Xiao Si-Yu, Shi Yu-Ting, Xu Jian-Xia, Sun Ji-Hong, Yu Ri-Sheng
Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Eur J Radiol. 2025 Jan;182:111822. doi: 10.1016/j.ejrad.2024.111822. Epub 2024 Nov 8.
To establish a classification system which differentiates cystic intraductal papillary neoplasm of the bile duct (cystic IPNB) from hepatic mucinous cystic tumors (MCN) based on their radiological difference.
A total of 75 patients pathologically diagnosed as MCN and IPNB in two major hospitals from 2015 to 2024 were enrolled. Radiological features were recorded and compared between these two tumors. Variables with significant differences were included in multivariate logistic regression (LR) analysis. A decision model was built and simplified based on importance ranking of variables. K-nearest-neighbor (KNN) model was introduced to learn distribution of individuals in main dimensions based on multiple correspondence analysis (MCA) and predicted diagnosis. The diagnostic efficacy of the classification system and the KNN model was compared.
Significant differences existed in Dmax-IVC angle, septation, mural nodule, upstream and downstream biliary dilatation, communication with bile duct between MCN and cystic IPNB. Downstream biliary dilatation and communication with bile duct were highly specific for IPNB (specificity, 97.9 % and 100 %, respectively), which could independently diagnose IPNB. Among four significant indicators in LR analysis, upstream biliary dilatation and Dmax-IVC angle were used for a simplified decision model to attain good applicability. The KNN model based on MCA data achieved highest accuracy (0.910) when K = 11. Overall, the classification system achieved an AUC of 0.882 (0.95CI: 0.797-0.966), compared with 0.911 (0.95CI: 0.818-1.000) in the KNN model, which demonstrated no significant difference (p = 0.655) in differential performance.
The classification system combining four important indicators had equivalent performance to KNN model in discrimination, which was simple and applicable for clinical practice, and also accessible on unenhanced examinations.
基于胆管内乳头状黏液性肿瘤（cystic IPNB）与肝黏液性囊性肿瘤（MCN）的影像学差异，建立一种将二者区分开来的分类系统。
纳入2015年至2024年在两家大型医院中病理诊断为MCN和IPNB的75例患者。记录并比较这两种肿瘤的影像学特征。将具有显著差异的变量纳入多因素逻辑回归（LR）分析。基于变量的重要性排序构建并简化决策模型。引入K近邻（KNN）模型，基于多重对应分析（MCA）学习个体在主要维度上的分布并预测诊断。比较分类系统和KNN模型的诊断效能。
MCN与cystic IPNB在Dmax-IVC角、分隔、壁结节、胆管上游和下游扩张、与胆管相通等方面存在显著差异。胆管下游扩张和与胆管相通对IPNB具有高度特异性（特异性分别为97.9%和100%），可独立诊断IPNB。在LR分析的四个显著指标中，胆管上游扩张和Dmax-IVC角用于简化决策模型以获得良好的适用性。基于MCA数据的KNN模型在K = 11时达到最高准确率（0.910）。总体而言，分类系统的AUC为0.882（0.95CI：0.797 - 0.966），KNN模型为0.911（0.95CI：0.818 - 1.000），二者在鉴别性能上无显著差异（p = 0.655）。
结合四个重要指标的分类系统在鉴别方面与KNN模型具有同等性能，简单且适用于临床实践，在未增强检查中也可应用。
