Computed tomography radiomics-based machine learning nomogram for preoperative prediction of glypican-3 expression in...

Hepatocellular carcinoma (HCC) is among the most common and fatal primary liver malignancies. Glypican-3 (GPC3) is a useful biomarker for HCC diagnosis and targeted therapy, but reliable noninvasive approaches for predicting GPC3 expression before surgery remain limited.

To develop and validate a computed tomography (CT)-based radiomics model using machine learning for the preoperative prediction of GPC3 expression in HCC.

This retrospective study included 103 patients with pathologically confirmed HCC who underwent contrast-enhanced CT at two centers between January 2013 and October 2023. Patients were assigned to training (n = 72) and testing (n = 31) sets using a 7:3 stratified random sampling. Regions of interest were manually delineated on non-contrast, arterial, portal venous, and delayed-phase images, followed by radiomic feature extraction. After feature selection, radiomics models were constructed using eight machine learning algorithms. Independent clinical predictors were identified by univariate and multivariate logistic regression and used to construct a clinical model. A combined model was then developed by integrating the optimal radiomics model with the clinical predictors, and a corresponding nomogram was generated. Model performance was evaluated using the area under the curve (AUC), DeLong test, net reclassification improvement, and integrated discrimination improvement. Calibration curves and decision curve analysis were used to assess the clinical utility of the nomogram.

Alpha-fetoprotein and total bilirubin were independent clinical predictors of GPC3 expression. After feature selection, 10 radiomic features were retained. Among the radiomics models, the random forest classifier showed the strongest predictive performance, with an AUC of 0.959 in the training set and 0.862 in the testing set. Integration of the radiomics signature with alpha-fetoprotein and total bilirubin further improved model performance, yielding AUCs of 0.979 and 0.948 in the training and testing sets, respectively. In both cohorts, the combined model showed positive reclassification gains, with net reclassification improvement > 0 and integrated discrimination improvement > 0. Calibration curves suggested a better goodness-of-fit for the clinical model. Decision curve analysis indicated that the nomogram derived from the combined model provided greater net clinical benefit across a broad range of threshold probabilities.

A nomogram combining a random forest-based CT radiomics model with clinical predictors showed strong performance and potential clinical value for preoperative prediction of GPC3 expression in HCC.