ArticleDiscover oncology2025
Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Tumor-infiltrating lymphocytes (TILs) are established prognostic biomarkers in high-grade breast cancer, yet traditional manual assessment suffers from inter-observer variability, subjective interpretation, and limited scalability. We propose a novel artificial intelligence-based framework for automated TIL quantification that integrates foundation-model embeddings, graph-based spatial attention, and uncertainty calibration to improve generalizability and clinical reliability. The multi-stage pipeline incorporates advanced preprocessing, colour normalisation, multi-scale feature extraction using dilated residual networks, and hybrid detection-segmentation via YOLO and U-Net for accurate lymphocyte detection. Clinical validation was conducted on a multi-institutional dataset comprising 2847 cases from BINO Hospital and two independent external cohorts: TCGA-BRCA (1020 slides) and Camelyon17 (500 slides). The framework achieved 94.7% accuracy and AUC = 0.92 internally, with robust external performance (92.1% / 0.895 and 91.3% / 0.882), demonstrating effective cross-scanner and cross-staining adaptability. Blinded multi-reader analysis involving expert pathologists showed strong concordance (Pearson’s r = 0.879), and a prospective deployment-style pilot achieved real-time processing in 2.3 min per slide, reducing assessment time by 87% compared to manual scoring. Prognostic evaluation using Kaplan-Meier survival analysis revealed a significant correlation between AI-derived TIL density and disease-free survival (HR = 0.642, p < 0.001), thereby enhancing clinical decision support for risk stratification and treatment planning. Comparative benchmarking against TILScout, CommunEng-TIL, DeepTILs, and QuPath demonstrates superior accuracy, computational efficiency, and clinical robustness. This framework provides standardised, reproducible, and high-throughput TIL quantification, addressing the limitations of manual evaluation and establishing a scalable solution for precision oncology in diverse pathology settings.
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