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Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study

  • Cardiac
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Abstract

Objectives

We aimed to investigate if lesion-specific ischaemia by invasive fractional flow reserve (FFR) can be predicted by an integrated machine learning (ML) ischaemia risk score from quantitative plaque measures from coronary computed tomography angiography (CTA).

Methods

In a multicentre trial of 254 patients, CTA and invasive coronary angiography were performed, with FFR in 484 vessels. CTA data sets were analysed by semi-automated software to quantify stenosis and non-calcified (NCP), low-density NCP (LD-NCP, < 30 HU), calcified and total plaque volumes, contrast density difference (CDD, maximum difference in luminal attenuation per unit area) and plaque length. ML integration included automated feature selection and model building from quantitative CTA with a boosted ensemble algorithm, and tenfold stratified cross-validation.

Results

Eighty patients had ischaemia by FFR (FFR ≤ 0.80) in 100 vessels. Information gain for predicting ischaemia was highest for CDD (0.172), followed by LD-NCP (0.125), NCP (0.097), and total plaque volumes (0.092). ML exhibited higher area-under-the-curve (0.84) than individual CTA measures, including stenosis (0.76), LD-NCP volume (0.77), total plaque volume (0.74) and pre-test likelihood of coronary artery disease (CAD) (0.63); p < 0.006.

Conclusions

Integrated ML ischaemia risk score improved the prediction of lesion-specific ischaemia by invasive FFR, over stenosis, plaque measures and pre-test likelihood of CAD.

Key Points

• Integrated ischaemia risk score improved prediction of ischaemia over quantitative plaque measures

• Integrated ischaemia risk score showed higher prediction of ischaemia than standard approach

• Contrast density difference had the highest information gain to identify lesion-specific ischaemia

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Abbreviations

CDD:

Contrast density difference

CP:

Calcified plaque

CTA:

CT angiography

DLP:

Dose–length product

FFR:

Fractional flow reserve

HU:

Hounsfield unit

LD-NCP:

Low-density noncalcified plaque

NCP:

Noncalcified plaque

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Funding

This study has received funding by National Heart Lung Blood Institute (NIH/NHLBI grant R01HL133616) and the Bundesministerium für Bildung und Forschung (01EX1012B, Spitzencluster Medical Valley)

Author information

Authors and Affiliations

  1. Biomedical Imaging Research Institute, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Taper building, A238, 8700 Beverly Blvd, Los Angeles, 90048, USA

    Damini Dey & Markus Goeller

  2. Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark

    Sara Gaur, Kristian A. Ovrehus, Hans Erik Botker, Jesper Moller Jensen, Jens Flensted Lassen & Bjarne Linde Norgaard

  3. Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Piotr J. Slomka, Julian Betancur, Heidi Gransar & Daniel S. Berman

  4. Department of Cardiology, Friedrich-Alexander Universitat Erlangen-Nurnberg, Erlangen, Germany

    Markus Goeller, Michaela M. Hell & Stephan Achenbach

Authors
  1. Damini Dey
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  2. Sara Gaur
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  3. Kristian A. Ovrehus
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  4. Piotr J. Slomka
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  5. Julian Betancur
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  6. Markus Goeller
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  7. Michaela M. Hell
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  8. Heidi Gransar
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  9. Daniel S. Berman
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  10. Stephan Achenbach
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  11. Hans Erik Botker
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  12. Jesper Moller Jensen
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  13. Jens Flensted Lassen
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  14. Bjarne Linde Norgaard
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Corresponding author

Correspondence to Damini Dey.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Dr. Damini Dey (Associate Professor, Cedars-Sinai Medical Center).

Conflict of interest

The authors of this manuscript declare relationships with the following companies: None.

Dr. Piotr Slomka, Dr. Daniel S Berman and Dr. Damini Dey received software royalties from Cedars-Sinai Medical Center and hold a patent.

Statistics and biometry

One of the co-authors (Heidi Gransar, MS) is an experienced biostatistician and she provided her expertise for this study.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional review board approval was obtained.

Study subjects or cohorts overlap

This study is a new post hoc analysis comprising all 254 patients from the prospective, multicentre NXT trial (NCT01757678). The original study has been previously described (Norgaard et al. J Am Coll Cardiology 2014 [21]). While the patient cohort is the same, there was no overlap with this study which evaluates objective machine learning integration of quantitative coronary CT angiography to predict ischaemia.

Methodology

• prospective study

• post hoc analysis

• multicentre study

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Dey, D., Gaur, S., Ovrehus, K.A. et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study. Eur Radiol 28, 2655–2664 (2018). https://doi.org/10.1007/s00330-017-5223-z

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  • DOI: https://doi.org/10.1007/s00330-017-5223-z

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