Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques Academic Article uri icon

Overview

MeSH Major

  • Brain Neoplasms
  • Glioblastoma
  • Image Interpretation, Computer-Assisted

abstract

  • By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients.

publication date

  • March 2016

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC4767233

Digital Object Identifier (DOI)

  • 10.1093/neuonc/nov127

PubMed ID

  • 26188015

Additional Document Info

start page

  • 417

end page

  • 25

volume

  • 18

number

  • 3