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Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge
Wen Li1, Savannah C Partridge1, David C Newitt1
1From the Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, Calif (W.L., D.C.N., N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.); Center for Statistical Sciences, Brown University, Providence, RI (J.S., H.S.M.); Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging, Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.); Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.); University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md (V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y., K.S.); Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Tex (J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology, University of Michigan, Ann Arbor, Mich (T.L.C.).
Abstract:
Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.
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