What question did this study set out to answer?

The research aims to develop a noninvasive method to predict glypican-3 expression and assess its impact on recurrence-free survival in hepatocellular carcinoma patients.

April 13, 2026Open Access

Noninvasive prediction of glypican-3 expression and recurrence-free survival in hepatocellular carcinoma using contrast-enhanced ultrasound

Key Points

The research aims to develop a noninvasive method to predict glypican-3 expression and assess its impact on recurrence-free survival in hepatocellular carcinoma patients.
Conducted a retrospective analysis of 316 HCC patients undergoing preoperative CEUS.
Identified predictive CEUS features and clinical variables using statistical regression methods.
Constructed and validated a nomogram model in training and validation cohorts.
Evaluated model performance through various statistical analyses including receiver operating characteristic curves.
Achieved area under the curve values of 0.90 in the training cohort and 0.88 in the validation cohort for the nomogram.
Positive predictive value of 97.14% and 96.77% in the training and validation cohorts, respectively.
Glypican-3 was identified as an independent prognostic factor for recurrence-free survival with a hazard ratio of 3.56.
High nomogram scores correlated with significantly shorter recurrence-free survival (P < 0.001).

Abstract

Glypican-3 (GPC-3) is a promising biomarker and therapeutic target in hepatocellular carcinoma (HCC), yet its noninvasive preoperative assessment remains challenging. This study developed and validated a nomogram model integrating contrast-enhanced ultrasound (CEUS) features with clinical variables to predict GPC-3 expression and assess its prognostic significance for recurrence-free survival (RFS). A retrospective analysis of 316 HCC patients who underwent preoperative CEUS was performed. Predictive CEUS features and clinical variables were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression. The nomogram was constructed and validated in training and validation cohorts, with performance assessed by receiver operating characteristic curve, calibration curves, and decision curve analysis. Kaplan-Meier and Cox regression analyses were used to assess the association between GPC-3 expression, nomogram scores, and RFS. The median age of the patients was 59 years (IQR, 52–67 years), with a predominance of male patients (267/316, 84.49%). Four predictors of GPC-3 expression were identified: alpha-fetoprotein, feeding artery, hyper-enhanced rim in the arterial phase, and necrosis. The nomogram achieved area under the curve values of 0.90 (95% CI: 0.86, 0.94) in the training cohort and 0.88 (95% CI: 0.80, 0.94) in the validation cohort. The positive predictive value and negative predictive value were 97.14% (95% CI: 94.38, 99.90) and 54.32% (95% CI: 43.47, 65.17) in the training cohort, and 96.77% (95% CI: 91.80, 99.99) and 45.46% (95% CI: 28.12, 62.08) in the validation cohort, respectively. Calibration plots and decision curve analysis confirmed its clinical utility. GPC-3 expression was identified as an independent prognostic factor for RFS (hazard ratio: 3.56; 95% CI: 2.18, 5.83), and patients with high nomogram scores had significantly shorter RFS (P < 0.001). This CEUS-based nomogram provides an accurate, noninvasive method for predicting GPC-3 expression and RFS in HCC patients, serving as a valuable tool for personalized treatment strategies and optimizing GPC-3-targeted therapies.

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