Contrast-enhanced ultrasound and clinical features for preoperative prediction of GPC-3 expression in hepatocellular carcinoma.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
197 patients with pathologically confirmed HCC were retrospectively enrolled.
I · Intervention 중재 / 시술
추출되지 않음
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
Our study showed that heterogeneous arterial phase hyperenhancement (APHE), mosaic architecture, alpha-fetoprotein (AFP) ≥ 100 ng/mL, rim enhancement in arterial phase (AP), neutrophil count (Nc) level and age were independently associated with GPC-3 expression.
[PURPOSE] Glypican 3 (GPC-3) in hepatocellular carcinoma (HCC) is receiving attention as a potential biomarker for treatment options.
- 95% CI 0.622-0.878
APA
Shu Q, Pang C, et al. (2026). Contrast-enhanced ultrasound and clinical features for preoperative prediction of GPC-3 expression in hepatocellular carcinoma.. Abdominal radiology (New York). https://doi.org/10.1007/s00261-026-05388-2
MLA
Shu Q, et al.. "Contrast-enhanced ultrasound and clinical features for preoperative prediction of GPC-3 expression in hepatocellular carcinoma.." Abdominal radiology (New York), 2026.
PMID
41575493
Abstract
[PURPOSE] Glypican 3 (GPC-3) in hepatocellular carcinoma (HCC) is receiving attention as a potential biomarker for treatment options. However, GPC-3 expression can only be assessed through surgical pathology. Therefore, we aim to develop and validate a practical tool based on contrast-enhanced ultrasound (CEUS) and clinical features to preoperatively identify GPC-3 expression in HCC.
[METHODS] From August 2021 to July 2024, 197 patients with pathologically confirmed HCC were retrospectively enrolled. The least absolute shrinkage and selection operator (LASSO) and stepwise logistic regression analyses were performed in training cohort to identify predictors and establish a model for predicting GPC-3 expression. The performance of model was evaluated by C-index, area under the receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA).
[RESULTS] Our study showed that heterogeneous arterial phase hyperenhancement (APHE), mosaic architecture, alpha-fetoprotein (AFP) ≥ 100 ng/mL, rim enhancement in arterial phase (AP), neutrophil count (Nc) level and age were independently associated with GPC-3 expression. The model demonstrated strong predictive performance and the areas under curve were 0.811 (95% confidence interval[CI]: 0.737-0.885) and 0.750 (95%CI: 0.622-0.878) in training and validation cohorts. Good calibration and prominent net benefit were exhibited in both cohorts. Moreover, The risk score calculated by the model presented promising stratification ability between high-risk and low-risk groups in terms of relapse-free survival (RFS) .
[CONCLUSION] Based on CEUS and clinical features, the model can help identify GPC-3 expression accurately and may provide valuable preoperative insights for customizing personalized treatment planning in HCC patients.
[METHODS] From August 2021 to July 2024, 197 patients with pathologically confirmed HCC were retrospectively enrolled. The least absolute shrinkage and selection operator (LASSO) and stepwise logistic regression analyses were performed in training cohort to identify predictors and establish a model for predicting GPC-3 expression. The performance of model was evaluated by C-index, area under the receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA).
[RESULTS] Our study showed that heterogeneous arterial phase hyperenhancement (APHE), mosaic architecture, alpha-fetoprotein (AFP) ≥ 100 ng/mL, rim enhancement in arterial phase (AP), neutrophil count (Nc) level and age were independently associated with GPC-3 expression. The model demonstrated strong predictive performance and the areas under curve were 0.811 (95% confidence interval[CI]: 0.737-0.885) and 0.750 (95%CI: 0.622-0.878) in training and validation cohorts. Good calibration and prominent net benefit were exhibited in both cohorts. Moreover, The risk score calculated by the model presented promising stratification ability between high-risk and low-risk groups in terms of relapse-free survival (RFS) .
[CONCLUSION] Based on CEUS and clinical features, the model can help identify GPC-3 expression accurately and may provide valuable preoperative insights for customizing personalized treatment planning in HCC patients.