A nomogram for determining the disease-specific survival in Ewing sarcoma: a population study

Jun Zhang†,
Zhenyu Pan†,
Jin Yang†,
Xiaoni Yan,
Yuanjie Li and
Jun LyuEmail author View ORCID ID profile

†Contributed equally

BMC Cancer201919:667

https://doi.org/10.1186/s12885-019-5893-9

Received: 4 December 2018
Accepted: 30 June 2019
Published: 5 July 2019

Open Peer Review reports

Abstract

Background

We aimed to develop and validate a nomogram for predicting the disease-specific survival of Ewing sarcoma (ES) patients.

Methods

The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify ES from 1990 to 2015, in which the data was extracted from 18 registries in the US. Multivariate analysis performed using Cox proportional hazards regression models was performed on the training set to identify independent prognostic factors and construct a nomogram for the prediction of the 3-, 5-, and 10-year survival rates of patients with ES. The predictive values were compared by using concordance indexes (C-indexes), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).

Results

A total of 2,643 patients were identified. After multivariate Cox regression, a nomogram was established based on a new model containing the predictive variables of age, race, extent of disease, tumor size, and therapy of surgery. The new model provided better C-indexes (0.684 and 0.704 in the training and validation cohorts, respectively) than the model without therapy of surgery (0.661 and 0.668 in the training and validation cohorts, respectively). The good discrimination and calibration of the nomogram were demonstrated for both the training and validation cohorts. NRI and IDI were also improved. Finally, DCA demonstrated that the nomogram was clinically useful.

Conclusion

We developed a reliable nomogram for determining the prognosis and treatment outcomes of patients with ES in the US. However, the proposed nomogram still requires external data verification in future applications, especially for regions outside the US.