A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study.

Journal: La Radiologia medica
PMID:

Abstract

PURPOSE: To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatellite instability (MSI) prediction in colorectal cancer (CRC).

Authors

  • Weicui Chen
    Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China.
  • Kaiyi Zheng
    School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Wenjing Yuan
    Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China.
  • Ziqi Jia
    Department of Thoracic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
  • Yuankui Wu
  • Xiaohui Duan
    Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China. Electronic address: duanxh5@mail.sysu.edu.cn.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Zhibo Wen
    Radiology Department, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China. zhibowen@163.com.
  • Liming Zhong
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.
  • Xian Liu
    Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, PR China.