Prediction of recurrence after resection in hepatocellular carcinoma via whole liver deep learning on preoperative contrast-enhanced CT.

ABSTRACT: This study aimed to develop a fully automated survival prediction (FASP) system that analyzes whole-liver regions from preoperative contrast-enhanced CT scans for predicting recurrence-free survival (RFS) after curative resection in Hepatocellular carcinoma (HCC). FASP comprised three consecutive components: automatic liver and tumor segmentation models, and a RFS prediction model, LiverSurv, all based on deep convolutional neural networks. FASP was compared against a clinical model leveraging clinical factors and three tumor-based methods using semantic, radiomic, or deep learning features. A total of 827 patients were included across the development, internal test, and external test sets. In the internal and external test sets, FASP achieved concordance indices (C-indices) of 0.646 (95%

SUBMITTER: Huang C 

PROVIDER: S-EPMC12672729 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature