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

Late distant recurrence (DR) remains a persistent risk in hormone receptor-positive (HR+) early breast cancer after completion of 5 years of endocrine therapy. We developed and validated a multimodal artificial intelligence (AI) model to improve long-term risk stratification and to explore heterogeneity in benefit from extended letrozole therapy (ELT). The deep learning model integrating digitized hematoxylin and eosin whole-slide images with clinicopathologic variables was developed using 2,271 patients from the NSABP B-42 trial with 5-fold cross-validation and externally validated in 4,300 patients from the TAILORx trial who were disease-free at 5 years from initial diagnosis. Prognostic performance was evaluated using hazard ratios (HRs) and absolute risk differences. Exploratory analyses assessed extended letrozole therapy (ELT) benefit across model-defined risk groups. In NSABP B-42, the model stratified patients into groups with markedly different outcomes, with a 10-year absolute DR risk difference of 7.95% between high- and low-risk groups (HR, 5.71; 95% CI, 3.5-9.317; P<0.001). High-risk patients derived greater absolute benefit from ELT (4.09%) than low-risk patients (0.49%). External validation in the independent TAILORx cohort confirmed prognostic performance, with MI Clarity M3T identifying patients with significantly different late DR outcomes (HR, 1.893; 95% CI, 1.413-2.534; P<0.001). This multimodal AI approach using routine pathology and clinical data enables robust and generalizable stratification of late distant recurrence risk in HR+ breast cancer. This scalable strategy may complement existing genomic assays and support more individualized decisions regarding extended endocrine therapy.

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Journal
Cancer Research Communications
Published
2026-07-17
DOI
https://doi.org/10.1158/2767-9764.crc-26-0362
Primary Topic
Breast Cancer Treatment Studies
Type
article
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article

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

Priya Rastogi, Yating Cheng, Eghbal Amidi, Jennifer R. Ribeiro et al.
Cancer Research Communications
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Development and Validation of a Multimodal-Multitask Deep Learning Approach for Estimating Late Distant Recurrence Risk in HR–Positive Early Breast Cancer

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

Abstract

Late distant recurrence (DR) remains a persistent risk in hormone receptor-positive (HR+) early breast cancer after completion of 5 years of endocrine therapy. We developed and validated a multimodal artificial intelligence (AI) model to improve long-term risk stratification and to explore heterogeneity in benefit from extended letrozole therapy (ELT). The deep learning model integrating digitized hematoxylin and eosin whole-slide images with clinicopathologic variables was developed using 2,271 patients from the NSABP B-42 trial with 5-fold cross-validation and externally validated in 4,300 patients from the TAILORx trial who were disease-free at 5 years from initial diagnosis. Prognostic performance was evaluated using hazard ratios (HRs) and absolute risk differences. Exploratory analyses assessed extended letrozole therapy (ELT) benefit across model-defined risk groups. In NSABP B-42, the model stratified patients into groups with markedly different outcomes, with a 10-year absolute DR risk difference of 7.95% between high- and low-risk groups (HR, 5.71; 95% CI, 3.5-9.317; P<0.001). High-risk patients derived greater absolute benefit from ELT (4.09%) than low-risk patients (0.49%). External validation in the independent TAILORx cohort confirmed prognostic performance, with MI Clarity M3T identifying patients with significantly different late DR outcomes (HR, 1.893; 95% CI, 1.413-2.534; P<0.001). This multimodal AI approach using routine pathology and clinical data enables robust and generalizable stratification of late distant recurrence risk in HR+ breast cancer. This scalable strategy may complement existing genomic assays and support more individualized decisions regarding extended endocrine therapy.

Cancer Research Communications
Orlando Health (US), University of Pittsburgh (US), Dana-Farber Cancer Institute (US), Caris Life Sciences (United States) (US), Flagship Pioneering (United States) (US), NRG Oncology (US), NSABP Foundation (US), Icahn School of Medicine at Mount Sinai (US)
Good health and well-being
Openalex Percentile: Top 11%
Breast Cancer Treatment Studies
DOIOpen Access PDFOpenAlex
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