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
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