Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients.

ABSTRACT:

Background

Deep learning has been extensively used in digital histopathology. The purpose of this study was to test deep learning (DL) algorithms for predicting the vital status of whole-slide image (WSI) of uveal melanoma (UM).

Methods

We developed a deep learning model (Google-net) to predict the vital status of UM patients from histopathological images in TCGA-UVM cohort and validated it in an internal cohort. The histopathological DL features extracted from the model and then were applied to classify UM patients into two subtypes. The differences between two subtypes in clinical outcomes, tumor mutation, and microenvironment, and probability of drug therapeutic response were investigated further.

Results

We observed that the developed DL model can achieve a high a

SUBMITTER: Wan Q 

PROVIDER: S-EPMC10239131 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature