CT morphological index provides incremental value to machine learning based CT-FFR for predicting hemodynamically significant coronary stenosis.

Journal: International journal of cardiology
Published Date: Aug 15, 2018

Abstract

AIMS: To study the diagnostic performance of the ratio of Duke jeopardy score (DJS) to the minimal lumen diameter (MLD) at coronary computed tomographic angiography (CCTA) and machine learning based CT-FFR for differentiating functionally significant from insignificant lesions, with reference to fractional flow reserve (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.
  • Wenbin Li
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.
  • Meng Wei
  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Jiayin Zhang
    Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.

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