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Abstract #5120

MRI-based radiomics model for predicting the nature of nodules in cirrhotic liver

Xueling Wen1, Jiawen Luo1, Wenxue Pan1, Wenjia Wang2, and Caiyun Yu1
1The Second Hospital of Dalian Medical University, Dalian city, Liaoning province, China, 2MR Research Center China, GE HealthCare, Dalian city, Liaoning province, China

Synopsis

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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