ArticleCurrent oncology (Toronto, Ont.)2020
Lung Metastasis Probability in Ewing Sarcoma: A Nomogram Based on the SEER Database.
Article in Current oncology (Toronto, Ont.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A novel tool to predict the probability of pulmonary metastasis in patients with primary osseous sacral/pelvic tumors: a large real-world multicenter study.BMC pulmonary medicine · 2025Article
- The development and external validation of a web-based nomogram for predicting overall survival with Ewing sarcoma in children.Journal of children's orthopaedics · 2024Article
- Overall survival comparison between pediatric and adult Ewing sarcoma of bone and adult nomogram construction: a large population-based analysis.Frontiers in pediatrics · 2023Article
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3 authors.
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Abstract
backgroundUp to now, an accurate nomogram to predict the lung metastasis probability in Ewing sarcoma (ES) at initial diagnosis is lacking. Our objective was to construct and validate a nomogram for the prediction of lung metastasis in ES patients.
methodsA total of 1157 patients with ES from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. The predictors of lung metastasis were identified via the least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis. The discrimination and calibration of the nomogram were validated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical usefulness and net benefits of the prediction model.
resultsFactors including age, tumor size, primary site, tumor extension, and other site metastasis were identified as the ultimate predictors for the nomogram. The calibration curves for the training and validation cohorts both revealed good agreement, and the Hosmer-Lemeshow test identified that the model was well fitted (
conclusionThe nomogram constructed and validated by us could provide a convenient and effective tool for clinicians that can improve prediction of the probability of lung metastasis in patients with ES at initial diagnosis.
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