目的:探讨评估临床指标和多期动态增强CT (Contrast-enhanced computed tomography, CECT)的影像学特征,并构建回归模型预测术前肝细胞癌(Hepatocellular carcinoma, HCC)微血管侵犯(Microvascular invasion, MVI)状态。方法:回顾性研究...目的:探讨评估临床指标和多期动态增强CT (Contrast-enhanced computed tomography, CECT)的影像学特征,并构建回归模型预测术前肝细胞癌(Hepatocellular carcinoma, HCC)微血管侵犯(Microvascular invasion, MVI)状态。方法:回顾性研究141例HCC患者的临床、影像学和病理资料。根据是否存在微血管侵犯,分为MVI阳性组77例,MVI阴性组64例。用单因素和多因素Logistic回归分析筛选MVI的独立危险因素,构建回归模型预测MVI,使用Area under the curve (AUC值)、特异度和灵敏度评估模型的预测效能。结果:最终筛选出MVI的临床和影像学独立危险因素为甲胎蛋白(Alpha-fetoprotein, AFP) ≥ 400 ng/ml、瘤周低密度环和肝外生长。结合这三个因素构建的模型ROC曲线下面积(Area under the curve, AUC)值为0.730,特异度为0.625,灵敏度为0.727。结论:由AFP联合影像学特征(瘤周低密度环和生长方式)建立的回归模型可以在一定程度上预测术前MVI状态,有助于临床优化治疗策略。Objective: To investigate the role of clinical indicators and imaging characteristics from multiphase contrast-enhanced computed tomography (CECT) in predicting preoperative microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and to construct a predictive regression model for microvascular invasion (MVI) status. Methods: Clinical, imaging, and pathological data of 141 patients with HCC were studied retrospectively. Based on the presence or absence of microvascular invasion, patients were divided into MVI-positive group (n = 77) and MVI-negative group (n = 64). Independent risk factors for MVI were screened using univariate and multivariate Logistic regression analyses, and a regression model was constructed to predict MVI, and the predictive efficacy of the model was assessed using the Area under the curve (AUC value), specificity and sensitivity. Results: Independent risk factors for MVI identified from both clinical and imaging data included AFP ≥ 400 ng/ml, peritumoral hypodense halo, and extrahepatic growth. The area under the ROC curve (AUC) value of the model combining these three factors was 0.730, the specificity was 0.625 and the sensitivity was 0.727. Conclusion: The regression model incorporating AFP levels and key imaging features (peritumoral hypodense halo and extrahepatic growth) demonstrates moderate predictive value for preoperative MVI status, which may assist in optimizing clinical treatment strategies for patients with HCC.展开更多
文摘目的:探讨评估临床指标和多期动态增强CT (Contrast-enhanced computed tomography, CECT)的影像学特征,并构建回归模型预测术前肝细胞癌(Hepatocellular carcinoma, HCC)微血管侵犯(Microvascular invasion, MVI)状态。方法:回顾性研究141例HCC患者的临床、影像学和病理资料。根据是否存在微血管侵犯,分为MVI阳性组77例,MVI阴性组64例。用单因素和多因素Logistic回归分析筛选MVI的独立危险因素,构建回归模型预测MVI,使用Area under the curve (AUC值)、特异度和灵敏度评估模型的预测效能。结果:最终筛选出MVI的临床和影像学独立危险因素为甲胎蛋白(Alpha-fetoprotein, AFP) ≥ 400 ng/ml、瘤周低密度环和肝外生长。结合这三个因素构建的模型ROC曲线下面积(Area under the curve, AUC)值为0.730,特异度为0.625,灵敏度为0.727。结论:由AFP联合影像学特征(瘤周低密度环和生长方式)建立的回归模型可以在一定程度上预测术前MVI状态,有助于临床优化治疗策略。Objective: To investigate the role of clinical indicators and imaging characteristics from multiphase contrast-enhanced computed tomography (CECT) in predicting preoperative microvascular invasion (MVI) in hepatocellular carcinoma (HCC), and to construct a predictive regression model for microvascular invasion (MVI) status. Methods: Clinical, imaging, and pathological data of 141 patients with HCC were studied retrospectively. Based on the presence or absence of microvascular invasion, patients were divided into MVI-positive group (n = 77) and MVI-negative group (n = 64). Independent risk factors for MVI were screened using univariate and multivariate Logistic regression analyses, and a regression model was constructed to predict MVI, and the predictive efficacy of the model was assessed using the Area under the curve (AUC value), specificity and sensitivity. Results: Independent risk factors for MVI identified from both clinical and imaging data included AFP ≥ 400 ng/ml, peritumoral hypodense halo, and extrahepatic growth. The area under the ROC curve (AUC) value of the model combining these three factors was 0.730, the specificity was 0.625 and the sensitivity was 0.727. Conclusion: The regression model incorporating AFP levels and key imaging features (peritumoral hypodense halo and extrahepatic growth) demonstrates moderate predictive value for preoperative MVI status, which may assist in optimizing clinical treatment strategies for patients with HCC.
