HER2 and FISH Status Prediction in Breast Biopsy H&E-Stained Images Using Deep Learning

TL;DR

This study develops a deep learning pipeline to predict HER2 status directly from H&E-stained breast biopsy images, potentially reducing reliance on costly FISH tests and expediting treatment decisions.

Contribution

It introduces a novel weak supervision and contrastive learning approach for HER2 prediction from H&E images, trained on large public datasets with promising accuracy.

Findings

Achieved AUC of 0.85 on TCGA-H&E dataset

Achieved AUC of 0.81 on equivocal HER2 cases

Demonstrated potential to reduce FISH testing requirements

Abstract

The current standard for detecting human epidermal growth factor receptor 2 (HER2) status in breast cancer patients relies on HER2 amplification, identified through fluorescence in situ hybridization (FISH) or immunohistochemistry (IHC). However, hematoxylin and eosin (H\&E) tumor stains are more widely available, and accurately predicting HER2 status using H\&E could reduce costs and expedite treatment selection. Deep Learning algorithms for H&E have shown effectiveness in predicting various cancer features and clinical outcomes, including moderate success in HER2 status prediction. In this work, we employed a customized weak supervision classification technique combined with MoCo-v2 contrastive learning to predict HER2 status. We trained our pipeline on 182 publicly available H&E Whole Slide Images (WSIs) from The Cancer Genome Atlas (TCGA), for which annotations by the pathology team at Yale School of Medicine are publicly available. Our pipeline achieved an Area Under the Curve (AUC) of 0.85 across four different test folds. Additionally, we tested our model on 44 H&E slides from the TCGA-BRCA dataset, which had an HER2 score of 2+ and included corresponding HER2 status and FISH test results. These cases are considered equivocal for IHC, requiring an expensive FISH test on their IHC slides for disambiguation. Our pipeline demonstrated an AUC of 0.81 on these challenging H&E slides. Reducing the need for FISH test can have significant implications in cancer treatment equity for underserved populations.