Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal Cancers.

ABSTRACT:

Importance

Tertiary lymphoid structures (TLSs) are associated with a favorable prognosis and improved response to cancer immunotherapy. The current approach for evaluation of TLSs is limited by interobserver variability and high complexity and cost of specialized imaging techniques.

Objective

To develop a machine learning model for automated and quantitative evaluation of TLSs based on routine histopathology images.

Design, setting, and participants

In this multicenter, international diagnostic/prognostic study, an interpretable machine learning model was developed and validated for automated detection, enumeration, and classification of TLSs in hematoxylin-eosin-stained images. A quantitative scoring system for TLSs was proposed, and its association with survival was

SUBMITTER: Li Z 

PROVIDER: S-EPMC10408275 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature