Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool
This provides a cost-effective alternative to genomic tools like Oncotype DX for breast cancer patients, potentially broadening access to personalized treatment, though it is incremental as it builds on existing computational pathology methods.
The study tackled the problem of predicting breast cancer recurrence risk by developing Deep-BCR-Auto, a deep learning tool that uses routine H&E-stained whole slide images, achieving AUROCs of 0.827 and 0.832 on independent datasets with up to 82.0% accuracy.
Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX (ODX) offer valuable genomic insights for HR+/HER2- patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-BCR-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine H&E-stained whole slide images (WSIs). Our methodology was validated on two independent cohorts: the TCGA-BRCA dataset and an in-house dataset from The Ohio State University (OSU). Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On the TCGA-BRCA dataset, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.827, significantly outperforming existing weakly supervised models (p=0.041). In the independent OSU dataset, Deep-BCR-Auto maintained strong generalizability, achieving an AUROC of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.