Fully automated hybrid approach to predict the IDH mutation status of gliomas via deep learning and radiomics.

Journal: Neuro-oncology
Published Date:

Abstract

BACKGROUND: Glioma prognosis depends on isocitrate dehydrogenase (IDH) mutation status. We aimed to predict the IDH status of gliomas from preoperative MR images using a fully automated hybrid approach with convolutional neural networks (CNNs) and radiomics.

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.
  • Sohi Bae
    Department of Radiology, National Health Insurance Service Ilsan Hospital, Goyang-si, Gyeonggi-do, Korea.
  • 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.
  • Se Hoon Kim
    Department of Pathology, Yonsei University College of Medicine, Seoul, South Korea.
  • Jinna Kim
    Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Tyler Hyungtaek Rim
    Department of Ocular Epidemiology, Singapore Eye Research Institute, Singapore, Singapore.
  • Seung Hong Choi
    From the Graduate School of Medical Science and Engineering (K.H.K., S.H.P.) and Department of Bio and Brain Engineering (S.H.P.), Korea Advanced Institute of Science and Technology, Room 1002, CMS (E16) Building, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea; Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (S.H.C.); Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.H.C.); and Center for Nanoparticle Research, Institute for Basic Science, Seoul, Republic of Korea (S.H.C.).
  • 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.