The Construction and Validation of a new Predictive Model for Overall Survival of Clear Cell Renal Cell Carcinoma Patients with Bone Metastasis Based on Machine Learning Algorithm

Abstract

Background This study aimed to develop and validate predictive models based on machine learning (ML) algorithms for patients with bone metastases (BM) from clear cell renal cell carcinoma (ccRCC) and to identify appropriate models for clinical decision-making. Methods In this retrospective study, we obtained information on ccRCC patients diagnosed with bone metastasis (ccRCC-BM), from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 ( n = 1490), and collected clinicopathological information on ccRCC-BM patients at our hospital ( n = 42). We then applied four ML algorithms: extreme gradient boosting (XGB), logistic regression (LR), random forest (RF), and Naive Bayes model (NB), to develop models for predicting the overall survival (OS) of patients with bone metastasis from ccRCC. In the SEER dataset, 70% of the patients were randomly divided into training cohorts and the remaining 30% were used as validation cohorts. Data from our center were used as an external validation cohort. Finally, we evaluated the model performance using receiver operating characteristic curves (ROC), area under the ROC curve (AUC), accuracy, specificity, and F1-scores. Results The mean survival times of patients in the SEER and Chinese cohort were 21.8 months and 37.0 months, respectively. Age, marital status, grade, T stage, N stage, tumor size, brain metastasis, liver metastasis, lung metastasis, and surgery were included in the ML model. We observed that all four ML algorithms performed well in predicting the 1-year and 3-year OS of patients with ccRCC-BM. Conclusion ML is useful in predicting the survival of patients with ccRCC-BM, and ML models can play a positive role in clinical applications.