Integrating pathomics and deep learning for subtyping uveal melanoma: identifying high-risk immune infiltration profiles.

ABSTRACT:

Purpose

Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults, characterized by high mortality despite its relatively low incidence. This study aimed to utilize unsupervised learning techniques to identify a high immune infiltration subtype of UVM and improve patient stratification based on mortality risk.

Methods

A total of 70 hematoxylin and eosin (H&E) stained whole-slide images (WSIs) of UVM were collected from the Genomic Data Commons (GDC) data portal, along with genomic and clinical data. An additional validation cohort of 68 UVM patients from West China Hospital was included. Pathomic features were extracted using CellProfiler software, and deep learning models were constructed for classification and survival prediction. Unsupervised cluste

SUBMITTER: Wan Q 

PROVIDER: S-EPMC12283581 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature