ArticleAmerican journal of translational research2026
Explainable machine learning-based multiphase contrast-enhanced CT radiomics for noninvasively predicting GPC3 expression in hepatocellular carcinoma: a bicentric study.
Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesAccurate pre-operative prediction of Glypican-3 (GPC3) profiles in hepatocellular carcinoma (HCC) is essential for selecting candidates for targeted therapy and individualized treatment. This study aimed to develop and externally validate an explainable machine learning model for noninvasively prediction of GPC3 expression in HCC.
methodsA total of 301 HCC patients were retrospectively enrolled from two centers (Center 1: 241; Center 2: 60) between January 2016 and September 2024. Radiomics features were extracted from arterial (AP), portal venous (PVP), and delayed phase (DP) images. Robust features were selected via a sequential pipeline including interobserver agreement screening (intraclass correlation coefficient [ICC] > 0.8), Spearman correlation analysis (Ρ > 0.85), and recursive feature elimination (RFE). Single-phase (AP, PVP, DP), combined-phase (APD) radiomics, clinical, and clinical-radiomics models were constructed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), and SHAP analysis was applied for model interpretability.
resultsThe combined clinical-radiomics model outperformed the clinical model, achieving an AUC of 0.886 in the training cohort, 0.880 during internal validation cohort, and 0.839 during external validation cohort (P < 0.05). The APD radiomics model showed performance non-inferior to that of the combined model (P > 0.05) and outperformed all single-phase models. AFP > 7 ng/mL (OR=3.257) and intratumoral necrosis (OR=2.947), as confirmed by multivariate logistic regression, were independent predictors for GPC3 positivity. SHAP analysis further identified radiomics score as the most influential predictor (mean |SHAP|=1.84).
conclusionThe multiphase CECT radiomics-based combined model demonstrates good cross-center generalizability and enables accurate noninvasive prediction of GPC3 expression status in HCC. This model may provide a reliable imaging-based tool for screening candidate patients for GPC3-targeted therapies.
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