GLIMPSE: a glioblastoma prognostication model using ensemble learning-a surveillance, epidemiology, and end results study.

ABSTRACT:

Purpose

Glioblastoma is one of the most common and aggressive brain tumors in the world with a poor prognosis. A glioblastoma prognostication model has the potential to improve the cancer's standard of care. No other paper has looked at using ensemble learning with a population database to predict multiple binary glioblastoma survival outcomes.

Methods

We utilized ensemble learning to design, build, and test a prognostication system for glioblastoma for short-, intermediate- and long-term survival, based on various clinical features. We used the population database SEER which covers 17 different registries. The most important prognostic features were identified and used as a clinical feature set. The statistical feature set was determined using Random Forests. The accuracy,

SUBMITTER: Samara KA 

PROVIDER: S-EPMC7803850 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature