Dual-Energy CT-Derived Parameters: A Promising Tool for Noninvasive Prediction of Glypican-3 in Hepatocellular Carcinoma

TL;DR

This study shows that dual-energy CT scans can noninvasively predict GPC3 expression in liver cancer, potentially reducing the need for invasive tests.

Contribution

The study introduces a noninvasive method using DECT parameters to predict GPC3 expression in hepatocellular carcinoma.

Findings

GPC3-positive tumors showed higher DECT parameters in the arterial phase compared to GPC3-negative tumors.

A model combining NID-AP and peritumoral enhancement achieved 78% specificity and 68% sensitivity in predicting GPC3 expression.

All DECT parameters demonstrated excellent interobserver reproducibility with ICC values above 0.75.

Abstract

Background/Objectives: Glypican-3 (GPC3), a membrane-bound heparan sulfate proteoglycan, has been identified as a promising target for both the diagnosis and treatment of hepatocellular carcinoma (HCC). However, the diagnosis of GPC3 expression mainly depended on invasive procedures. This study aimed to investigate the potential of dual-energy computed tomography (DECT)-derived parameters for noninvasive prediction of GPC3 expression in HCC. Methods: This retrospective study included 79 HCC patients with confirmed GPC3 immunohistochemistry and pretreatment contrast-enhanced DECT. Qualitative imaging features and quantitative DECT parameters, including iodine density of HCC (IDCa), normalized iodine density (NID), slope of spectral attenuation curve (λHU), and effective atomic number (Zeff), were evaluated in both arterial and portal venous phases. Univariate and multivariate logistic regression analyses were employed to identify independent predictors, and a combined model was subsequently constructed. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic efficiency of imaging parameters in predicting GPC3 expression. Interobserver agreement of DECT parameters was evaluated using intraclass correlation coefficients (ICC). Results: GPC3-positive HCCs demonstrated significantly higher arterial phase (AP) IDCa, NID, λHU, and Zeff (all p ≤ 0.001) than GPC3-negative HCCs. Multivariate logistic regression analysis identified NID-AP [Odds ratio (OR) = 2.00, p = 0.010] and peritumoral enhancement (OR = 9.25, p = 0.046) as independent predictors. The model combining NID-AP and peritumoral enhancement achieved the best diagnostic performance (AUC = 0.781, sensitivity = 67.86%, specificity = 78.26%) for predicting GPC3 expression. All DECT-derived parameters showed excellent interobserver reproducibility (ICC > 0.75 for all). Conclusions: Parameters derived from DECT, especially combining NID-AP and peritumoral enhancement, may be a potential tool to noninvasively predict GPC3 expression in HCC.

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