Machine learning model improves differentiation of regenerative and dysplastic nodules and hepatocellular carcinoma, suggesting non-invasive diagnosis may be possible.
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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Wen et al. (2025) studied this question.
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