Multiparametric MRI combined with clinical factors to predict glypican-3 expression of hepatocellular carcinoma.

Objectives: The present study aims at establishing a noninvasive and reliable model for the preoperative prediction of glypican 3 (GPC3)-positive hepatocellular carcinoma (HCC) based on multiparametric magnetic resonance imaging (MRI) and clinical indicators. Methods: As a retrospective study, the subjects included 158 patients from two institutions with surgically-confirmed single HCC who underwent preoperative MRI between 2020 and 2022. The patients, 102 from institution I and 56 from institution II, were assigned to the training and the validation sets, respectively. The association of the clinic-radiological variables with the GPC3 expression was investigated through performing univariable and multivariable logistic regression (LR) analyses. The synthetic minority over-sampling technique (SMOTE) was used to balance the minority group (GPC3-negative HCCs) in the training set, and diagnostic performance was assessed by the area under the curve (AUC) and accuracy. Next, a prediction nomogram was developed and validated for patients with GPC3-positive HCC. The performance of the nomogram was evaluated through examining its calibration and clinical utility. Results: Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75 Conclusions: The nomogram combining multiparametric MRI and clinical indicators is found to have satisfactory predictive efficacy for preoperative prediction of GPC3-positive HCC. Accordingly, the proposed method can promote individualized risk stratification and further treatment decisions of HCC patients.