Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

ABSTRACT:

Background

MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB).

Methods

One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival an

SUBMITTER: Macyszyn L 

PROVIDER: S-EPMC4767233 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature