The Tissue Image Analytics (TIA) Centre hosts a seminar series for those interested in computational pathology. The series invites researchers and leaders in computational pathology and related fields to present their work and foster thought-provoking discussions.

This seminar features Mart van Rijthoven, Radboud UMC, Nijmegen, The Netherlands.

Abstract

Tumor-infiltrating lymphocytes (TILs) is a recognized prognostic biomarker in breast cancer. However, poor interobserver agreement and limited reproducibility highlight the need for computational approaches. Despite advances, adoption of computational models has been hindered by lack of standardized methods and robust benchmarks. To address this, we launched TIGER, an international competition to build open-source computational TILs (cTILs) models. Here, we present a multi-centric analysis of cTILs methods on resections and biopsies from 3,708 human epidermal growth factor receptor 2-positive (HER2+) and triple-negative breast cancers (TNBC) from clinical practice and phase 3 trials. We report benchmarks on image analysis performance, show strong agreement of cTILs with pathologists, and demonstrate positive association of cTILs with neoadjuvant therapy response in HER2+, superior to visually scored TILs. We also show that cTILs add independent prognostic information to clinical variables in TNBC resections. Data, methods and benchmarks are publicly available: https://tiger.grand-challenge.org/

Bio

Mart van Rijthoven did his PhD work in the Computational Pathology Group at Radboud University Medical Center, focusing on deep learning for biomarker quantification in whole-slide images. His work includes the HookNet model, quantification of tertiary lymphoid structures, and few-shot learning for histopathology. He also developed the WholeSlideData package and co-organized the TIGER challenge.