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. 2019 Feb 12:15:100223.
doi: 10.1016/j.jbo.2019.100223. eCollection 2019 Apr.

A nomogram to predict prognosis in Ewing sarcoma of bone

Qiang Zhou  1 , Zong-Yi Wu  2 , Zhong-Qin Lin  1
Affiliations

A nomogram to predict prognosis in Ewing sarcoma of bone

Qiang Zhou et al. J Bone Oncol. .

Abstract

Objective: This study was designed to develop a nomogram for assessing the survival of patients with Ewing sarcoma (ES).

Methods: Data from patients diagnosed with ES between 2004 and 2013 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Based on patient registration, the primary cohort was divided into a training set (n = 479, data from 17 cancer registries) and a validation set (n = 137, data from 1 cancer registry). Then, the prognostic effects of variables were analyzed using Kaplan-Meier method and Cox proportional hazard model. Moreover, nomograms were established for estimating 3- and 5-year overall survival (OS) and cancer-special survival (CSS) based on Cox regression model. Last, nomogram was validated by training set and validation set.

Results: According to the multivariate analysis of training set, nomogram which combined age, race, stage, tumor site, tumor size and chemotherapy was identified. The internal bootstrap resampling approach suggested the nomogram had sufficient discriminatory power with the C-index of OS: 0.754 (95% CI, 0.705-0.802) and CSS: 0.759 (95% CI, 0.700-0.800). The calibration plots also demonstrated good consistence between the prediction and the observation.

Conclusion: Our nomogram is a reliable and powerful tool for distinguishing and predicting the survival of ES patients, thus helping to better select medical examinations and optimize treatment options in collaboration with medical oncologists and surgeons.

Keywords: Ewing sarcoma; Nomogram; SEER.

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Figures

Fig. 1.
Fig. 1
(A-F) The graphs show defining the optimal cutoff values of age and tumor size via X-tile analysis. (A, D) The black dot indicates that optimal cutoff values of age and tumor size have been identified. (B, E) A histogram and (C, F) Kaplan–Meier were constructed based on the identified cutoff values. Optimal cutoff values of age and tumor size were identified as 13 years and 28 years, 71 mm and 115 mm based on survival, respectively.
Fig. 2.
Fig. 2
Nomograms for predicting the 3-, and 5-year overall survival (A) and cancer-specific (B) survival of ES patients. Description using nomograms: First, each feature point of the patient is assigned by plotting a vertical line to a point scale from the variable. Then, sum all the points and draw a vertical line from the total point scale to the liver metastasis axis to obtain the probability.
Fig. 3.
Fig. 3
(A–H) The graphs show the calibration plots for internal validation of (A) actual 3-year cancer special survival and (B) 3-year overall survival; (C) actual 5-year cancer special survival and (D) actual 5-year overall survival; and external validation of (E) actual 3-year cancer special survival and (F) 3-year overall survival; and (G) actual 5-year cancer special survival and (H) 5-year overall survival. The 45-degree line represents an ideal match between the actual survival (Y-axis) and nomogram-predicted survival (X-axis). The perpendicular line means 95% confidence intervals. Closer distances from the points to the dashed line indicate higher prediction accuracy.
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