Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

TL;DR

This study demonstrates that deep learning models applied to H&E-stained whole-slide images can accurately classify breast cancer molecular subtypes, offering a cost-effective alternative to traditional methods.

Contribution

The paper introduces a novel pipeline leveraging deep learning on WSIs for molecular subtyping, achieving high accuracy with a two-step classification process.

Findings

Achieved 0.95 macro F1 score for tumor detection

Achieved 0.73 macro F1 score for molecular subtyping

Proposed a scalable, image-based classification pipeline

Abstract

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.