrading of BI-RADS 4 lesions on CE-MRI, avoiding unnecessary biopsies. EXPERIMENTAL DESIGN This was a retrospective, multicentric, cross-sectional study. Data from five centers were pooled on the individual lesion level. Eligible patients had a BI-RADS 4 rating on CE-MRI. For each center, two breast radiologists evaluated the images. Data on lesion morphology (mass, non-mass), size, and ADC were collected. Histology was the standard of reference. A previously suggested ADC cutoff (≥1.5 × 10-3 mm2/second) was applied. A negative likelihood ratio of 0.1 or lower was considered as a rule-out criterion for breast cancer. Diagnostic performance indices were calculated by ROC analysis. RESULTS There were 657 female patients (mean age, 42; SD, 14.1) with 696 BI-RADS 4 lesions included. Disease prevalence was 59.5% (414/696). The area under the ROC curve was 0.784. Applying the investigated ADC cutoff, sensitivity was 96.6% (400/414). The potential reduction of unnecessary biopsies was 32.6% (92/282). CONCLUSIONS An ADC cutoff of ≥1.5 × 10-3 mm2/second allows downgrading of lesions classified as BI-RADS 4 on breast CE-MRI. One-third of unnecessary biopsies could thus be avoided.
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Affiliation(s)
- Paola Clauser
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Krug
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Hubert Bickel
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Matthias Dietzel
- Department of Radiology, Friedrich-Alexander-University Hospital Erlangen-Nürnberg, Erlangen, Germany
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Victor-Frederic Neuhaus
- Department of Diagnostical and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Maria Adele Marino
- Department of Biomedical Sciences and Morphologic and Functional Imaging, Policlinico Universitario G. Martino, University of Messina, Messina, Italy
| | - Marco Moschetta
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Nicoletta Troiano
- DETO Breast Care Unit, University of Bari Medical School, Bari, Italy
| | - Thomas H Helbich
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Pascal A T Baltzer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
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48
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Eskreis-Winkler S, Simon K, Reichman M, Spincemaille P, Nguyen TD, Christos PJ, Drotman M, Prince MR, Pinker K, Sutton EJ, Morris EA, Wang Y. Multispectral Imaging for Metallic Biopsy Marker Detection During MRI-Guided Breast Biopsy: A Feasibility Study for Clinical Translation. Front Oncol 2021; 11:605014. [PMID: 33828972 PMCID: PMC8020905 DOI: 10.3389/fonc.2021.605014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 02/04/2021] [Indexed: 11/18/2022] Open
Abstract
Purpose To assess the feasibility and diagnostic accuracy of multispectral MRI (MSI) in the detection and localization of biopsy markers during MRI-guided breast biopsy. Methods This prospective study included 20 patients undergoing MR-guided breast biopsy. In 10 patients (Group 1), MSI was acquired following tissue sampling and biopsy marker deployment. In the other 10 patients (Group 2), MSI was acquired following tissue sampling but before biopsy marker deployment (to simulate deployment failure). All patients received post-procedure mammograms. Group 1 and Group 2 designations, in combination with the post-procedure mammogram, served as the reference standard. The diagnostic performance of MSI for biopsy marker identification was independently evaluated by two readers using two-spectral-bin MR and one-spectral-bin MR. The κ statistic was used to assess inter-rater agreement for biopsy marker identification. Results The sensitivity, specificity, and accuracy of biopsy marker detection for readers 1 and 2 using 2-bin MSI were 90.0% (9/10) and 90.0% (9/10), 100.0% (10/10) and 100.0% (10/10), 95.0% (19/20) and 95.0% (19/20); and using 1-bin MSI were 70.0% (7/10) and 80.0% (8/10), 100.0% (8/8) and 100.0% (10/10), 85.0% (17/20) and 90.0% (18/20). Positive predictive value was 100% for both readers for all numbers of bins. Inter-rater agreement was excellent: κ was 1.0 for 2-bin MSI and 0.81 for 1-bin MSI. Conclusion MSI is a feasible, diagnostically accurate technique for identifying metallic biopsy markers during MRI-guided breast biopsy and may eliminate the need for a post-procedure mammogram.
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Affiliation(s)
- Sarah Eskreis-Winkler
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Katherine Simon
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Melissa Reichman
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Pascal Spincemaille
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Thanh D Nguyen
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Paul J Christos
- Division of Biostatistics and Epidemiology, Department of Healthcare Policy & Research, Weill Cornell Medicine, New York, NY, United States
| | - Michele Drotman
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Martin R Prince
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth J Sutton
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Elizabeth A Morris
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Yi Wang
- Department of Radiology, Weill Cornell Medicine, New York, NY, United States
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49
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Eskreis-Winkler S, Onishi N, Pinker K, Reiner JS, Kaplan J, Morris EA, Sutton EJ. Using Deep Learning to Improve Nonsystematic Viewing of Breast Cancer on MRI. J Breast Imaging 2021; 3:201-207. [PMID: 38424820 DOI: 10.1093/jbi/wbaa102] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Indexed: 03/02/2024]
Abstract
OBJECTIVE To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images. METHODS This IRB-approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into "cancer" and "no cancer" categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics. RESULTS Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%-93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm. CONCLUSION In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.
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Affiliation(s)
| | - Natsuko Onishi
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
- University of California, Department of Radiology, San Francisco, CA
| | - Katja Pinker
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
| | - Jeffrey S Reiner
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
| | - Jennifer Kaplan
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
| | - Elizabeth A Morris
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
| | - Elizabeth J Sutton
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, NY
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50
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Krajnc D, Papp L, Nakuz TS, Magometschnigg HF, Grahovac M, Spielvogel CP, Ecsedi B, Bago-Horvath Z, Haug A, Karanikas G, Beyer T, Hacker M, Helbich TH, Pinker K. Breast Tumor Characterization Using [ 18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers (Basel) 2021; 13:cancers13061249. [PMID: 33809057 PMCID: PMC8000810 DOI: 10.3390/cancers13061249] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/06/2021] [Accepted: 03/09/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Breast cancer is the second most common diagnosed malignancy in women worldwide. In this study, we examine the feasibility of breast tumor characterization based on [18F]FDG-PET/CT images using machine learning (ML) approaches in combination with data-preprocessing techniques. ML prediction models for breast cancer detection and the identification of breast cancer receptor status, proliferation rate, and molecular subtypes were established and evaluated. Furthermore, the importance of most repeatable features was investigated. Results displayed high performance of malignant/benign tumor differentiation and triple negative tumor subtype ML models. We observed high repeatability of radiomic features for both high performing predictive models. Abstract Background: This study investigated the performance of ensemble learning holomic models for the detection of breast cancer, receptor status, proliferation rate, and molecular subtypes from [18F]FDG-PET/CT images with and without incorporating data pre-processing algorithms. Additionally, machine learning (ML) models were compared with conventional data analysis using standard uptake value lesion classification. Methods: A cohort of 170 patients with 173 breast cancer tumors (132 malignant, 38 benign) was examined with [18F]FDG-PET/CT. Breast tumors were segmented and radiomic features were extracted following the imaging biomarker standardization initiative (IBSI) guidelines combined with optimized feature extraction. Ensemble learning including five supervised ML algorithms was utilized in a 100-fold Monte Carlo (MC) cross-validation scheme. Data pre-processing methods were incorporated prior to machine learning, including outlier and borderline noisy sample detection, feature selection, and class imbalance correction. Feature importance in each model was assessed by calculating feature occurrence by the R-squared method across MC folds. Results: Cross validation demonstrated high performance of the cancer detection model (80% sensitivity, 78% specificity, 80% accuracy, 0.81 area under the curve (AUC)), and of the triple negative tumor identification model (85% sensitivity, 78% specificity, 82% accuracy, 0.82 AUC). The individual receptor status and luminal A/B subtype models yielded low performance (0.46–0.68 AUC). SUVmax model yielded 0.76 AUC in cancer detection and 0.70 AUC in predicting triple negative subtype. Conclusions: Predictive models based on [18F]FDG-PET/CT images in combination with advanced data pre-processing steps aid in breast cancer diagnosis and in ML-based prediction of the aggressive triple negative breast cancer subtype.
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Affiliation(s)
- Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | - Thomas S. Nakuz
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Heinrich F. Magometschnigg
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Marko Grahovac
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Clemens P. Spielvogel
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
| | | | - Alexander Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
- Christian Doppler Laboratory for Applied Metabolomics, Medical University of Vienna, 1090 Vienna, Austria
| | - Georgios Karanikas
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria; (D.K.); (L.P.); (B.E.)
- Correspondence:
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (T.S.N.); (M.G.); (C.P.S.); (A.H.); (G.K.); (M.H.)
| | - Thomas H. Helbich
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
| | - Katja Pinker
- Division of Molecular and Gender Imaging, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria; (H.F.M.); (T.H.H.); or (K.P.)
- Memorial Sloan Kettering Cancer Center, Breast Imaging Service, Department of Radiology, New York, NY 10065, USA
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