Background. Up 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. Methods. A 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. Results. Factors 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 (p > 0.05). In addition, the area under the ROC curve (AUC) values in the training and validation cohorts were 0.732 (95% confidence interval, CI: 0.607–0.808) and 0.741 (95% CI: 0.602–0.856), respectively, indicating good predictive discrimination. The DCA showed that when the predictive metastasis probability was between 1% and 90%, the nomogram could provide clinical usefulness and net benefit. Conclusion. The 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.

中文翻译：

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尤文肉瘤的肺转移概率：基于 SEER 数据库的列线图

背景。到目前为止，缺乏准确的列线图来预测尤文肉瘤 (ES) 在初始诊断时的肺转移概率。我们的目标是构建和验证用于预测 ES 患者肺转移的列线图。方法。回顾性收集了来自监测、流行病学和最终结果 (SEER) 数据库的 1157 名 ES 患者。通过最小绝对收缩和选择算子（LASSO）和多变量逻辑分析确定肺转移的预测因子。诺模图的鉴别和校准通过受试者工作特征（ROC）曲线和校准曲线进行验证。决策曲线分析 (DCA) 用于评估预测模型的临床实用性和净收益。结果。因素包括年龄、肿瘤大小、原发部位、肿瘤扩展和其他部位转移被确定为列线图的最终预测因子。训练组和验证组的校准曲线都显示出良好的一致性，Hosmer-Lemeshow 检验表明模型拟合良好（p > 0.05）。此外，训练组和验证组的 ROC 曲线下面积 (AUC) 值分别为 0.732（95% 置信区间，CI：0.607–0.808）和 0.741（95% CI：0.602–0.856），表明具有良好的预测性歧视。DCA 表明，当预测转移概率在 1% 到 90% 之间时，列线图可以提供临床有用性和净收益。结论。