TIAger: Tumor-Infiltrating Lymphocyte Scoring in Breast Cancer for the TiGER Challenge

TL;DR

This paper presents a computer vision algorithm for quantifying tumor-infiltrating lymphocytes in breast cancer tissue, demonstrating its prognostic value and achieving top performance in the TiGER challenge.

Contribution

The authors developed a novel algorithm for segmenting tumor and stroma, localizing tumor regions, and scoring TILs, leading to improved survival prediction in breast cancer.

Findings

Achieved a tumor-stroma weighted Dice score of 0.791

Attained a FROC score of 0.572 for lymphocytic detection

Reached a C-index of 0.719 for survival prediction

Abstract

The quantification of tumor-infiltrating lymphocytes (TILs) has been shown to be an independent predictor for prognosis of breast cancer patients. Typically, pathologists give an estimate of the proportion of the stromal region that contains TILs to obtain a TILs score. The Tumor InfiltratinG lymphocytes in breast cancER (TiGER) challenge, aims to assess the prognostic significance of computer-generated TILs scores for predicting survival as part of a Cox proportional hazards model. For this challenge, as the TIAger team, we have developed an algorithm to first segment tumor vs. stroma, before localising the tumor bulk region for TILs detection. Finally, we use these outputs to generate a TILs score for each case. On preliminary testing, our approach achieved a tumor-stroma weighted Dice score of 0.791 and a FROC score of 0.572 for lymphocytic detection. For predicting survival, our model achieved a C-index of 0.719. These results achieved first place across the preliminary testing leaderboards of the TiGER challenge.