2022/06/23 by Adam Shephard, Mostafa Jahanifar, Shephard, Adam +15 · 2 citations
Biochemistry, Genetics and Molecular Biology · Medicine · #Cancer Immunotherapy and Biomarkers #Cancer-related molecular mechanisms research #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Inflammatory Biomarkers in Disease Prognosis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2206.11943
openalex publication_date 2022/06/23 · openalex created_date 2022/06/29 · openalex updated_date 2026/07/28
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.