Multi-resolution deep learning characterizes tertiary lymphoid structures and their prognostic relevance in solid tumors.

ABSTRACT:

Background

Tertiary lymphoid structures (TLSs) are dense accumulations of lymphocytes 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 proposed 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.

Methods

We introduce an approach based on HookNet-TLS, a multi-resolution deep learning model, for automated and unbiased TLS quantification and identification of germinal centers in routine hematoxylin and eosin stained digital pathology slides. We developed HookNet-TLS using n = 1019 manually annota

SUBMITTER: van Rijthoven M 

PROVIDER: S-EPMC10770129 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature