Assessing the risk of recurrence in early-stage breast cancer through H&E stained whole slide images

TL;DR

This study demonstrates that deep learning models analyzing H&E stained whole slide images can predict early-stage breast cancer recurrence risk with promising accuracy, potentially serving as a cost-effective alternative to genomic assays.

Contribution

The paper introduces a deep learning approach that predicts breast cancer recurrence risk from pathology images, showing comparable results to genomic tests.

Findings

Sensitivity up to 0.857 for low-risk category

Specificity up to 0.972 for high-risk category

Correlation of 0.61 with histological grade

Abstract

Accurate prediction of the likelihood of recurrence is important in the selection of postoperative treatment for patients with early-stage breast cancer. In this study, we investigated whether deep learning algorithms can predict patients' risk of recurrence by analyzing the pathology images of their cancer histology.We analyzed 125 hematoxylin and eosin-stained whole slide images (WSIs) from 125 patients across two institutions (National Cancer Center and Korea University Medical Center Guro Hospital) to predict breast cancer recurrence risk using deep learning. Sensitivity reached 0.857, 0.746, and 0.529 for low, intermediate, and high-risk categories, respectively, with specificity of 0.816, 0.803, and 0.972, and a Pearson correlation of 0.61 with histological grade. Class activation maps highlighted features like tubule formation and mitotic rate, suggesting a cost-effective approach to risk stratification, pending broader validation. These findings suggest that deep learning models trained exclusively on hematoxylin and eosin stained whole slide images can approximate genomic assay results, offering a cost-effective and scalable tool for breast cancer recurrence risk assessment. However, further validation using larger and more balanced datasets is needed to confirm the clinical applicability of our approach.