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Abstract #1416

MRI-based Radiomics Predict β-catenin Mutation Status and Prognosis in Hepatocellular Carcinoma: A Multi-Institutional Study

Qingqing Chen1, Yuanyuan Zhang1, Yechong Huang2, and Hongjie Hu1
1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China, 2SenseTime Research, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Cancer, hepatocellular carcinoma

Motivation: Mutations in the β-catenin gene are significantly associated with immune evasion and prognosis in HCC patients.

Goal(s): Develop and validate radiomics models using dynamic contrast-enhanced MRI to identify β-catenin status and prognosis in HCC.

Approach: 465 HCC patients from four centers were enrolled. Tumor boundaries were delineated, and six machine learning algorithms were used to predict β-catenin mutation status. Ten radiomics models, a clinical model, and combined clinical-radiomics models were developed.

Results: The GBM-based radiomics model outperformed others. The ADC model was the best single-sequence, while the all-sequence model was the most effective combined model. Integrating clinical factors achieved the highest predictive performance.

Impact: The radiomics model using DCE-MRI and clinical factors offers a new tool for personalized treatment.

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