Abstract
Hepatic alveolar echinococcosis (HAE) is a rare zoonotic parasitic disease that closely resembles malignant tumors in both behavior and appearance. It can cause infiltration of affected organs and chronic liver damage. In advanced stages, it may metastasize or invade surrounding organs, resembling liver cancer, and is clinically referred to as "parasitic cancer." However, the prognosis of HAE with pulmonary metastasis is poor, and no reliable method currently exists to predict lung metastasis. This study aims to investigate the efficacy of a nomogram model, based on CT and MRI imaging features in conjunction with clinical indicators, for predicting pulmonary metastasis in HAE. A retrospective analysis was conducted using imaging and clinical data from 297 patients diagnosed with HAE. Univariate and multivariate logistic regression analyses identified independent factors associated with pulmonary metastasis, including lesion size, the presence of metastasis to other organs, cavitary lesions, and enhancement characteristics. The nomogram, developed using these variables, demonstrated strong predictive performance in both the training and validation cohorts. This model provides an effective tool for predicting the risk of pulmonary metastasis, offering early insights into disease progression and assisting clinicians in formulating personalized treatment and prognostic plans.