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

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.

A nomogram based on a patient-reported outcomes measure: predicting the risk of readmission for patients with chronic heart failure

“…Many studies show that HRQOL is independent predictive factors of prognosis for inpatients with HF and that they have important predictive value [12,13] and nomogram based on Cox regression has been widely used to predict the survival time of chronic diseases, especially cancer [30][31][32]. However, as far as we know, there have been no studies based on it to construct the quantitative model to predict the probability of readmission for patients with CHF.…”

High Specificity of BCL11B and GLG1 for EWSR1-FLI1 and EWSR1-ERG Positive Ewing Sarcoma

“…In more than 30% of cases, micrometastases are present at time of diagnosis [6]. For these patients, outcome is rather poor with a long-term survival rate of around 25% [6][7][8], with a slightly better survival rate for patients exhibiting exclusively lung metastases [9,10]. Nevertheless, patients with localized (i.e., non-metastatic) disease can be effectively treated with combined chemotherapy and surgery [11][12][13], resulting in a long-term survival rate of more than 70% [14][15][16].…”

How to Identify White Coat Hypertension at The First Diagnosis: Establishment of A Scoring Model for The Differential Diagnosis of White Coat Hypertension and Sustained Hypertension

“…The AUC of the model applied to the training set and the veri cation set are 0.705 and 0.718 respectively, which is signi cantly improved compared with the ability of univariate WCH identi cation. Moreover, the AUC values of the training set and the veri cation set are relatively close, which indicates that the model is relatively robust [36]. Further calibration test results show that the model has good stability in both the training set and veri cation set.…”