基于CT影像学特征的列线图模型可有效预测肝细胞癌肿瘤包绕型血管OA

Objective To develop a nomogram model for predicting vessels encapsulating tumor clusters(VETC)-positive hepatocellular carcinoma(HCC)based on preoperative contrast-enhanced CT imaging features combined with clinical characteristics.Methods Clinical and radiological data of 174 patients diagnosed with HCC by pathology admitted to Nanfang Hospital,Southern Medical University from October 2019 to April 2022,were retrospectively collected.The patients were randomly divided into a training cohort(n=121)and a validation cohort(n=53)in a 7:3 ratio.Based on CD34 immunostaining results,the percentage of tumor area occupied by the VETC pattern(VETC index)was calculated,and patients were classified into the VETC-positive group(≥55%)and the VETC-negative group(<55%)using this threshold.Independent predictive factors significantly associated with VETC-positive HCC were identified using univariate and multivariate logistic regression analyses,and a nomogram prediction model was constructed.The diagnostic performance of the model was evaluated using the ROC curve,and its calibration was assessed by calibration curves.Results Non-rim arterial phase hyperenhancement,enhancing capsule,intratumoral artery,and arterial peritumoral enhancement were identified as independent predictive factors for VETC-positive HCC(P<0.05).The nomogram model established by these factors demonstrated good performance,with the AUC of 0.815 in the training cohort and 0.760 in the validation cohort.The predicted probabilities from the nomogram model demonstrated high consistency with the actual incidence rates.Conclusion The nomogram model based on preoperative contrast-enhanced CT imaging features exhibits favorable predictive performance for VETC-positive HCC,which may facilitate the identification of high-risk individuals with HCC and inform clinical diagnosis and treatment decisions.