Deep learning-assisted literature mining for in vitro radiosensitivity data.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Oct 1, 2019

Abstract

BACKGROUND AND PURPOSE: Integrated analysis of existing radiosensitivity data obtained by the gold-standard clonogenic assay has the potential to improve our understanding of cancer cell radioresistance. However, extraction of radiosensitivity data from the literature is highly labor-intensive. To aid in this task, using deep convolutional neural networks (CNNs) and other computer technologies, we developed an analysis pipeline that extracts radiosensitivity data derived from clonogenic assays from the literature.

Authors

  • Shuichiro Komatsu
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Takahiro Oike
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan. Electronic address: oiketakahiro@gmail.com.
  • Yuka Komatsu
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Yoshiki Kubota
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Makoto Sakai
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Toshiaki Matsui
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Endang Nuryadi
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan; Department of Radiotherapy, Dr. Cipto Mangunkusumo National General Hospital - Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
  • Tiara Bunga Mayang Permata
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan; Department of Radiotherapy, Dr. Cipto Mangunkusumo National General Hospital - Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
  • Hiro Sato
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Hidemasa Kawamura
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Masahiko Okamoto
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Takuya Kaminuma
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Kazutoshi Murata
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Naoko Okano
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Yuka Hirota
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan.
  • Tatsuya Ohno
    Gunma University Heavy Ion Medical Center, Gunma, Japan.
  • Jun-Ichi Saitoh
    Department of Radiation Oncology, University of Toyama Faculty of Medicine, Japan.
  • Atsushi Shibata
    Department of Cardiovascular Medicine, Osaka City University Graduate School of Medicine, Osaka, Japan.
  • Takashi Nakano
    Department of Radiation Oncology, Gunma University Graduate School of Medicine, Gunma, Japan; Gunma University Heavy Ion Medical Center, Gunma, Japan.

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