Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

Abstract

Authors

• Yae Won Park
  Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea.
• Jongmin Oh
  Department of Convergence Medicine, Ewha Womans University College of Medicine, Seoul, South Korea.
• Seng Chan You
  Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea.
• Kyunghwa Han
  From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea (S.H.P.); and Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.).
• 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.
• 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.
• Jong Hee Chang
  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.
• 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.

Keywords

External Resources