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
NIH Invests Additional $12.6M in USC-Led Imaging AI for Alzheimer's
NIH has renewed and expanded its support for a USC-led consortium developing AI to decode and treat Alzheimer's using imaging and genomic data.
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.
•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.