Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database.

ABSTRACT: Meningiomas are known to have relatively lower aggressiveness and better outcomes than other central nervous system (CNS) tumors. However, there is considerable overlap between clinical and radiological features characterizing benign, atypical, and malignant tumors. In this study, we developed methods and a practical app designed to assist with the diagnosis and prognosis of meningiomas. Statistical learning models were trained and validated on 62,844 patients from the Surveillance, Epidemiology, and End Results database. We used balanced logistic regression-random forest ensemble classifiers and proportional hazards models to learn multivariate patterns of association between malignancy, survival, and a series of basic clinical variables-such as tumor size, location, and surgical procedur

SUBMITTER: Moreau JT 

PROVIDER: S-EPMC6992687 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature