Department of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
Curr Oncol. 2020 Dec 5;28(1):69-77. doi: 10.3390/curroncol28010009.
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.
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.
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 ( > 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.
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.
目前，尚缺乏用于预测初诊尤文肉瘤（ES）患者发生肺转移概率的准确列线图。本研究旨在构建并验证用于预测 ES 患者发生肺转移概率的列线图。
本研究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 例 ES 患者的数据。采用最小绝对值收缩和选择算子（LASSO）和多因素 logistic 回归分析确定肺转移的预测因素。通过受试者工作特征（ROC）曲线和校准曲线验证列线图的区分度和校准度。采用决策曲线分析（DCA）评估预测模型的临床实用性和净获益。
年龄、肿瘤大小、原发部位、肿瘤扩散程度和远处转移等因素被确定为列线图的最终预测因素。训练集和验证集的校准曲线均显示出较好的一致性，Hosmer-Lemeshow 检验表明模型拟合良好（>0.05）。此外，训练集和验证集的 ROC 曲线下面积（AUC）分别为 0.732（95%置信区间：0.607-0.808）和 0.741（95%置信区间：0.602-0.856），表明具有良好的预测区分度。DCA 显示，当预测转移概率在 1%至 90%之间时，该列线图可提供临床实用性和净获益。
本研究构建和验证的列线图可为临床医生提供一种便捷有效的工具，可提高初诊 ES 患者肺转移概率的预测能力。
