Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients.

Journal: International journal of surgery (London, England)
Published Date:

Abstract

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis even after curative resection. A deep learning-based stratification of postoperative survival in the preoperative setting may aid the treatment decisions for improving prognosis. This study was aimed to develop a deep learning model based on preoperative data for predicting postoperative survival.

Authors

  • Woohyung Lee
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: ywhnet@amc.seoul.kr.
  • Hyo Jung Park
    Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Hack-Jin Lee
    R&D Team, DoAI Inc., Seongnam-si, Gyeonggi-do, Republic of Korea. Electronic address: hackjinlee@doai.ai.
  • Eunsung Jun
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: jeongo1040@gmail.com.
  • Ki Byung Song
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: mtsong21c@naver.com.
  • Dae Wook Hwang
    Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: dwhwang@amc.seoul.kr.
  • Jae Hoon Lee
    Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea.
  • Kyongmook Lim
    R&D Team, DoAI Inc., Seongnam-si, Gyeonggi-do, Republic of Korea. Electronic address: kyongmooklim@doai.ai.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Seung Soo Lee
    From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).
  • Jae Ho Byun
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: jhbyun@amc.seoul.kr.
  • Hyoung Jung Kim
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. Electronic address: hjk@amc.seoul.kr.
  • Song Cheol Kim
    Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.