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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