State of the art: Machine learning applications in Glioma Imaging

Eyal Lotan, Rajan Jain, Narges Razavian, Girish M. Fatterpekar, Yvonne W. Lui*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


OBJECTIVE. Machine learning has recently gained considerable attention because of promising results for a wide range of radiology applications. Here we review recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas. CONCLUSION. We discuss available resources, state-of-the-art segmentation methods, and machine learning radiomics for glioma. We highlight the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.

Original languageEnglish
Pages (from-to)26-37
Number of pages12
JournalAmerican Journal of Roentgenology
Issue number1
StatePublished - Jan 2019
Externally publishedYes


  • Brain lesion segmentation
  • Deep learning
  • Glioma
  • Machine learning
  • Radiomics


Dive into the research topics of 'State of the art: Machine learning applications in Glioma Imaging'. Together they form a unique fingerprint.

Cite this