Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer.

Journal: Molecular imaging and biology
Published Date: Mar 24, 2021

Abstract

PURPOSE: To examine the prognostic significance of pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG) positron emission tomography (PET)-based radiomic features using a machine learning approach in patients with endometrial cancers.

Authors

  • Masatoyo Nakajo
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan. toyo.nakajo@dolphin.ocn.ne.jp.
  • Megumi Jinguji
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Atsushi Tani
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Hidehiko Kikuno
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Daisuke Hirahara
  • Shinichi Togami
    Department of Obstetrics and Gynecology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Hiroaki Kobayashi
    Department of Urology Saiseikai Yokohamashi Tobu Hospital Kanagawa Japan.
  • Takashi Yoshiura
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.

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