Automatic quantification of tumor-stroma ratio as a prognostic marker for pancreatic cancer.

ABSTRACT:

Purpose

This study aims to introduce an innovative multi-step pipeline for automatic tumor-stroma ratio (TSR) quantification as a potential prognostic marker for pancreatic cancer, addressing the limitations of existing staging systems and the lack of commonly used prognostic biomarkers.

Methods

The proposed approach involves a deep-learning-based method for the automatic segmentation of tumor epithelial cells, tumor bulk, and stroma from whole-slide images (WSIs). Models were trained using five-fold cross-validation and evaluated on an independent external test set. TSR was computed based on the segmented components. Additionally, TSR's predictive value for six-month survival on the independent external dataset was assessed.

Results

Median Dice (inter-quartile range

SUBMITTER: Vendittelli P 

PROVIDER: S-EPMC11108171 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature