Multi-resolution deep learning characterizes tertiary lymphoid structures in solid tumors
Mart van Rijthoven, Simon Obahor, Fabio Pagliarulo, Maries F. van den Broek, Peter Schraml, Holger Moch, Jeroen van der Laak, Francesco Ciompi, Karīna Siliņa · bioRxiv (Cold Spring Harbor Laboratory) · 2023
Abstract Tertiary lymphoid structures (TLSs) are dense accumulations of lym-phocytes in inflamed peripheral tissues, including cancer, and are associated with improved survival and response to immunotherapy in various solid tumors. Histological TLS quantification has been pro-posed as a novel predictive and prognostic biomarker, but lack of standardized methods of TLS characterization hampers assessment of TLS densities across different patients, diseases, and clinical centers. We introduce a novel approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hema-toxylin and eosin stained digital pathology slides. We developed a HookNet-TLS model using n=1019 manually annotated TCGA slides from clear cell renal cell carcinoma, muscle-invasive blad-der cancer, and lung squamous cell carcinoma. We show that HookNet-TLS automates TLS quantification with a human-level performance and demonstrates prognostic associations similar to visual assessment. We made HookNet-TLS publicly available to aid the adoption of objective TLS assessment in routine pathology.