Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography

QUIET WARRIOR - Rationale and design: An ancillary study to the Women's IschemiA TRial to Reduce Events in Nonobstructive CAD (WARRIOR)

Background

Cardiovascular disease is the leading cause of death among women in the US, predominantly due to ischemic heart disease (IHD). There is a notable deficiency in therapies tailored for IHD in women, who often present with variable symptoms that delay diagnosis and treatment. In many cases, coronary angiography does not reveal obstructive coronary artery disease (CAD) despite increased risk for major adverse cardiac events (MACE) compared with sex and age-matched asymptomatic cohorts.

Objectives

The Women's IschemiA TRial to Reduce Events in Nonobstructive CAD (WARRIOR) evaluates intensive medical treatment for women with Ischemia with No Obstructive Coronary Arteries (INOCA). The QUIET WARRIOR sub-study aims to improve predictive tools for adverse outcomes by detailed analysis of Coronary Computed Tomography Angiography (CCTA) data and biorepository samples. These data will also uncover pathophysiological mechanisms associated with angina and MACE, improving predictive tools for symptomatic women with INOCA.

Methods

This ancillary study will analyze CCTA images from 600 WARRIOR subjects. It will assess clinical, social, and coronary artery variables, including plaque characteristics and markers of inflammation. Advanced imaging techniques and machine-learning models will be employed to quantify plaque features and predict clinical outcomes.

Expected results

The study aims to elucidate associations between CCTA-derived plaque characteristics, ischemic symptoms, and MACE. Anticipated findings include correlations of specific plaque attributes with angina severity and novel insights into inflammatory markers. Socioeconomic variables will also be examined for their impact on cardiovascular risk.

Conclusion

The QUIET WARRIOR sub-study will advance the understanding of INOCA in women, integrating clinical, imaging, and socioeconomic data to enhance risk prediction and guide personalized therapeutic strategies. This research will address critical gaps in managing nonobstructive CAD, promoting more equitable cardiovascular care.

Advanced CT measures of coronary artery disease with intermediate stenosis in patients with severe aortic valve stenosis

BACKGROUND

Coronary artery disease (CAD) and severe aortic valve stenosis (AS) frequently coexist. While pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) allows to rule out obstructive CAD, interpreting hemodynamic significance of intermediate stenoses is challenging. This study investigates the incremental value of CT-derived fractional flow reserve (CT-FFR), quantitative coronary plaque characteristics (e.g., stenosis degree, plaque volume, and composition), and peri-coronary adipose tissue (PCAT) density to detect hemodynamically significant lesions among those with AS and CAD.

MATERIALS AND METHODS

We included patients with severe AS and intermediate coronary lesions (20-80% diameter stenosis) who underwent pre-TAVR CTA and invasive coronary angiogram (ICA) with resting full-cycle ratio (RFR) assessment between 08/16 and 04/22. CTA image analysis included assessment of CT-FFR, quantitative coronary plaque analysis, and PCAT density. Coronary lesions with RFR ≤ 0.89 indicated hemodynamic significance as reference standard.

RESULTS

Overall, 87 patients (age 77.9 ± 7.4 years, 38% female) with 95 intermediate coronary artery lesions were included. CT-FFR showed good discriminatory capacity (area under receiver operator curve (AUC) = 0.89, 95% confidence interval (CI) 0.81-0.96, p < 0.001) to identify hemodynamically significant lesions, superior to anatomical assessment, plaque morphology, and PCAT density. Plaque composition and PCAT density did not differ between lesions with and without hemodynamic significance. Univariable and multivariable analyses revealed CT-FFR as the only predictor for functionally significant lesions (odds ratio 1.28 (95% CI 1.17-1.43), p < 0.001). Overall, CT-FFR ≤ 0.80 showed diagnostic accuracy, sensitivity, and specificity of 88.4% (95%CI 80.2-94.1), 78.5% (95%CI 63.2-89.7), and 96.2% (95%CI 87.0-99.5), respectively.

CONCLUSION

CT-FFR was superior to CT anatomical, plaque morphology, and PCAT assessment to detect functionally significant stenoses in patients with severe AS.

CLINICAL RELEVANCE STATEMENT

CT-derived fractional flow reserve in patients with severe aortic valve stenosis may be a useful tool for non-invasive hemodynamic assessment of intermediate coronary lesions, while CT anatomical, plaque morphology, and peri-coronary adipose tissue assessment have no incremental or additional benefit. These findings might help to reduce pre-transcatheter aortic valve replacement invasive coronary angiogram.

KEY POINTS

• Interpreting the hemodynamic significance of intermediate coronary stenoses is challenging in pre-transcatheter aortic valve replacement CT. • CT-derived fractional flow reserve (CT-FFR) has a good discriminatory capacity in the identification of hemodynamically significant coronary lesions. • CT-derived anatomical, plaque morphology, and peri-coronary adipose tissue assessment did not improve the diagnostic capability of CT-FFR in the hemodynamic assessment of intermediate coronary stenoses.

Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.

Coronary CT-based FFR in patients with acute myocardial infarction might predict follow-up invasive FFR: The XPECT-MI study

BACKGROUND

We aimed to evaluate whether invasive fractional flow reserve (FFRi) of non-infarction related (non-IRA) lesions changes over time in ST-elevation myocardial infarction (STEMI) patients. Moreover, we assessed the diagnostic performance of coronary CT angiography-derived FFR(FFRCT) following the index event in predicting follow-up FFRi.

METHODS

We prospectively enrolled 38 STEMI patients (mean age 61.6 ​± ​9 years, 23.1% female) who underwent non-IRA baseline and follow-up FFRi measurements and a baseline FFRCT (within ≤10 days after STEMI). Follow-up FFRi was performed at 45-60 days (FFRi and FFRCT value of ≤0.8 was considered positive).

RESULTS

FFRi values showed significant difference between baseline and follow-up (median and interquartile range (IQR) 0.85 [0.78-0.92] vs. 0.81 [0.73-0.90] p ​= ​0.04, respectively). Median FFRCT was 0.81 [0.68-0.93]. In total, 20 lesions were positive on FFRCT. A stronger correlation and smaller bias were found between FFRCT and follow-up FFRi (ρ ​= ​0.86,p ​< ​0.001,bias:0.01) as compared with baseline FFRi (ρ ​= ​0.68, p ​< ​0.001,bias:0.04). Comparing follow-up FFRi and FFRCT, no false negatives but two false positive cases were found. The overall accuracy was 94.7%, with sensitivity and specificity of 100.0% and 90.0% for identifying lesions ≤0.8 on FFRi. Accuracy, sensitivity, and specificity were 81.5%, 93.3%, and 73.9%, respectively, for identifying significant lesions on baseline FFRi using index FFRCT.

CONCLUSION

FFRCT in STEMI patients close to the index event could identify hemodynamically relevant non-IRA lesions with higher accuracy than FFRi measured at the index PCI, using follow-up FFRi as the reference standard. Early FFRCT in STEMI patients might represent a new application for cardiac CT to improve the identification of patients who benefit most from staged non-IRA revascularization.

Artificial Intelligence in Cardiovascular CT and MR Imaging

The technological development of Artificial Intelligence (AI) has grown rapidly in recent years. The applications of AI to cardiovascular imaging are various and could improve the radiologists' workflow, speeding up acquisition and post-processing time, increasing image quality and diagnostic accuracy. Several studies have already proved AI applications in Coronary Computed Tomography Angiography and Cardiac Magnetic Resonance, including automatic evaluation of calcium score, quantification of coronary stenosis and plaque analysis, or the automatic quantification of heart volumes and myocardial tissue characterization. The aim of this review is to summarize the latest advances in the field of AI applied to cardiovascular CT and MR imaging.

Deep learning-based detection of functionally significant stenosis in coronary CT angiography

Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.

Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow

BACKGROUND

A pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET).

METHODS

This post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [15O]H2O PET, and invasive FFR. Plaque quantification from CCTA was performed using semiautomated software. An ML algorithm trained on the prospective NXT trial (484 vessels) was used to develop a ML score for the prediction of ischemia (FFR≤0.80), which was then evaluated in 581 vessels from the PACIFIC trial. Thereafter, the ML score was applied for predicting impaired hyperemic MBF (≤2.30 mL/min per g) from corresponding PET scans. The performance of the ML score was compared with CCTA reads and noninvasive FFR derived from CCTA (FFRCT).

RESULTS

One hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84; P<0.001) and comparable with that of FFRCT (0.93; P=0.34). Quantitative percent diameter stenosis and low-density noncalcified plaque volume had the greatest ML feature importance for predicting FFR-defined ischemia. When applied for impaired MBF prediction, the ML score exhibited an area under the receiver-operating characteristic curve of 0.80; significantly higher than visual stenosis grade (area under the receiver-operating characteristic curve 0.74; P=0.02) and comparable with FFRCT (area under the receiver-operating characteristic curve 0.77; P=0.16).

CONCLUSIONS

An externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFRCT.

Artificial intelligence and machine learning in cardiovascular computed tomography

Computed tomography (CT) is emerging as a prominent diagnostic modality in the field of cardiovascular imaging. Artificial intelligence (AI) is making significant strides in the field of information technology, the commercial industry, and health care. Machine learning (ML), a branch of AI, can optimize the performance of CT and augment the assessment of coronary artery disease. These ML platforms can automate multiple tasks, perform calculations, and integrate information from a variety of data sources. In this review article, we explore the ML in CT imaging.

Clinical application of computed tomography angiography and fractional flow reserve computed tomography in patients with coronary artery disease: A meta-analysis based on pre- and post-test probability

PURPOSE

To assess the diagnostic role of coronary computed tomography angiography (CCTA) and fractional flow reserve computed tomography (FFRCT) in confirming or excluding ischemic coronary artery disease (CAD) and to provide a rational use of CCTA and FFRCT in different pre-test probability (PTP) of CAD.

METHODS

We searched the electronic databases from the earliest relevant literature to July 2020 comparing FFRCT or CCTA with FFR. The bivariate random-effects models and Bayes' theorem were used to investigate the diagnostic performance of CCTA and FFRCT with the sensitivity, specificity, pre- and post-test probability.

RESULTS

Fifty-three articles with 4817 patients and 7026 vessels finally met our inclusion criteria. At the patient level, the sensitivity and specificity of CCTA were (0.94, 0.89-0.97), and (0.50, 0.43-0.58), respectively. For FFRCT, the sensitivity and specificity were (0.90, 0.87-0.93) and (0.81, 0.73-0.87). CCTA or FFRCT could increase the post-test probability to >85 % in patients with a PTP > 74.9 % or 54.5 %; CCTA or FFRCT could decrease the post-test probability to <15 % in patients with a pre-test probability <61.3 % or 59.3 %.

CONCLUSIONS

In patients with low to intermediate PTP, CCTA is suggested to exclude CAD, while the time-consuming calculation of FFRCT may be unnecessary. If CCTA detects significant or uncertain stenosis with intermediate to high PTP of CAD, further FFRCT is suggested. The advantages of FFRCT for guiding CAD treatment have sufficiently been demonstrated.

Artificial intelligence in cardiac radiology

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.

Noncalcified plaque burden quantified from coronary computed tomography angiography improves prediction of side branch occlusion after main vessel stenting in bifurcation lesions: results from the CT-PRECISION registry

OBJECTIVES

To assess the incremental value of quantitative plaque features measured from computed tomography angiography (CTA) for predicting side branch (SB) occlusion in coronary bifurcation intervention.

METHODS

We included 340 patients with 377 bifurcation lesions in the post hoc analysis of the CT-PRECISION registry. Each bifurcation was divided into three segments: the proximal main vessel (MV), the distal MV, and the SB. Segments with evidence of coronary plaque were analyzed using semi-automated software allowing for quantitative analysis of coronary plaque morphology and stenosis. Coronary plaque measurements included calcified and noncalcified plaque volumes, and corresponding burdens (respective plaque volumes × 100%/vessel volume), remodeling index, and stenosis.

RESULTS

SB occlusion occurred in 28 of 377 bifurcation lesions (7.5%). The presence of visually identified plaque in the SB segment, but not in the proximal and distal MV segments, was the only qualitative parameter that predicted SB occlusion with an area under the curve (AUC) of 0.792. Among quantitative plaque parameters calculated for the SB segment, the addition of noncalcified plaque burden (AUC 0.840, p = 0.003) and low-density plaque burden (AUC 0.836, p = 0.012) yielded significant improvements in predicting SB occlusion. Using receiver operating characteristic curve analysis, optimal cut-offs for noncalcified plaque burden and low-density plaque burden were > 33.6% (86% sensitivity and 78% specificity) and > 0.9% (89% sensitivity and 73% specificity), respectively.

CONCLUSIONS

CTA-derived noncalcified plaque burden, when added to the visually identified SB plaque, significantly improves the prediction of SB occlusion in coronary bifurcation intervention.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT03709836 registered on October 17, 2018.

Prognostic Value of Coronary Computed Tomography Angiography-derived Morphologic and Quantitative Plaque Markers Using Semiautomated Plaque Software

PURPOSE

In this study, we analyzed the prognostic value of coronary computed tomography angiography-derived morphologic and quantitative plaque markers and plaque scores for major adverse cardiovascular events (MACEs).

MATERIALS AND METHODS

We analyzed the data of patients with suspected coronary artery disease (CAD). Various plaque markers were obtained using a semiautomated software prototype or derived from the results of the software analysis. Several risk scores were calculated, and follow-up data concerning MACE were collected from all patients.

RESULTS

A total of 131 patients (65±12 y, 73% male) were included in our study. MACE occurred in 11 patients within the follow-up period of 34±25 months.CAD-Reporting and Data System score (odds ratio [OR]=11.62), SYNTAX score (SS) (OR=1.11), Leiden-risk score (OR=1.37), segment involvement score (OR=1.76), total plaque volume (OR=1.20), and percentage aggregated plaque volume (OR=1.32) were significant predictors for MACE (all P≤0.05). Moreover, the difference of the corrected coronary opacification (ΔCCO) correlated significantly with the occurrence of MACE (P<0.0001). The CAD-Reporting and Data System score, SS, and Leiden-risk score showed substantial sensitivity for predicting MACE (90.9%). The SS and Leiden-risk score displayed high specificities of 80.8% and 77.5%, respectively. These plaque markers and risk scores all provided high negative predictive value (>90%).

CONCLUSION

The coronary computed tomography angiography-derived plaque markers of segment involvement score, total plaque volume, percentage aggregated plaque volume, and ΔCCO, and the risk scores exhibited predictive value for the occurrence of MACE and can likely aid in identifying patients at risk for future cardiac events.

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia

OBJECTIVES

We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FFR as the reference standard.

METHODS

Eighty-four patients (61 ± 10 years, 65% male) who had undergone cCTA followed by invasive FFR were included in this single-center retrospective, IRB-approved, HIPAA-compliant study. Various plaque markers were derived from cCTA using a semi-automatic software prototype and deep machine learning-based CT-FFR. The discriminatory value of plaque markers and CT-FFR to identify lesion-specific ischemia on a per-vessel basis was evaluated using invasive FFR as the reference standard.

RESULTS

One hundred three lesion-containing vessels were investigated. 32/103 lesions were hemodynamically significant by invasive FFR. In a multivariate analysis (adjusted for Framingham risk score), the following markers showed predictive value for lesion-specific ischemia (odds ratio [OR]): lesion length (OR 1.15, p = 0.037), non-calcified plaque volume (OR 1.02, p = 0.007), napkin-ring sign (OR 5.97, p = 0.014), and CT-FFR (OR 0.81, p < 0.0001). A receiver operating characteristics analysis showed the benefit of identifying plaque markers over cCTA stenosis grading alone, with AUCs increasing from 0.61 with ≥ 50% stenosis to 0.83 with addition of plaque markers to detect lesion-specific ischemia. Further incremental benefit was realized with the addition of CT-FFR (AUC 0.93).

CONCLUSION

Coronary CTA-derived plaque markers portend predictive value to identify lesion-specific ischemia when compared to cCTA stenosis grading alone. The addition of CT-FFR to plaque markers shows incremental discriminatory power.

KEY POINTS

• Coronary CT angiography (cCTA)-derived quantitative plaque markers of atherosclerosis portend high discriminatory power to identify lesion-specific ischemia. • Coronary CT angiography-derived fractional flow reserve (CT-FFR) shows superior diagnostic performance over cCTA alone in detecting lesion-specific ischemia. • A combination of plaque markers with CT-FFR provides incremental discriminatory value for detecting flow-limiting stenosis.

Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up

Aims

Adverse plaque characteristics determined by coronary computed tomography angiography (CTA) have been associated with future cardiac events. Our aim was to investigate whether quantitative global per-patient plaque characteristics from coronary CTA can predict subsequent cardiac death during long-term follow-up.

Methods and results

Out of 2748 patients without prior history of coronary artery disease undergoing CTA with dual-source CT, 32 patients suffered cardiac death (mean follow-up of 5 ± 2 years). These patients were matched to 32 controls by age, gender, risk factors, and symptoms (total 64 patients, 59% male, age 69 ± 10 years). Coronary CTA data sets were analysed by semi-automated software to quantify plaque characteristics over the entire coronary tree, including total plaque volume, volumes of non-calcified plaque (NCP), low-density non-calcified plaque (LD-NCP, attenuation <30 Hounsfield units), calcified plaque (CP), and corresponding burden (plaque volume × 100%/vessel volume), as well as stenosis and contrast density difference (CDD, maximum percent difference in luminal attenuation/cross-sectional area compared to proximal cross-section). In patients who died from cardiac cause, NCP, LD-NCP, CP and total plaque volumes, quantitative stenosis, and CDD were significantly increased compared to controls (P < 0.025 for all). NCP > 146 mm³ [hazards ratio (HR) 2.24; 1.09-4.58; P = 0.027], LD-NCP > 10.6 mm³ (HR 2.26; 1.11-4.63; P = 0.025), total plaque volume > 179 mm³ (HR 2.30; 1.12-4.71; P = 0.022), and CDD > 35% in any vessel (HR 2.85;1.4-5.9; P = 0.005) were associated with increased risk of future cardiac death, when adjusted for segment involvement score.

Conclusion

Among quantitative global plaque characteristics, total, non-calcified, and low-density plaque volumes as well as CDD predict cardiac death in long-term follow-up.

Decision Support Tools, Systems, and Artificial Intelligence in Cardiac Imaging

Noninvasive cardiac imaging is widely used for the diagnosis and management of cardiac patients. The increasing demand for cardiac imaging begins to exceed the number of available interpreting physicians, leaving less time to interpret studies. In addition, the busy clinician is facing the increasingly daunting task of keeping abreast of current medical advancements and the ongoing changes in disease diagnosis and therapy. Committing to memory and recalling such large volumes of information is challenging and is responsible for difficulties in adopting the rapid changes in imaging practice, and is likely partially responsible for errors in patient diagnosis and management. Diagnostic errors rank high in the cause of death in the United States, and are more common than any other medical error and are responsible for most malpractice claims. Most of these errors are related to cognitive errors. The use of artificial intelligence systems that can serve as complementary methods to assist humans with decision making can potentially prevent these errors. The past decades witnessed the development and integration of these tools, which can assist physicians with image interpretation. These tools work to optimize image quality for better visualization and accompany all imaging modalities, starting from patient selection for the appropriate test, patient preparation, image acquisition, processing, and finally interpretation. Current and future directions for technologies that support cardiac imaging physicians are discussed in this review.

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

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.

Automated Quantitative Plaque Analysis for Discrimination of Coronary Chronic Total Occlusion and Subtotal Occlusion in Computed Tomography Angiography

PURPOSE

The aim of this study was to evaluate the utility of automated plaque analysis in differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) in patients with ambiguous coronary lesions on coronary computed tomography angiography (CTA).

MATERIALS AND METHODS

A total of 63 patients with 63 ambiguous coronary lesions on CTA were included. The lesion length (LL), diameter stenosis, plaque volume and composition, remodeling index, and contrast density difference (CDD) (reflecting intraluminal contrast kinetics over the lesion) were assessed using an automatic software tool. All patients underwent invasive coronary angiography.

RESULTS

Coronary angiography confirmed 28 CTOs and 35 SOs. CTOs showed significantly longer LL (6.4±12.3 vs. 1.0±2.2 mm, P=0.03) and higher CDD (74%±31% vs. 55%±32%, P=0.02) compared with SO. The optimal thresholds for prediction of CTO for CDD and LL were ≥43% and ≥1 mm, respectively (max. sensitivity: 82% for CDD, max. specificity: 77% for LL). The guidewire manipulation time correlated with LL (r=0.529, P=0.004) and CDD (r=0.435, P=0.021) in lesions attempted by percutaneous coronary intervention.

CONCLUSIONS

Automated computed tomography plaque analysis may be applied as a noninvasive tool to differentiate CTO from SO.

Quantitative plaque features from coronary computed tomography angiography to identify regional ischemia by myocardial perfusion imaging

AIMS

We aimed to investigate whether quantitative plaque features measured from coronary CT angiography (CCTA) predict ischemia by myocardial perfusion SPECT imaging (MPI).

METHODS AND RESULTS

Hundred and eighty-four consecutive patients (63% males) with suspected-coronary artery disease, undergoing hybrid CCTA, and attenuation corrected solid state 99mTc stress/rest MPI and single vessel ischemia were considered. Quantitative analysis of CCTA derived non-calcified plaque (NCP), low-density NCP [< 30 Hounsfield Units (HU)] (LDNCP), calcified and total plaque burdens (%, normalized to vessel volume), maximum diameter stenosis and contrast density difference (CD, maximum difference in HU/lumen area within lesion). Normal thresholds for plaque features were defined as 95th percentile thresholds, from 40% of vessels with non-ischemic MPI regions. These vessels were excluded from further analysis. Regional ischemia (≥ 2%) was quantified from MPI. All plaque features were higher in arteries corresponding to ischemia (P < 0.003 for all). In multi-variable analysis, abnormal NCP burden [odds ratio (OR) 2.6], LDNCP burden (OR 3.9), and CD (OR 2.7) were significantly associated with ischemia, whereas stenosis ≥ 50% was not (P = 0.14). In a subset of vessels with ≥ 50% stenosis, LDNCP burden (OR 4.3, P = 0.008) and CD (OR 3.7, P = 0.029) were associated with ischemia. In subsets of vessels with stenosis 30-69% and ≥ 70%, abnormal LDNCP burden (OR 6.4, P = 0.006) and CD (OR 7.3, P = 0.02) were associated with ischemia.

CONCLUSIONS

Quantitative plaque features obtained from CCTA, LDNCP, and CD, are associated with ischemia by MPI independent of stenosis. LDNCP burden and CD are associated with ischemia in stenosis 30-69% and ≥ 70%, respectively.

Cardiac imaging: working towards fully-automated machine analysis & interpretation

INTRODUCTION

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Functional Evaluation of Coronary Disease by CT Angiography

In recent years, several technical developments in the field of cardiac computed tomography (CT) have made possible the extraction of functional information from an anatomy-based examination. Several different lines have been explored and will be reviewed in the present paper, namely: 1) myocardial perfusion imaging; 2) transluminal attenuation gradients and corrected coronary opacification indexes; 3) fractional flow reserve computed from CT; and 4) extrapolation from atherosclerotic plaque characteristics. In view of these developments, cardiac CT has the potential to become in the near future a truly 2-in-1 noninvasive evaluation for coronary artery disease.

Coronary CT angiography derived morphological and functional quantitative plaque markers correlated with invasive fractional flow reserve for detecting hemodynamically significant stenosis

OBJECTIVE

Compare morphological and functional coronary plaque markers derived from coronary CT angiography (CCTA) for their ability to detect lesion-specific ischemia.

MATERIALS AND METHODS

Data of patients who had undergone both dual-source CCTA and invasive fractional flow reserve (FFR) measurement within 3 months were retrospectively analyzed. Various quantitative stenosis markers were derived from CCTA: Corrected coronary opacification (CCO), transluminal attenuation gradient (TAG), remodeling index (RI), computational FFR (cFFR), lesion length (LL), vessel volume (VV), total plaque volume (TPV), and calcified and non-calcified plaque volume (CPV and NCPV). Discriminatory power of these markers for flow-limiting versus non-significant coronary stenosis was assessed against invasive FFR as the reference standard.

RESULTS

The cohort included 37 patients (61 ± 12 years, 68% male). Among 37 lesions, 11 were hemodynamically significant by FFR. On a per-lesion level, sensitivity and specificity of TPV, CPV, and NCPV for hemodynamically significant stenosis detection were 88% and 74%, 67% and 53%, and 92% and 81%, respectively. For CCO, TAG, RI, and cFFR these were 64% and 86%, 35% and 56%, 82% and 54%, and 100% and 90%, respectively. At ROC analysis, only TPV (0.78, p = 0.013), NCPV (0.79, p = 0.009), cFFR (0.85, p = 0.003), and CCO (0.82, p = 0.0003) showed discriminatory power for detecting hemodynamically significant stenosis.

CONCLUSION

TPV, NCPV, CCO, and cFFR derived from CCTA can aid detecting hemodynamically significant coronary lesions with cFFR showing the greatest discriminatory ability.

A Preoperative Assessment of Significant Coronary Stenosis Based on a Semiquantitative Analysis of Coronary Artery Calcification on Noncontrast Computed Tomography in Aortic Stenosis Patients Undergoing Aortic Valve Replacement

Invasive coronary angiography (ICA) is the recommended assessment for coronary artery disease in patients undergoing elective aortic valve replacement (AVR). Noncontrast computed tomography (CT) is useful for evaluating lung lesions and calcifications at the cannulation site of the ascending aorta. The purpose of this study was to evaluate the role of noncontrast CT in the visual assessment of coronary artery calcification (CAC) in patients undergoing AVR. We retrospectively identified patients with significant aortic stenosis (AS) who were referred for AVR between January 2006 and December 2013. Among these, we included 386 patients (53.6% males, 69.2 ± 8.4 years) who underwent both noncontrast CT and ICA. Significant coronary artery stenosis (CAS) in the ICA was defined as luminal stenosis ≥70%. The 4 main coronary arteries were visually assessed on noncontrast CT and were scored based on the Weston score as follows: 0, no visually detected calcium; 1, a single high-density pixel detected; 3, calcium was dense enough to create a blooming artifact; and 2, calcium in between 1 and 3. Four groups were reclassified by the sum of the Weston scores from each vessel, as follows: noncalcification (0); mild calcification (1-4); moderate calcification (5-8); and severe calcification (9-12). Receiver-operating characteristic (ROC) analysis was generated to identify the cutoff Weston score values for predicting significant CAS. Diagnostic estimates were calculated based on these cutoffs. In the ICA analysis, 62 of the 386 patients (16.1%) had significant CAS. All patients were divided into 4 groups. The noncalcification group had 97 subjects (Weston score 0), the mild degree group had 100 (2.6 ± 1.0), the moderate calcification group had 114 (6.6 ± 1.1), and the severe calcification group had 75 (10.7 ± 1.1). The prevalence of significant CAS in the noncalcification, mild, moderate, and severe groups was 1% (1/97), 5% (5/100), 24% (27/114), and 39% (29/75), respectively. The group with CAS had significantly more CAC than the group without CAS (8.37 ± 2.93 vs 4.01 ± 3.75, P < 0.001). The cutoff value (by Weston score) for predicting significant CAS is ≥5 (sensitivity 90.3%, specificity 59.0%, positive predictive value 29.6%, and negative predictive value 97%). The degree of CAC detected on noncontrast CT can help to predict significant CAS in AS patients who are referred for AVR. For the clinicians, the visual assessment of CAC on noncontrast CT was easy and useful for estimating CAS. Therefore, ICA should be recommended to selective patients based on patients' CAC and Weston scores during the preoperative evaluation for elective AVR.

Quantitative coronary CT angiography: absolute lumen sizing rather than %stenosis predicts hemodynamically relevant stenosis

Objective

To identify the most accurate quantitative coronary stenosis parameter by CTA for prediction of functional significant coronary stenosis resulting in coronary revascularization.

Methods

160 consecutive patients were prospectively examined with CTA. Proximal coronary stenosis was quantified by minimal lumen area (MLA) and minimal lumen diameter (MLD), %area and %diameter stenosis. Lesion length (LL) was measured. The reference standard was invasive coronary angiography (ICA) (>70 % stenosis, FFR <0.8).

Results

210 coronary segments were included (59 % positive). MLA of ≤1.8 mm² was identified as the optimal cut-off (c = 0.97, p < 0.001; 95 % CI 0.94–0.99) (sensitivity 90.9 %, specificity 89.3 %) for prediction of functional-relevant stenosis (for MLA >2.1 mm² sensitivity was 100 %). The optimal cut-off for MLD was 1.2 mm (c = 0.92; p < 0.001; 95 % CI 0.88–95) (sensitivity 90.9, specificity 85.2) while %area and %diameter stenosis were less accurate (c = 0.89; 95 % CI 0.84–93, c = 0.87; 95 % CI 0.82–92, respectively, with thresholds at 73 % and 61 % stenosis). Accuracy for LL was c = 0.74 (95 % CI 0.67–81), and for LL/MLA and LL/MLD ratio c = 0.90 and c = 0.84.

Conclusions

MLA ≤1.8 mm² and MLD ≤1.2 mm are the most accurate cut-offs for prediction of haemodynamically significant stenosis by ICA, with a higher accuracy than relative % stenosis.

Key Points

• Quantitative coronary CT-angiography is accurate for prediction of functional relevant stenosis.

• Absolute lumen area and diameter rather than %stenosis predict functional relevance.

• Lumen area <1.8 mm²and diameter <1.2 mm are the most accurate cut-offs.

• Quantitative parameters are helpful for decision-making in terms of patient management.