Combining CT-based radiomics and deep learning features with clinical data enhances prediction of hepatocellular carcinoma risk in cirrhosis patients.
Key Details
- 1Study used a multicenter, prospective cohort of 2,411 cirrhosis patients in China (2018–2023).
- 2All patients underwent 3-phase contrast-enhanced abdominal CT at baseline.
- 3AI model extracted radiomics (PyRadiomics) and deep learning (ResNet-18) features from liver and spleen on CT.
- 4The integrated aMAP-CT model significantly outperformed standard clinical models (AUC 0.809–0.869).
- 5Model stratified patients into high- (26.3% incidence) and low-risk (1.7%) groups over three years.
- 6Stepwise application identified 7% of patients at very high risk for HCC (27.2% three-year incidence).
Why It Matters
This study demonstrates how AI-driven analysis of imaging data can meaningfully improve personalized risk stratification in a major oncologic domain, supporting earlier HCC detection and potentially better clinical outcomes for high-risk cirrhosis patients.
Source
EurekAlert
Related News
•EurekAlert
AI Method Automates X-ray Absorption Spectroscopy for Material Analysis
Researchers have developed an AI-based approach to automate and enhance the analysis of X-ray absorption spectroscopy (XAS) data for materials science.
•EurekAlert
BraDiPho: New 3D AI Atlas Integrates Brain Dissections with MRI
Researchers have developed BraDiPho, a tool that merges ex-vivo photogrammetric brain dissection data with in-vivo MRI tractography using AI.
•EurekAlert
AI Maps Genetic Factors Shaping the Corpus Callosum via MRI Scans
USC researchers used AI to analyze MRI scans and uncover the genetic architecture of the brain's corpus callosum.