Deep learning-enabled coronary CT angiography for plaque and stenosis quantification and cardiac risk prediction: an international multicentre study

BACKGROUND: Atherosclerotic plaque quantification from coronary CT angiography (CCTA) enables accurate assessment of coronary artery disease burden and prognosis. We sought to develop and validate a deep learning system for CCTA-derived measures of plaque volume and stenosis severity. METHODS: This international, multicentre study included nine cohorts of patients undergoing CCTA at 11 sites, who were assigned into training and test sets. Data were retrospectively collected on patients with a wide range of clinical presentations of coronary artery disease who underwent CCTA between Nov 18, 2010, and Jan 25, 2019. A novel deep learning convolutional neural network was trained to segment coronary plaque in 921 patients (5045 lesions). The deep learning network was then applied to an independent test set, which included an external validation cohort of 175 patients (1081 lesions) and 50 patients (84 lesions) assessed by intravascular ultrasound within 1 month of CCTA. We evaluated the prognostic value of deep learning-based plaque measurements for fatal or non-fatal myocardial infarction (our primary outcome) in 1611 patients from the prospective SCOT-HEART trial, assessed as dichotomous variables using multivariable Cox regression analysis, with adjustment for the ASSIGN clinical risk score. FINDINGS: In the overall test set, there was excellent or good agreement, respectively, between deep learning and expert reader measurements of total plaque volume (intraclass correlation coefficient [ICC] 0·964) and percent diameter stenosis (ICC 0·879; both p<0·0001). When compared with intravascular ultrasound, there was excellent agreement for deep learning total plaque volume (ICC 0·949) and minimal luminal area (ICC 0·904). The mean per-patient deep learning plaque analysis time was 5·65 s (SD 1·87) versus 25·66 min (6·79) taken by experts. Over a median follow-up of 4·7 years (IQR 4·0–5·7), myocardial infarction occurred in 41 (2·5%) of 1611 patients from the SCOT-HEART trial. A deep learning-based total plaque volume of 238·5 mm(3) or higher was associated with an increased risk of myocardial infarction (hazard ratio [HR] 5·36, 95% CI 1·70–16·86; p=0·0042) after adjustment for the presence of deep learning-based obstructive stenosis (HR 2·49, 1·07–5·50; p=0·0089) and the ASSIGN clinical risk score (HR 1·01, 0·99–1·04; p=0·35). INTERPRETATION: Our novel, externally validated deep learning system provides rapid measurements of plaque volume and stenosis severity from CCTA that agree closely with expert readers and intravascular ultrasound, and could have prognostic value for future myocardial infarction

Implication of thoracic aortic calcification over coronary calcium score regarding the 2018 ACC/AHA Multisociety cholesterol guideline: results from the CAC Consortium.

ObjectiveTAC is associated with an increased atherosclerotic cardiovascular disease (ASCVD) risk, but it is unclear how to interpret thoracic aortic calcification (TAC) findings in conjunction with ASCVD risk and coronary artery calcium (CAC) score according to 2018 ACC/AHA Multisociety cholesterol guidelines. We evaluate the incremental value of thoracic aortic calcification TAC over CAC for predicting and reclassifying ASCVD mortality risk.MethodThe study included 30,630 asymptomatic individuals (mean age: 55 ± 8 years, male: 64%) from the CAC Consortium. TAC was categorized as TAC 0, 1-300, and &gt;300. Patients were categorized as low (&lt;5%), borderline (5-7.5%), intermediate (7.5-20%), or high (≥20%) 10-year ASCVD risk according to the Pooled Cohorts Equation. In the intermediate risk group, the utility of TAC beyond CAC for statin eligibility was assessed according to the guideline. CAC was categorized as CAC=0 (no statin), CAC 1-100 (favors statin), or CAC&gt;100 (initiate stain).ResultsDuring the median 11.2 years (IQR 9.2-12.4) follow-up, 345 (1.1%) CVD deaths occurred. TAC&gt;300 was associated with increased CVD mortality after adjusting for ASCVD risk and CAC (HR:4.72, 95% CI: 3.39-6.57, p&lt;0.001). In borderline and intermediate risk groups, TAC improved discrimination when added to a model included ASCVD risk and CAC (C-statistic: 0.77 vs. 0.68 in borderline group; 0.67 vs. 0.63 in intermediate group, both p&nbsp;&lt;&nbsp;0.05). The addition of TAC over CAC improved risk reclassification in borderline, intermediate and high-risk groups (categorical net reclassification index: 0.40, 0.29, and 0.49, respectively, all p&nbsp;&lt;&nbsp;0.001). Of intermediate risk participants for whom consideration of CAC was recommended based on the guideline, TAC &gt;300 was associated with an increased CVD mortality risk across each statin eligibility group (all p&nbsp;&lt;&nbsp;0.001, compared to TAC 0).ConclusionTAC was independently associated with CVD death. Among individuals with borderline or intermediate ASCVD risk, a TAC threshold of 300 may provide added prognostic and reclassification value beyond the current guideline-based approach

Sex differences in computed tomography angiography-derived coronary plaque burden in relation to invasive fractional flow reserve

Background: Distinct sex-related differences exist in coronary artery plaque burden and distribution. We aimed to explore sex differences in quantitative plaque burden by coronary CT angiography (CCTA) in relation to ischemia by invasive fractional flow reserve (FFR). Methods: This post-hoc analysis of the PACIFIC trial included 581 vessels in 203 patients (mean age 58.1 ​± ​8.7 years, 63.5% male) who underwent CCTA and per-vessel invasive FFR. Quantitative assessment of total, calcified, non-calcified, and low-density non-calcified plaque burden were performed using semiautomated software. Significant ischemia was defined as invasive FFR ≤0.8. Results: The per-vessel frequency of ischemia was higher in men than women (33.5% vs. 7.5%, p ​< ​0.001). Women had a smaller burden of all plaque subtypes (all p ​< ​0.01). There was no sex difference on total, calcified, or non-calcified plaque burdens in vessels with ischemia; only low-density non-calcified plaque burden was significantly lower in women (beta: -0.183, p ​= ​0.035). The burdens of all plaque subtypes were independently associated with ischemia in both men and women (For total plaque burden (5% increase): Men, OR: 1.15, 95%CI: 1.06–1.24, p ​= ​0.001; Women, OR: 1.96, 95%CI: 1.11–3.46, p ​= ​0.02). No significant interaction existed between sex and total plaque burden for predicting ischemia (interaction p ​= ​0.108). The addition of quantitative plaque burdens to stenosis severity and adverse plaque characteristics improved the discrimination of ischemia in both men and women. Conclusions: In symptomatic patients with suspected CAD, women have a lower CCTA-derived burden of all plaque subtypes compared to men. Quantitative plaque burden provides independent and incremental predictive value for ischemia, irrespective of sex

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]H 2O 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 (FFR CT). 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 FFR CT(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 FFR CT(area under the receiver-operating characteristic curve 0.77; P=0.16)

Relationship between impaired myocardial blood flow by positron emission tomography and low-attenuation plaque burden and pericoronary adipose tissue attenuation from coronary computed tomography: From the prospective PACIFIC trial

Background: Positron emission tomography (PET) is the clinical gold standard for quantifying myocardial blood flow (MBF). Pericoronary adipose tissue (PCAT) attenuation may detect vascular inflammation indirectly. We examined the relationship between MBF by PET and plaque burden and PCAT on coronary CT angiography (CCTA). Methods: This post hoc analysis of the PACIFIC trial included 208 patients with suspected coronary artery disease (CAD) who underwent [15O]H2O PET and CCTA. Low-attenuation plaque (LAP, 0.1). Conclusion: In patients with stable CAD, LAP burden was independently associated with impaired hyperemic MBF and a stronger predictor of impaired hyperemic MBF than NCP burden. There was no association between PCAT attenuation and hyperemic MBF