Automated deep learning-assisted early detection of radiation-induced temporal lobe injury on MRI: a multicenter retrospective analysis.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To evaluate the benefits of an automated deep learning-based tool (RTLI-DM) for early detection of radiation-induced temporal lobe injury (RTLI) on MRI.

Authors

  • Fangxue Yang
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Rong Hu
    College of Chemistry and Chemical Engineering, Yunnan Normal University , Yunnan, Kunming, 650092, People's Republic of China.
  • Jing Hu
    College of Chemistry, Sichuan University Chengdu 610064 People's Republic of China xmpuscu@scu.edu.cn +86 028 8541 2290.
  • Linmei Zhao
    Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N. Caroline St., Baltimore, MD 21287, USA.
  • Youming Zhang
    Helmholtz International Lab for Anti-Infectives, Shandong University-Helmholtz Institute of Biotechnology, State Key Laboratory of Microbial Technology, Shandong University, Qingdao, Shandong 266237, PR China.
  • Yitao Mao
    Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Jingyi Tang
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Sai Li
    Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, China.
  • Jiaqi He
    CAS Center for Excellence in Nanoscience, Beijing Key Laboratory of Micro-Nano Energy and Sensor, Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing 101400, P. R. China.
  • Ruiting Chen
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Jiuqing Guo
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Weiwei Zhang
    Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
  • Liping Zhu
  • Xiao Jiao
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Shulin Liu
    College of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
  • Guanghua Luo
    The First Affiliated Hospital, Department of Radiology, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China (J.D., B.L., G.L., H.Z.).
  • Hong Zhou
    Department of TCM Orthopedics & Traumatology, Gansu province hospital of TCM, Lanzhou, China.
  • Xiangjun Fang
    Department of Radiology, The Second Affiliated Hospital of the University of South China, Hengyang, China.
  • Haijun Zheng
    Department of Radiology, First People's Hospital of Chenzhou, University of South China, Chenzhou 423000, China.
  • Lang Li
    Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
  • Zaide Han
    Department of Radiology, Xiangya Hospital of Central South University, Changsha, China.
  • Zhicheng Jiao
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Junfeng Li
    School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China.
  • Weihua Liao
    Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China.