Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.

ABSTRACT:

Objectives

The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC).

Materials and methods

A total of 858 patients from primary cohort and two external cohorts were included. 3.0T or 1.5T axial portal venous phase MRI images were collected. We conducted radiomics feature-driven K-means clustering for automatic partition to reveal ITH. 2.5D and 3D deep learning models based on ResNet architecture were trained to extract deep learning hidden features of each subregion. The selected features were used to train Random Forest classifier, which constr

SUBMITTER: Song S 

PROVIDER: S-EPMC11917083 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature