Purpose: To develop a nomogram based on liver CT and clinical features to preoperatively predict lung metastasis (LM) secondary to hepatic alveolar echinococcosis (HAE).
Methods: 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.
Results: 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.
Conclusion: 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.
Keywords: Computed tomography; Eosinophil; Hepatic alveolar echinococcosis; Lung metastasis; Nomogram.
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