Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC.

ABSTRACT:

Background & aims

Microvascular invasion (MVI) is a key determinant of recurrence and poor outcomes in hepatocellular carcinoma (HCC), yet accurate preoperative detection remains challenging. A deep-learning model integrating multiphase MRI and clinical features was developed and externally validated to non-invasively predict MVI and stratify postoperative recurrence risk.

Methods

Clinico-radiological data from 924 patients with resected HCC (2014-2023, five tertiary centers in China) were retrospectively assembled. A deep-learning model (DL-TriFusion) integrating multiphase MRI and clinical variables for MVI prediction was trained (n = 361), internally validated (n = 155), and externally validated across three centers (n = 408; 188/136/84); whether recurrence risk stratific

SUBMITTER: Li F 

PROVIDER: S-EPMC12810540 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature