DEEP LEARNING-BASED ASSESSMENT OF TUMOR-ASSOCIATED STROMA FOR DIAGNOSING BREAST CANCER IN HISTOPATHOLOGY IMAGES

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.

Keywords: Breast Cancer; Convolutional Neural Networks; Digital pathology; Tumor Associated Stroma.

Fig. 1.

Sample classification result by CNN I (middle image) and CNN II (bottom image) for a WSI containing breast cancer. In the middle image, green, orange and red represent fat, stroma, and epithelium, respectively. The bottom image shows the likelihood map representing tumor-stroma probability overlaid on the original image (green represents low probability and red represents high probability of belonging to tumor-associated stroma class).

Fig. 2.

The ROC curve of the proposed system. Confidence interval is only shown for the system using both feature sets from CNN I and II.