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

Dey, Damini; Gaur, Sara; Øvrehus, Kristian Altern; Slomka, Piotr J.; Betancur, Julián; Goeller, Markus; Hell, Michaela M.; Gransar, Heidi; Berman, Daniel S.; Achenbach, Stephan; Bøtker, Hans Erik; Jensen, Jesper Møller; Lassen, Jens Flensted; Nørgaard, Bjarne Linde

doi:10.1007/s00330-017-5223-z

Cited by 146 publications

(92 citation statements)

References 47 publications

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“…Thus, compared to stenosis severity alone, diagnosis of ischemia improved with the addition of coronary plaque features and FFR derived from CCTA. These two findings were in line with observations from other cohorts [13, 14]. As a consequence, CCTA with its ability to fully quantify coronary atherosclerosis—i.e., not only coronary plaque but also the vessel wall to assess positive remodeling and the lumen to assess luminal narrowing—could refine the diagnosis of myocardial ischemia and the selection of patients for ICA with subsequent revascularization (Fig.…”

Section: Coronary Cta To Detect Hemodynamically Significant Atherosclsupporting

confidence: 90%

Coronary Computed Tomography Angiography as a Gatekeeper to Coronary Revascularization: Emphasizing Atherosclerosis Findings Beyond Stenosis

Hoogen

Rosendael

Lin

et al. 2019

Curr Cardiovasc Imaging Rep

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Purpose of Review Coronary computed tomography angiography (CCTA) is the optimal non-invasive test to rule out coronary artery disease (CAD). Decisions to perform coronary revascularization have traditionally been based upon ischemia testing. This review summarizes the latest observations and trials evaluating the suitability of CCTA to select patients for invasive coronary angiography (ICA) and subsequent revascularization. Recent Findings Recent data shows that beyond stenosis, whole-heart quantification and characterization of coronary atherosclerotic plaque improves the estimation of myocardial ischemia. This comprehensive evaluation of the coronary artery tree has greater diagnostic accuracy for invasive fractional flow reserve (FFR) than conventional stress tests. Further, clinical trials have demonstrated that the performance of CCTA in patients with a clinical indication for ICA results in more effective patient care and significantly lower costs. Summary Besides the excellent ability to rule out CAD, recent data shows that quantification and characterization of the coronary artery tree results in high accuracy for ischemia and that CCTA-guided care to select patients for ICA and revascularization is effective. Trials evaluating revascularization based on CCTA findings may be needed.

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Section: Coronary Cta To Detect Hemodynamically Significant Atherosclsupporting

confidence: 90%

Coronary Computed Tomography Angiography as a Gatekeeper to Coronary Revascularization: Emphasizing Atherosclerosis Findings Beyond Stenosis

Hoogen

Rosendael

Lin

et al. 2019

Curr Cardiovasc Imaging Rep

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“…43,44 Another potential utility of coronary CTA is to predict ischemia by assessing plaque stenosis, morphology and/or volume. [52][53][54][55][56] have revealed that increased prevalence of high-risk plaque features was associated with ischemia, especially when positive remodeling and low attenuation plaque were present. 52 Diaz-Zamudio et al has investigated if plaque characteristics assessed by quantitative CT software can predict ischemia and found that total, non-calcified and low attenuation plaque burden were associated with ischemia.…”

Section: Intra-/inter-reader Variability Of Plaque Imaging By Coronarmentioning

confidence: 99%

How accurate is atherosclerosis imaging by coronary computed tomography angiography?

Nakanishi

Motoyama

Leipsic

et al. 2019

Journal of Cardiovascular Computed Tomography

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Invasive coronary plaque imaging such as intravascular ultrasound and optical coherence tomography has been widely used to observe culprit or non-culprit coronary atherosclerosis, as well as optimize stent sizing, apposition and deployment. Coronary computed tomographic angiography (CTA) is non-invasively available to assess coronary artery disease (CAD) and has become an appropriate strategy to evaluate patients with suspected CAD. Given recent technologies, semi-automated plaque software is available to identify coronary plaque stenosis, volume and characteristics and potentially allows to be used for the assessment of more details of plaque information, progression and future risk as a surrogate tool of the invasive imaging modalities. This review article aims to focus on various evidence in coronary plaque imaging by coronary CTA and describes how accurate coronary CTA can classify coronary atherosclerosis.

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“…Finally, Dey and colleagues have reported on the utility of ML and boosted ensembles for the detection of plaque-specific ischemia through CCTA (AUC = 0.84) using invasive FFR as reference (Dey et al, 2018). Their report concentrated on the integration of plaque features and their integration into a strong classifying score, an advantage offered by iterative ML algorithms.…”

Section: Cardiovascular Diagnosismentioning

confidence: 99%

The machine learning horizon in cardiac hybrid imaging

Juárez-Orozco

Martinez-Manzanera

Nesterov

et al. 2018

European J Hybrid Imaging

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Background: Machine learning (ML) represents a family of algorithms that has rapidly developed within the last years in a wide variety of knowledge areas. ML is able to elucidate and grasp complex patterns from data in order to approach prediction and classification problems. The present narrative review summarizes fundamental notions in ML as well as the evidence of its application in standard cardiac imaging and the potential for implementation in cardiac hybrid imaging. Results: ML, and in particular Deep Learning, has begun to revolutionize medical imaging though the optimization of diagnostic and prognostic estimations at the individual-patient level. On the other hand, the spread and availability of high quality non-invasive imaging has provided growing amounts of data in the characterization of suspected cardiovascular diseases. At the same time, modern combined imaging equipment has set the ground for the concept of hybrid imaging to develop. Cardiac hybrid imaging refers to the combination of diagnostic images and offers the possibility to comprehensively characterize the heart and great vessels when a pathology is suspected or clinically known. Analysis and integration of large amounts of cardiac hybrid imaging data (and corresponding clinical profiles) constitutes a highly complex process and ML will likely be able to enhance it in the near future. Conclusion: ML conveys novel and powerful approaches in the processing of large and complex datasets that may include images as well as imaging-derived data. Given the growing amount of data in the realm of cardiac hybrid imaging and the rapid development of ML, it is highly desirable to implement and test ML in the optimization of our multimodality imaging diagnostic and prognostic evaluations in cardiovascular disease.

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

Cited by 146 publications

References 47 publications

Coronary Computed Tomography Angiography as a Gatekeeper to Coronary Revascularization: Emphasizing Atherosclerosis Findings Beyond Stenosis

Coronary Computed Tomography Angiography as a Gatekeeper to Coronary Revascularization: Emphasizing Atherosclerosis Findings Beyond Stenosis

How accurate is atherosclerosis imaging by coronary computed tomography angiography?

The machine learning horizon in cardiac hybrid imaging

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