Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images
This addresses breast cancer diagnosis by focusing on stroma, which is often neglected, offering a potential new biomarker for improved detection.
The paper tackled breast cancer diagnosis by assessing tumor-associated stroma in histopathology images, achieving an area under ROC of 0.92 on a dataset of 646 biopsies.
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.