Chen Jing, Wei Li, Deng Chun-Mei, Xiong Jing, Chen Song-Mei, Lu Ding, Li Zhi-Hong, Chen Yao, Xiao Jun, Chen Tian-Wu
The First Clinical College of Jinan University, Guangzhou 510630, Guangdong, China; Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu 610072, Sichuan, China.
Department of Radiology, Ganzi Hospital, West China Hospital of Sichuan University (Ganzi Tibetan Autonomous Prefecture People's Hospital), Ganzi 626000, Sichuan, China.
Eur J Radiol. 2025 Feb;183:111865. doi: 10.1016/j.ejrad.2024.111865. Epub 2024 Dec 1.
To develop a nomogram based on liver CT and clinical features to preoperatively predict lung metastasis (LM) secondary to hepatic alveolar echinococcosis (HAE).
A total of 291 consecutive HAE patients from Institution A undergoing preoperative abdominal contrast-enhanced CT and chest unenhanced CT were retrospectively reviewed, and were randomly divided into the training and internal validation sets at the 7:3 ratio. 82 consecutive patients from Institution B were enrolled as an external validation set. A nomogram was constructed based on the significant CT and clinical features of HAE from the training set selected by univariable and multivariable analyses to predict LM, and its predictive accuracy was assessed by area under the receiver operating characteristic curve (AUC) and Brier score. Decision-curve analysis was applied to evaluate the clinical effectiveness. This nomogram was verified in two independent validation sets.
Eosinophil (odds ratio [OR] = 9.60; 95 % confidence interval [CI]: 1.80-51.11), lesion size (OR = 1.02; 95 %CI: 1.01-1.04), and moderate-severe invasion of inferior vena cava (IVC) (OR = 5.57; 95 %CI: 1.82-17.10) were independently associated with LM (all P-values < 0.05). The nomogram based on the three independent predictors displayed AUCs of 0.875 (95 %CI, 0.824-0.927), 0.872 (95 %CI, 0.787-0.957) and 0.836 (95 %CI, 0.729-0.943), and Brier score of 0.105, 0.1 and 0.118 in the training, internal validation and external validation sets, respectively. Decision-curve analysis showed good clinical utility.
A nomogram based on eosinophil, lesion size and moderate-severe invasion of IVC showed good ability and accuracy for preoperative prediction of LM due to HAE.
基于肝脏CT和临床特征开发一种列线图，用于术前预测肝泡型包虫病（HAE）继发肺转移（LM）。
回顾性分析机构A连续收治的291例接受术前腹部增强CT和胸部平扫CT的HAE患者，并按7:3的比例随机分为训练集和内部验证集。将机构B连续收治的82例患者纳入外部验证集。基于单变量和多变量分析从训练集中筛选出的HAE的显著CT和临床特征构建列线图以预测LM，并通过受试者操作特征曲线（AUC）下面积和Brier评分评估其预测准确性。应用决策曲线分析评估临床有效性。该列线图在两个独立验证集中进行了验证。
嗜酸性粒细胞（比值比[OR]=9.60；95%置信区间[CI]：1.80 - 51.11）、病灶大小（OR = 1.02；95%CI：1.01 - 1.04）和下腔静脉（IVC）中重度侵犯（OR = 5.57；95%CI：1.82 - 17.10）与LM独立相关（所有P值<0.05）。基于这三个独立预测因素的列线图在训练集、内部验证集和外部验证集中的AUC分别为0.875（95%CI，0.824 - 0.927）、0.872（95%CI，0.787 - 0.957）和0.836（95%CI，0.729 - 0.943），Brier评分分别为0.105、0.1和0.118。决策曲线分析显示出良好的临床实用性。
基于嗜酸性粒细胞、病灶大小和IVC中重度侵犯的列线图在术前预测HAE所致LM方面显示出良好的能力和准确性。
