Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images

TL;DR

This paper presents a deep learning system that assesses tumor-associated stroma in histopathology images, demonstrating high accuracy in breast cancer diagnosis by focusing on previously overlooked stromal tissue.

Contribution

It introduces a CNN-based approach for stromal assessment in breast cancer diagnosis, highlighting the diagnostic value of tumor-associated stroma.

Findings

Achieved an AUC of 0.92 in classification performance.

Demonstrated stromal tissue's discriminative power as a biomarker.

Evaluated on a large cohort of 646 biopsies.

Abstract

Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we propose a system for classification of hematoxylin and eosin (H&E) stained breast specimens based on convolutional neural networks that primarily targets the assessment of tumor-associated stroma to diagnose breast cancer patients. We evaluate the performance of our proposed system using a large cohort containing 646 breast tissue biopsies. Our evaluations show that the proposed system achieves an area under ROC of 0.92, demonstrating the discriminative power of previously neglected tumor-associated stroma as a diagnostic biomarker.