Using machine learning techniques predicts prognosis of patients with Ewing sarcoma

Abstract

Ewing sarcoma is one of the most common types of malignant bone tumor in children and adolescents. However, to our limited knowledge, no study exists that uses machine learning to create algorithms for the prediction of survivorship for Ewing sarcoma. About 2332 patients with Ewing sarcoma between 1975 and 2016 in the United States were identified from Surveillance, Epidemiology, and End Results (SEER) program. All patients in the data set were randomly assigned into the training set and the testing set, at a 2:8 ratio. In the training set, boosted decision tree, support vector machine, nonparametric random forest method, and neural network models were developed to predict the 5-year survivorship. The overall survival rate in 5-year follow-up of this patient cohort is 60.72%. With respect to the algorithms for both cancer specific survival and overall survival, there was slight superiority in our performance metrics favoring the random forest method over the other models for survival prediction, with 77/83% sensitivity and 91/94% specificity, respectively. The random forest method was incorporated into a freely available web-based application. This application can be accessed through https://zryan.shinyapps.io/EwingSarcoma/. Clinical Significance: To the best of our knowledge, this is the first available predictive model for predicting survival in Ewing sarcoma based on machine-learning algorithms. This study may provide orthopedic surgeons with an easily accessible prediction tool when dealing with patients suffering from Ewing sarcoma.