Application of Machine Learning Techniques for Survival Prediction in Pediatric Malignant Non-Seminomatous Germ Cell Testicular Tumors: A SEER Database Study

Abstract

Background Childhood testicular cancers constitute 1-2% of all childhood tumors. According to the Surveillance, Epidemiology, and End Results (SEER) database, based on data from 2013 to 2019, the 5-year survival rate is 95.2%. The second most common type of testicular tumor is malignant non-seminomatous germ cell tumor. In recent years, various statistical techniques and extensive databases have been used to obtain information on disease prognosis and survival. In this study, we aimed to develop software using artificial intelligence and machine learning techniques to accurately predict the overall survival of patients with malignant nonseminomatous germ cell testicular tumors. Methods Our study included data from 788 patients aged 0-18 diagnosed with malignant nonseminomatous germ cell testicular cancer between January 1975 and December 2019. The main hypothesis of the study was to provide overall survival (OS) in years from the date of diagnosis to the date of death or the last follow-up date for surviving patients. In addition to survival analysis, we also analyzed patient age at diagnosis, race, laterality, year of diagnosis, tumor histological type, T stage, N stage, M stage, tumor size, mortality, and follow-up duration. Results The OS was found to be 41.29±0.43 years. The median survival time was 43.21±0.62 years for patients < 15 and 40.34±0.52 years for patients aged ≥15. We developed software that enabled the provision of patient-specific survival in addition to OS for all patients. Conclusion Recently, artificial intelligence techniques, such as machine learning, have shown remarkable advancements compared to other statistical methods. We believe that the use of artificial intelligence will not only provide faster and easier information for clinicians in the diagnosis and prognosis of cancers but also for all diseases in general.

Ähnliche Arbeiten