An Artificial Intelligence Model for Predicting the 5-Year Survival Status of Osteosarcoma Patients Based on the SEER Database and XGBoost Algorithm

Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database (2004-2014) and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic and decision curve analyses were performed to evaluate the prediction model. Age, primary tumor site, histological grade, extension stage, tumor size, local lymphatic metastasis, distant metastasis, radiation, chemotherapy and surgery were selected as the characteristics to construct the XGBoost model. The accuracy of the prediction model was excellent both in the training set (AUC = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. Two other representative artificial intelligence models (support vector machine and Bayesian network) were also tested and proved inferior to the XGBoost model. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings. Funding Statement: This work was supported in part by the National Nature Science Fund of China (81702143, 81772387 and 81472064); the Public Projects of Zhejiang Province (LGF19H060013) and the Natural Science Foundation of Zhejiang Province of China (LQ16C110001). Declaration of Interests: The authors declare that they have no conflicts of interest. Ethical Approval Statement: We obtained permission to access the files of SEER database. The personal identifying information was not involved in this study so that the informed consent was not required. This study was reviewed and approved by the Medical Ethic Committee of Sir Run Run Shaw hospital affiliated to Medical College of Zhejiang University.