A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
PMID:

Abstract

BACKGROUND: To construct and assess a deep learning (DL) signature that employs computed tomography imaging to predict the expression status of programmed cell death ligand 1 in patients with bladder cancer (BCa).

Authors

  • Xiaomeng Han
    Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China.
  • Jing Guan
    Department of Radiology, The Second Xiangya Hospital of Central South University, No.139 Middle Renmin Road, Changsha, Hunan 410011, PR China.
  • Li Guo
    Department of Dental Implantology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University, Nanjing, China.
  • Qiyan Jiao
    Qingdao Medical College, Qingdao University, Qingdao, Shandong, 266071, China.
  • Kexin Wang
    Clifford Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Feng Hou
    Department of Pathology, The Affiliated Hospital of Qingdao University, Shandong, China.
  • Shunli Liu
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
  • Shifeng Yang
    Department of Radiology, Shandong Provincial Hospital affiliated to Shandong University, Shandong University, Jinan, Shandong, P.R. China.
  • Chencui Huang
    Department of Research Collaboration, R&D Center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Wenbin Cong
    Department of Radiology, The Affiliated Hospital of Qingdao University, 16 Jiangsu Road, Qingdao, Shandong, 266003, China. wenbincong@qdu.edu.cn.
  • Hexiang Wang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Shinan Jiangsu 16 Rd, Qingdao, Shandong 266003, China.