CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.

Journal: Journal of translational medicine
PMID: 36949511

Abstract

BACKGROUND: Stratification of DNA mismatch repair (MMR) status in patients with colorectal cancer (CRC) enables individual clinical treatment decision making. The present study aimed to develop and validate a deep learning (DL) model based on the pre-treatment CT images for predicting MMR status in CRC.

Authors

  • Wuteng Cao
    Department of Colorectal Surgery, Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China.
  • Huabin Hu
    CAS Key Laboratory of Tropical Plant Resources and Sustainable Use, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming 650223, China.
  • Jirui Guo
    Department of Colorectal SurgeryThe Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China.
  • Qiyuan Qin
    Department of Colorectal SurgeryThe Sixth Affiliated Hospital, Sun Yat-sen University Guangzhou 510655 China.
  • Yanbang Lian
    Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
  • Jiao Li
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301 China yinhao@scsio.ac.cn.
  • Qianyu Wu
  • Junhong Chen
    The Hamlyn Centre for Robotic Surgery, Imperial College London, London, SW7 2AZ, UK.
  • Xinhua Wang
    Department of Neurology, Children's Hospital of Fudan University, Shanghai, 200032, China.
  • Yanhong Deng
    Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Research Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510655, Guangdong, China. dengyanh@mail.sysu.edu.cn.

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