부산대학교 도서관

Background Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learning studies in the chordoma literature. The purpose of this study was to develop machine learning models for survival prediction and deploy them as open access web applications as a proof of concept for machine learning in rare nervous system lesions. Methods The National Cancer Institute's Surveillance, Epidemiology, and End Results program database was used to identify adult patients diagnosed with spinal chordoma between 1995 and 2010. Four machine learning models were used to predict 5-year survival for spinal chordoma and assessed by discrimination, calibration, and overall performance. Results The 5-year overall survival for 265 patients with spinal chordoma was 67.5%. Variables used for prediction were age at diagnosis, tumor size, tumor location, extent of tumor invasion, and extent of surgery. For 5-year survival prediction, the Bayes Point Machine achieved the best performance with a c statistic of 0.80, calibration slope of 1.01, calibration intercept of 0.03, and Brier score of 0.16. This model for 5-year mortality prediction was incorporated into an open access application and can be found online (https://sorg-apps.shinyapps.io/chordoma/). Conclusions This analysis of patients with spinal chordoma demonstrated that machine learning models can be developed for survival prediction in rare pathologies and have the potential to serve as the basis for creation of decision support tools in the future. Highlights • Bayes Point Machine shows good performance for prediction of 5-year survival in spinopelvic chordoma. • Machine learning models have the potential to serve as decision support tools in the future. • Machine learning models can be developed in rare pathologies. [ABSTRACT FROM AUTHOR]