Keywords: computational pathology, pancreatic cancer, survival, tumor-stroma ratio
TL;DR: We developed an automatic method to quantify tumor-stroma ratio in pancreatic cancer slides and assess its prognostic relevance.
Abstract: The current diagnostic and outcome prediction methods for pancreatic cancer lack prognostic power. As such, identifying novel biomarkers using machine learning has become of increasing interest. In this study, we introduce a novel method for estimating the tumor-stroma ratio (TSR) in whole slide images (WSIs) of pancreatic tissue and assess its potential as a prognostic biomarker. A multi-step strategy for estimating TSR is proposed, including epithelium segmentation based on an immunohistochemical reference standard, a coarse pancreatic cancer segmentation, and a post-processing pipeline for TSR quantification. The resultant segmentation models are validated on external test sets using the Dice coefficient, and additionally, the TSR's potential as a prognostic factor is assessed using survival analysis, resulting in a C-index of 0.61.