Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.

Journal: Korean journal of radiology
Published Date: Sep 1, 2019

Abstract

OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup.

Authors

  • Yae Won Park
    Department of Radiology, Ewha Womans University College of Medicine, Seoul, Korea.
  • 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.
  • 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, Seoul, Korea.

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