Deep learning-based quantification of tumor-infiltrating lymphocytes as a prognostic indicator in nasopharyngeal carcinoma: multicohort findings.

ABSTRACT:

Background

Nasopharyngeal carcinoma (NPC) features a tumor-immune microenvironment rich in tumor-infiltrating lymphocytes (TILs), important for prognosis but labor-intensive to quantify. This study evaluates a deep learning model to quantify TILs (TIL_DL) in hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) of NPC and explores the association of TIL_DL percentage with patient outcomes and response to immune checkpoint blockade (ICB).

Methods

We retrospectively analyzed 435 nonmetastatic NPC patients from two centers, divided into a training cohort (n = 220) and a validation cohort (n = 215). An additional cohort of de novo metastatic NPC patients receiving ICB therapy (n = 63) was included. The deep learning model calculated TIL_DL p

SUBMITTER: Lu T 

PROVIDER: S-EPMC12270627 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature