MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.

ABSTRACT:

Background & aims

Microvascular invasion (MVI) is associated with poor prognosis in hepatocellular carcinoma (HCC). Topology may improve the predictive performance and interpretability of deep learning (DL). We aimed to develop and externally validate an MRI-based topology DL model for preoperative prediction of MVI.

Methods

This dual-centre retrospective study included consecutive surgically treated HCC patients from two tertiary care hospitals. Automatic liver and tumour segmentations were performed with DL methods. A pure convolutional neural network (CNN) model, a topology-CNN (TopoCNN) model and a topology-CNN-clinical (TopoCNN+Clinic) model were developed and externally validated. Model performance was assessed using the area under the receiver operating characteristic

SUBMITTER: Zheng T 

PROVIDER: S-EPMC11849444 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature