Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma.

ABSTRACT:

Background

Computer-aided histopathological image analysis is increasingly used for image evaluation and decision-making in cancer patients. This study extracted quantitative histopathological image features to predict molecular features, and combined them with omics data to predict prognosis of hepatocellular carcinoma (HCC) patients.

Methods

Totally 334 patients from The Cancer Genome Atlas were divided equally into the training and testing sets. Histopathological image features and multiple omics data (somatic mutation, mRNA expression, and protein expression) were used alone or in combination to build prediction models through machine learning. Areas under receiver operating characteristic curves (AUCs) were assessed for 1-year, 3-year, and 5-year overall survival (OS).<

SUBMITTER: Chen L 

PROVIDER: S-EPMC12324167 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature