State of the Art: Machine Learning Applications in Glioma Imaging.
AJR Am J Roentgenol. 2019 Jan;212(1):26-37. doi: 10.2214/AJR.18.20218. Epub 2018 Oct 17.
Lotan E1, Jain R1, Razavian N1,2, Fatterpekar GM1, Lui YW1.
Erratum in Corrections. [AJR Am J Roentgenol. 2019]
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.