Breast Cancer Recurrence Risk Prediction Based on Multiple Instance Learning

Jinqiu Chen; Huyan Xu

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arXiv · 2512.18734

Breast Cancer Recurrence Risk Prediction Based on Multiple Instance Learning

Jinqiu Chen·Huyan Xu

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Abstract

Predicting breast cancer recurrence risk is a critical clinical challenge. This study investigates the potential of computational pathology to stratify patients using deep learning on routine Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). We developed and compared three Multiple Instance Learning (MIL) frameworks -- CLAM-SB, ABMIL, and ConvNeXt-MIL-XGBoost -- on an in-house dataset of 210 patient cases. The models were trained to predict 5-year recurrence risk, categorized into three tiers (low, medium, high), with ground truth labels established by the 21-gene Recurrence Score. Features were extracted using the UNI and CONCH pre-trained models. In a 5-fold cross-validation, the modified CLAM-SB model demonstrated the strongest performance, achieving a mean Area Under the Curve (AUC) of 0.836 and a classification accuracy of 76.2%. Our findings demonstrate the feasibility of using deep learning on standard histology slides for automated, genomics-correlated risk stratification, highlighting a promising pathway toward rapid and cost-effective clinical decision support.

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BibTeX RIS

Jinqiu Chen, Huyan Xu. 2025-12-21. Breast Cancer Recurrence Risk Prediction Based on Multiple Instance Learning. https://arxiv.org/abs/2512.18734

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