Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.

Journal: Asian journal of surgery
PMID: 37062601

Abstract

BACKGROUND: For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great value in medical image analyses. Here we used deep learning algorithms of endoscopic images for the assessment of NAT response in LARC.

Authors

  • Anqi Wang
    Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, China.
  • Jieli Zhou
    UM-SJTU Joint Institute, Shanghai Jiao Tong University, China.
  • Gang Wang
    National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
  • Beibei Zhang
    School of Statistics, Capital University of Economics and Business, Beijing, China.
  • Hongyi Xin
    UM-SJTU Joint Institute, Shanghai Jiao Tong University, China. Electronic address: hongyi.xin@sjtu.edu.cn.
  • Haiyang Zhou
    Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, China. Electronic address: haiyang1985_1@aliyun.com.

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