Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction.

Journal: European radiology
Published Date: Jul 1, 2020

Abstract

BACKGROUND AND PURPOSE: Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status.

Authors

  • Yoon Seong Choi
    Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.
  • Sung Soo Ahn
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea. sungsoo@yuhs.ac.
  • Jong Hee Chang
    Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea.
  • Seok-Gu Kang
    Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea.
  • Eui Hyun Kim
    Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea.
  • Se Hoon Kim
    Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea.
  • Rajan Jain
    1 Department of Radiology, New York University Langone Medical Center, 660 1st Ave, Rm 336, New York, NY 10016.
  • Seung-Koo Lee
    Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.

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