医学
阿卡克信息准则
逐步回归
肝细胞癌
高强度
磁共振成像
逻辑回归
内科学
优势比
胃肠病学
放射科
统计
数学
作者
Jianwei Chen,Huizhen Chen,Dechun Zheng,Chuan Yan,Rongping Ye,Liting Wen,Yueming Li
标识
DOI:10.1177/02841851231203830
摘要
Background Hepatic lesions categorized as LR-3, LR-4, and LR-M are challenging to accurately assess and diagnose. Purpose To combine potential clinical and/or magnetic resonance imaging (MRI) features for a more comprehensive hepatocellular carcinoma (HCC) versus non-HCC diagnosis for patients with LR-3, LR-4, and LR-M graded lesions. Methods Data were consecutively retrieved from 82 at-risk patients with LR-3 (n = 43), LR-4 (n = 20), and LR-M (n = 23) lesions. Significant findings for the differentiation of HCC and non-HCC, including MRI features and clinical factors, were identified with univariable and multivariable analyses. The variables for a prediction model were selected through stepwise use of Akaike's Information Criterion (AIC) to build multivariable logistic regression model. Results Serum alpha-fetoprotein (AFP) >16.2 ng/mL (odds ratio [OR] = 22.4; P = 0.006), septum (OR = 52.1; P = 0.011), and hepatobiliary phase (HBP) hypointensity (OR = 40.2; P = 0.001) were confirmed as independent predictors of HCC. When combining the three predictors and mild-moderate T2 hyperintensity, the model (AIC = 50.91) showed good accuracy with a C-index of 0.948. Conclusion In at-risk patients with LR-3, LR-4, or LR-M lesions, integrating AFP, septum, HBP hypointensity, and mild-moderate T2 hyperintensity achieved high diagnostic performance for the diagnosis of HCC.
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