ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer

2025-01-24

AI Summary

Manual assessment of tumor-infiltrating lymphocytes (TILs) in triple-negative breast cancer (TNBC) is time-consuming, costly, and suffers from poor generalizability. Method: We propose ECTIL, a lightweight deep learning framework for TIL quantification that minimizes annotation burden—requiring only hundreds of coarse-grained clinical scores—and leverages a pathology foundation model to extract morphological features from whole-slide images (WSIs) for end-to-end regression of TIL levels. Results: Across five independent multicenter external cohorts, ECTIL achieves strong agreement with pathologist assessments (Pearson *r* = 0.54–0.74; AUROC = 0.80–0.94); each 10% increase in ECTIL score correlates with a significant 14% reduction in overall survival risk (HR = 0.86, *p* < 0.01). Model training completes in ~10 minutes, balancing clinical feasibility and predictive reliability—offering an efficient tool for immunotherapy patient selection and prognostic evaluation.

Technology Category

• Machine Learning: Other Foundations of Machine Learning
• Computer Vision: Other Foundations of Computer Vision
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations. We collected whole slide images (WSIs) with TILs scores and clinical data of 2,340 patients with BC from six cohorts including three randomised clinical trials. Morphological features were extracted from whole slide images (WSIs) using a pathology foundation model. Our label-efficient Computational stromal TIL assessment model (ECTIL) directly regresses the TILs score from these features. ECTIL trained on only a few hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all slides of five cohorts (ECTIL-combined) improved results on a held-out test set (r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that every 10% increase of ECTIL scores was associated with improved overall survival independent of clinicopathological variables (HR 0.86, p<0.01), similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL is highly concordant with an expert pathologist and obtains a similar hazard ratio. ECTIL has a fundamentally simpler design than existing methods and can be trained on orders of magnitude fewer annotations. Such a CTA may be used to pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a tool to assist clinicians in the diagnostic work-up of patients with BC. Our model is available under an open source licence (https://github.com/nki-ai/ectil).

Problem

• Tumor-Infiltrating Lymphocytes (TILs)
• Triple-Negative Breast Cancer
• Computer-Aided Assessment

Innovation

• ECTIL
• Breast Cancer TIL Assessment
• Open-source Model