Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.

Authors

  • Qiong Ma
    Beijing Institute of Radiation Medicine, 27 Taiping Road, 100850, Beijing, China.
  • Runqi Meng
    School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
  • Ruiting Li
    Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Ling Dai
    Department of Computer Science and Engineering, Shanghai Jiao Tong University, China; MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, China.
  • Fu Shen
    Department of Radiology, Changhai Hospital, No.168 Changhai Road, Shanghai, 200433, China. ssff_53@163.com.
  • Jie Yuan
    Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, National Center of Technology Innovation for Synthetic Biology, No. 32, Xiqi Road, Tianjin Airport Economic Park, Tianjin 300308, China.
  • Danqi Sun
    Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, China.
  • Manman Li
    Department of Radiology, Nantong Tumor Hospital, Nantong University, Nantong, China.
  • Caixia Fu
  • Rong Li
    Department of Neurology, People's Hospital of Longhua, Shenzhen, China.
  • Feng Feng
    Department of Microbiology, Boston University, Boston, MA 02118, USA.
  • Yonggang Li
    Department of Radiology, The First Affiliated Hospital of Soochow University, Jiangsu Province, Jiangsu Province, 215006, Suzhou City, China. liyonggang224@163.com.
  • Tong Tong
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Yajia Gu
    Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. cjr.guyajia@vip.163.com.
  • Yiqun Sun
    Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.