ArticleAbdominal radiology (New York)2026
Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeCurrent hepatocellular carcinoma (HCC) surveillance guidelines rely on manually defined LI-RADS (Liver Imaging Reporting and Data System) features rather than imaging data analysis. This study evaluates the feasibility of machine-learning (ML)-based image analysis frameworks to identify and localize hepatic parenchyma at elevated risk for HCC.
methodsIn this retrospective study, cirrhotic patients with HCC diagnosis undergoing MRI between 2008 and 2023 were included. The analysis included negative screening MRI preceding a positive screening MRI confirming a LR-5 lesion within 18 months. Volume-of-interest (VOI) annotations of 'non-malignant' and 'malignant' liver tissue on the screening MRI were manually or automatically placed, mapped from future HCC lesion on positive screening MRI. Radiomics were extracted from these VOIs using PyRadiomics. Logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting (XGB) models were trained and validated across four manual/automatic annotation combinations. Exploratory voxel-level heatmaps were generated to visualize high-risk HCC areas. Model performance was summarized using median values and 95% non-parametric confidence intervals.
results121 patients (65 ± 9.56; 99 men) were included. The best model performances for LR, RF, and XGB were achieved when training and validating on manual annotations: (AUC [95% CI]: 0.75 [0.72-0.78]; 0.80 [0.78-0.81]; 0.79 [0.77-0.81], respectively). Predictions from LR-3 lesions outperformed regions without visible precursor territory (RF AUC [95% CI]: 0.86 [0.84-0.88] vs. 0.66 [0.63-0.70]). In 36 out of 121 patients (30%), heatmaps showed visually increased probability signals in the region where HCC subsequently became visible on follow-up imaging.
conclusionThe results demonstrate the feasibility of ML-based MRI analysis for identifying liver regions at increased risk for HCC development prior to radiologic diagnosis, particularly in the presence of precursor lesions.
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