Real-time automatic prediction of treatment response to transcatheter arterial chemoembolization in patients with hepatocellular carcinoma using deep learning based on digital subtraction angiography videos.

Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date:

Abstract

BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach.

Authors

  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Yicheng Jiang
    Research Institute of Electronic Engineering Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.
  • Zhe Jin
    Zhejiang University, College of Computer Science and Technology, Hangzhou, China.
  • Wenting Jiang
    Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, China.
  • Bin Zhang
    Department of Psychiatry, Sleep Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Changmiao Wang
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, Shenzhen 518055, China; University of Chinese Academy of Sciences, 52 Sanlihe Road, Beijing 100864, China.
  • Lingeng Wu
    Department of Interventional Therapy, The Second Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, China.
  • Luyan Chen
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Qiuying Chen
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China.
  • Shuyi Liu
    The Experimental High School Attached to Beijing Normal University, No. 14 Erlong Road, Beijing 100051, PR China.
  • Jingjing You
    Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Xiaokai Mo
    Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, PR China; Shantou University Medical College, Guangdong, PR China.
  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Zhiyuan Xiong
    Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.
  • Tao Huang
    The Second Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Liyang Yang
    Medical Imaging Center, Nanfang Hospital, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou, Guangdong, China.
  • Xiang Wan
    Institute of Computational and Theoretical Study and Department of Computer Science, Hong Kong Baptist University, Hong Kong, P.R. China.
  • Ge Wen
    Medical Imaging Center, Nanfang Hospital, Southern Medical University, 1023 Shatai South Road, Baiyun District, Guangzhou, Guangdong, China. m13360022166@163.com.
  • Xiao Guang Han
    Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, China. hanxiaoguang@cuhk.edu.cn.
  • Weijun Fan
    Department of Minimally Invasive Intervention, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China. fanwj@sysucc.org.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.