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.
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.
Based on the results obtained from multivariable analyses, alpha-fetoprotein levels > 20 ng/mL, 75th percentile ADC value < 1.48 ×103 mm2/s and R2* value ≥ 38.6 sec-1 were found to be the significant independent predictors of GPC3-positive HCC. The SMOTE-LR model based on three features achieved the best predictive performance in the training (AUC, 0.909; accuracy, 83.7%) and validation sets (AUC, 0.829; accuracy, 82.1%) with a good calibration performance and clinical usefulness.
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.