Preoperative 3D morphological phenotyping of hepatocellular carcinoma via a foundation model: a non-invasive biomarker for recurrence risk stratification

Background

Imaging features play a central role in hepatocellular carcinoma (HCC) staging, but current systems remain limited in prognostic precision and treatment guidance. Pathological gross classification, which categorises HCC into four subtypes based on tumour margin morphology, represents a morphological surrogate of aggressive tumour behaviour encompassing histological and molecular features and has demonstrated value in predicting prognosis and guiding adjuvant locoregional therapy selection. Importantly, gross classification is assessable through imaging, but its evaluation utterly depends on postoperative pathology and remains subjective, time-consuming and poorly reproducible. We aimed to develop a three-dimensional (3D) Foundation Model-driven system to automatically perform non-invasive morphological phenotyping and to evaluate its prognostic utility.