Deep Learning and Radiomics Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion using CTA.

Journal: Academic radiology
Published Date: Mar 30, 2025

Abstract

RATIONALE AND OBJECTIVES: Coronary chronic total occlusion (CTO) and subtotal occlusion (STO) pose diagnostic challenges, differing in treatment strategies. Artificial intelligence and radiomics are promising tools for accurate discrimination. This study aimed to develop deep learning (DL) and radiomics models using coronary computed tomography angiography (CCTA) to differentiate CTO from STO lesions and compare their performance with that of the conventional method.

Authors

  • Zhen Zhou
    Deepwise Healthcare, Beijing 100080, China.
  • Kairui Bo
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University.
  • 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.
  • Hongkai Zhang
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China (Z.Z., K.B., Y.G., H.Z., Y.C., Y.C., H.W., N.Z., L.X.).
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Yanchun Chen
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China (Z.Z., K.B., Y.G., H.Z., Y.C., Y.C., H.W., N.Z., L.X.).
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Nan Zhang
    Department of Pulmonary and Critical Care Medicine II, Emergency General Hospital, Beijing, China.
  • Yimin Huang
    ShuKun Technology Co., Ltd, Beichen Century Center, West Beichen Road, Beijing, 100029, China.
  • Xinsheng Mao
    Shukun (Beijing) Network Technology Co., Ltd., Beijing 102200, China.
  • Zhifan Gao
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • Heye Zhang
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • 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.

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