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2026-07-31· CLARITY

Figure 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer

Eleftherios P. Mamounas, Ming Chen, Joseph A. Sparano, Md Ashequr Rahman, Yating Cheng, Victoria Wang, Robert J. Gray, Priya Rastogi, Eghbal Amidi, Charles E. Geyer, Tommy Boucher, Tanner J. Freeman, Mohammadreza Ramzanpour, Mukund Varma, Hassan Ghani, Caleb Cheng, Casey Bales, Jennifer R. Ribeiro, Hanna Bandos, Nicolas Stransky, Mark R. Miglarese, Matthew J. Oberley, David Spetzler, Milan Radovich, George W. Sledge, Norman Wolmark

原始摘要(英文原文)· Original abstract
External validation of MI Clarity M3T for late DR in the TAILORx translational cohort. A, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in all patients. B, Multivariable Cox analysis assessing the independent prognostic value of the MI Clarity M3T risk label, adjusted for clinical covariates. C, Kaplan–Meier analysis of late DR comparing MI Clarity M3T risk groups in arms B and C (ITT). LDR, late distant recurrence.
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Figure 6 from Development and Validation of a Multimodal–Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR-Positive Early Breast Cancer — 科研速览 Science Skim