Keywords: Diagnosis/Prediction, Liver
Motivation: We developed and validated a machine learning model combining semantic and radiomic features from MRI to differentiate regenerative nodules, dysplastic nodules, and hepatocellular carcinoma in cirrhotic liver.
Goal(s): To differentiate regenerative nodules, dysplastic nodules, and hepatocellular carcinoma in cirrhotic liver.
Approach: We developed and validated a machine learning model combining semantic and radiomic features from MRI to differentiate regenerative nodules, dysplastic nodules, and hepatocellular carcinoma in cirrhotic liver.
Results: The combined model achieved superior performance (AUC=0.936) compared to single-feature models in a multicenter study of 266 patients.
Impact: It provides a promising tool for non-invasive characterization of cirrhotic nodules.
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