Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.

Journal: International journal of radiation oncology, biology, physics
PMID: 31344432

Abstract

PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.

Authors

  • Satoshi Takahashi
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Wataru Takahashi
    Department of Radiology, University of Tokyo, Tokyo.
  • Shota Tanaka
    Department of Neurosurgery, University of Tokyo, Tokyo. Electronic address: tanakas-tky@umin.ac.jp.
  • Akihiro Haga
  • Takahiro Nakamoto
    Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University.
  • Yuichi Suzuki
  • Akitake Mukasa
    Department of Diagnostic Radiology, Graduate School of Life Sciences, Kumamoto University, 1-1-1 Honjo, Chuoku, Kumamoto, 860-8556, Japan. Electronic address: mukasa@kumamoto-u.ac.jp.
  • Shunsaku Takayanagi
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Yosuke Kitagawa
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Taijun Hana
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Takahide Nejo
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Masashi Nomura
    Department of Neurosurgery, University of Tokyo, Tokyo.
  • Keiichi Nakagawa
    Department of Radiology, University of Tokyo, Tokyo.
  • Nobuhito Saito

Keywords

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