The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR, or high-risk plaque features?

Journal: European radiology
Published Date: Mar 22, 2019

Abstract

OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically significant, with reference to FFR.

Authors

  • Mengmeng Yu
    Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, 200233 Shanghai, China.
  • Zhigang Lu
    Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital Shanghai 200233, China.
  • Chengxing Shen
    Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China.
  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Yining Wang
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
  • Bin Lu
    Department of Endocrinology and Metabolism, Huadong Hospital, Fudan University, Shanghai, China.
  • Jiayin Zhang
    Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.

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