Pathomics Image Analysis of Tumor Infiltrating Lymphocytes (TILs) in Colon Cancer

TL;DR

This paper introduces a deep learning method to analyze colon cancer images and map immune cell infiltration, which can predict patient survival and guide precision oncology.

Contribution

A novel Pathomics workflow using deep learning to quantify and map tumor infiltrating lymphocytes (TILs) in colon cancer.

Findings

High TILs% correlates with improved overall survival and progression-free interval in colon cancer patients.

Tumor-TIL maps reveal distinct TIL-rich and TIL-poor tumor habitats unique to each patient sample.

TILs% is a significant prognostic biomarker validated through Kaplan-Meier and Cox regression analyses.

Abstract

We developed a deep learning Pathomics image analysis workflow to generate spatial Tumor-TIL maps to visualize and quantify the abundance and spatial distribution of tumor infiltrating lymphocytes (TILs) in colon cancer. Colon cancer and lymphocyte detection in hematoxylin and eosin (H&E) stained whole slide images (WSIs) has revealed complex immuno-oncologic interactions that form TIL-rich and TIL-poor tumor habitats, which are unique in each patient sample. We compute Tumor%, total lymphocyte%, and TILs% as the proportion of the colon cancer microenvironment occupied by intratumoral lymphocytes for each WSI. Kaplan-Meier survival analyses and multivariate Cox regression were utilized to evaluate the prognostic significance of TILs% as a Pathomics biomarker. High TILs% was associated with improved overall survival (OS) and progression-free interval (PFI) in localized and metastatic colon cancer and other clinicopathologic variables, supporting the routine use of Pathomics Tumor-TIL mapping in biomedical research, clinical trials, laboratory medicine, and precision oncology.