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 Accurately Predicts Lymph Node Extension in HPV-related Throat Cancer via CT
An AI pipeline automates lymph node segmentation and extranodal extension prediction from CT in HPV-positive oropharyngeal cancer, correlating with patient outcomes.
•EurekAlert
Study Finds Doctors, Nurses Outperform AI for Emergency Triage
Clinical staff outperform ChatGPT AI at emergency department triage, but AI shows promise as a support tool for urgent cases.
•EurekAlert
AI Enables Rapid Body Composition Analysis on Routine MRI to Predict Cardiometabolic Risk
An open-source AI tool can quickly and accurately assess body composition from routine MRI, helping identify patients at elevated cardiometabolic risk.