列线图
医学
肝细胞癌
接收机工作特性
内科学
肝硬化
曲线下面积
逻辑回归
胃肠病学
凝血酶原时间
肿瘤科
单变量分析
乙型肝炎
多元分析
作者
Yu Zhou,Dongmei Chen,Yansong Zheng,Xuedan Wang,Shuna Huang,Tong Lin,Yihan Lin,Yanfang Zhang,Yinde Huang,Qishui Ou,Jinlan Huang
标识
DOI:10.1007/s00432-023-04997-4
摘要
AFP appears to be negative in about 30% of overall hepatocellular carcinoma (HCC). Our study aimed to develop a nomogram model to diagnose AFP-negative HCC (AFPN-HCC).The training set included 294 AFPN-HCC patients, 159 healthy objects, 63 patients with chronic hepatitis B(CHB), and 64 patients with liver cirrhosis (LC). And the validation set enrolled 137 healthy controls objects, 47 CHB patients and 45 patients with LC. LASSO, univariate, and multivariable logistic regression analysis were performed to construct the model and then transformed into a visualized nomogram. The receiver operating characteristic (ROC) curves, the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were further used for validation.Four variables including age, PIVKA-II, platelet (PLT) counts, and prothrombin time (PT) were selected to establish the nomogram. The area under the curve (AUC) of the ROC to distinguish AFPN-HCC patients was 0.937(95% CI 0.892-0.938) in training set and 0.942(95% CI 0.921-0.963) in validation set. We also found that the model had high diagnostic value for small-size HCC (tumor size < 5 cm) (AUC = 0.886) and HBV surface antigen-positive AFPN-HCC (AUC = 0.883).Our model was effective for discrimination of AFPN-HCC from patients with benign liver diseases and healthy controls, and might be helpful for the diagnosis for AFPN-HCC.
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