Lung Metastasis Probability in Ewing Sarcoma: A Nomogram Based on the SEER Database.

Jie Wang,Yonggang Fan,Lei Xia

doi:10.3390/curroncol28010009

Jie Wang, Yonggang Fan + Show 1 more

Open Access

https://doi.org/10.3390/curroncol28010009

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Abstract

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.

Highlights

Ewing sarcoma (ES) is the second most common malignant primary osseous neoplasm, accounting for 8% of all cases in children and adolescents [1,2]
According to the inclusion and exclusion criteria, a total of 1157 ES patients, which were assigned to the training cohort (n = 812, for the construction and internal validation of the nomogram) or the validation cohort (n = 345, for the external validation of the nomogram), were identified
Most of the patients were below 20 years old, and the total proportion of patients with lung metastasis at

Summary

Introduction

Ewing sarcoma (ES) is the second most common malignant primary osseous neoplasm, accounting for 8% of all cases in children and adolescents [1,2]. Patients with lung metastasis alone have better survival than those with metastases at other sites, their mortality at 5 years is still approximately 60–70% [7,8,9,10]. 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. 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

Objectives

Methods

Results

Conclusion