Transformer-based skeletal muscle deep-learning model for survival prediction in gastric cancer patients after curative resection.

Journal: Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
Published Date:

Abstract

BACKGROUND: We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict the survival of patients with gastric cancer (GC).

Authors

  • Qiuying Chen
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
  • Lian Jian
    Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China.
  • Hua Xiao
    Department of Hepatobiliary and Intestinal Surgery, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People's Republic of China.
  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Xiaoping Yu
    Department of Radiology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, Hunan, China.
  • Bo Lai
    Department of Radiology, The First Affiliated Hospital, Jinan University, No. 613, Huangpu West Road, Tianhe District, Guangzhou, Guangdong, 510627, People's Republic of China.
  • Xuewei Wu
    Department of Radiology, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
  • Jingjing You
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Zhe Jin
    Zhejiang University, College of Computer Science and Technology, Hangzhou, China.
  • Li Yu
    Key Laboratory of Colloid and Interface Chemistry, Shandong University, Ministry of Education, Jinan 250100, P. R. China. ylmlt@sdu.edu.cn.
  • Shuixing Zhang
    Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, PR China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, Guangdong, PR China. Electronic address: shui7515@126.com.