Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging.

Journal: AJNR. American journal of neuroradiology
Published Date: Jul 1, 2020

Abstract

BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors on routine imaging may help in preoperative evaluation and guide surgical resection planning. However, qualitative radiologic MR imaging review has limited performance. This study aimed to compare different machine learning approaches to classify pediatric posterior fossa tumors on routine MR imaging.

Authors

  • H Zhou
    Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • R Hu
    From the School of Computer Science and Engineering (R.H., B.Z., C.Z.).
  • O Tang
    Warren Alpert Medical School, Brown University (O.T.), Providence, Rhode Island.
  • C Hu
    Department of Neurology (C.H.), Hunan Provincial People's Hospital, Changsha, Hunan, China.
  • L Tang
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, China.
  • K Chang
    Department of Radiology (K.C.), Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Q Shen
    Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • J Wu
    Radiology (Q.S., J.W.), Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • B Zou
    From the School of Computer Science and Engineering (R.H., B.Z., C.Z.).
  • B Xiao
    Department of Neurology (H.Z., L.T., B.X.), Xiangya Hospital of Central South University, Changsha, Hunan, China.
  • J Boxerman
    Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital.
  • W Chen
    Research Information Solutions and Innovations , Columbus, OH.
  • R Y Huang
    Department of Radiology (R.Y.H.), Brigham and Women's Hospital, Boston, Massachusetts.
  • L Yang
    Departments of Neurology (L.Y.).
  • H X Bai
    Department of Diagnostic Imaging (J.B., H.X.B.), Rhode Island Hospital.
  • C Zhu
    From the School of Computer Science and Engineering (R.H., B.Z., C.Z.) anandawork@126.com.

Keywords

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