Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT Angiography.

Journal: Radiology
PMID:

Abstract

UNLABELLED: Background CT is helpful in guiding the revascularization of chronic total occlusion (CTO), but manual prediction scores of percutaneous coronary intervention (PCI) success have challenges. Deep learning (DL) is expected to predict success of PCI for CTO lesions more efficiently. Purpose To develop a DL model to predict guidewire crossing and PCI outcomes for CTO using coronary CT angiography (CCTA) and evaluate its performance compared with manual prediction scores.

Authors

  • Zhen Zhou
    Deepwise Healthcare, Beijing 100080, China.
  • Yifeng Gao
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University.
  • Weiwei Zhang
    Department of Laboratory Medicine, Affiliated Hospital of Nantong University, Nantong, Jiangsu, China.
  • Nan Zhang
    Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Zhifan Gao
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • Xiaomeng Huang
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Rd, Chaoyang District, Beijing 100029, China (Z.Z., Y.G., N.Z., H.W., R.W., L.X.); School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China (W.Z., Z.G., H.Z.); Keya Medical Company, Shenzhen, China (X.H.); Department of Cardiology, Chinese PLA General Hospital, Beijing, China (S.Z.); Department of Radiology, The First Hospital of China Medical University, Shenyang, China (X.D.); Cardiovascular Research Centre, Royal Brompton Hospital, London, UK (G.Y.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); and Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif (K.N.).
  • Shanshan Zhou
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Rd, Chaoyang District, Beijing 100029, China (Z.Z., Y.G., N.Z., H.W., R.W., L.X.); School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China (W.Z., Z.G., H.Z.); Keya Medical Company, Shenzhen, China (X.H.); Department of Cardiology, Chinese PLA General Hospital, Beijing, China (S.Z.); Department of Radiology, The First Hospital of China Medical University, Shenyang, China (X.D.); Cardiovascular Research Centre, Royal Brompton Hospital, London, UK (G.Y.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); and Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif (K.N.).
  • Xu Dai
    From the Institute of Diagnostic and Interventional Radiology (Y.L., M.Y., X.D., J.Z.) and Department of Cardiology (Z.L., C.S.), Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China 200233; Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China (Y.W.); and Department of Radiology, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Centre for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (B.L.).
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Heye Zhang
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • Koen Nieman
    Department of Cardiology (A.C., M.L.L., J.D., K.N.).
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.