Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN).

Authors

  • Yuki Sasaki
    Division of Central Radiology, Niigata Cancer Center Hospital, 2-15-3 Kawagishi-cho, Chuo-ku, Niigata-shi, Niigata, 951-8566, Japan. ysasakiaca@gmail.com.
  • Yohan Kondo
    Department of Radiological Technology, Graduate School of Health Sciences, Niigata University, 2-746 Asahimachi-dori, Chuo-ku, Niigata, 951-8518 Japan.
  • Tadashi Aoki
    Department of Thoracic Surgery, Niigata Cancer Center Hospital, Niigata, Japan.
  • Naoya Koizumi
    Department of Radiology, Niigata Cancer Center Hospital, Niigata, Japan.
  • Toshiro Ozaki
    Department of Radiology, Niigata Cancer Center Hospital, Niigata, Japan.
  • Hiroshi Seki
    Department of Radiology, Niigata Cancer Center Hospital, Niigata, Japan.