Deep learning-based MRI model for predicting P53-mutated hepatocellular carcinoma.

ABSTRACT:

Background

The P53-mutated Hepatocellular Carcinoma (HCC) is an aggressive variant associated with vascular endothelial growth factor (VEGF) overexpression and increased microvascular density. This study aimed to develop an MRI-based deep learning model for predicting P53-mutated HCC.

Methods

A total of 312 HCC patients who underwent gadolinium-enhanced MRI and were pathologically confirmed between January 2018 and December 2023 were retrospectively enrolled. Participants were randomly divided into training and test dataset at an 8:2 ratio. We developed an EfficientNetV2-based deep learning model, constructing arterial phase (AP) model, portal venous phase (VP), T2-weighted imaging (T2WI), hepatobiliary phase (HBP) single-sequence model, and combined models to predict P53 mu

SUBMITTER: Jia L 

PROVIDER: S-EPMC12723874 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature