Assessment of atherosclerotic plaque burden: comparison of AI-QCT versus SIS, CAC, visual and CAD-RADS stenosis categories.

This study assesses the agreement of Artificial Intelligence-Quantitative Computed Tomography (AI-QCT) with qualitative approaches to atherosclerotic disease burden codified in the multisociety 2022 CAD-RADS 2.0 Expert Consensus. 105 patients who underwent cardiac computed tomography angiography (CCTA) for chest pain were evaluated by a blinded core laboratory through FDA-cleared software (Cleerly, Denver, CO) that performs AI-QCT through artificial intelligence, analyzing factors such as % stenosis, plaque volume, and plaque composition. AI-QCT plaque volume was then staged by recently validated prognostic thresholds, and compared with CAD-RADS 2.0 clinical methods of plaque evaluation (segment involvement score (SIS), coronary artery calcium score (CACS), visual assessment, and CAD-RADS percent (%) stenosis) by expert consensus blinded to the AI-QCT core lab reads. Average age of subjects were 59 ± 11 years; 44% women, with 50% of patients at CAD-RADS 1-2 and 21% at CAD-RADS 3 and above by expert consensus. AI-QCT quantitative plaque burden staging had excellent agreement of 93% (k = 0.87 95% CI: 0.79-0.96) with SIS. There was moderate agreement between AI-QCT quantitative plaque volume and categories of visual assessment (64.4%; k = 0.488 [0.38-0.60]), and CACS (66.3%; k = 0.488 [0.36-0.61]). Agreement between AI-QCT plaque volume stage and CAD-RADS % stenosis category was also moderate. There was discordance at small plaque volumes. With ongoing validation, these results demonstrate a potential for AI-QCT as a rapid, reproducible approach to quantify total plaque burden.

Development and Validation of a Quantitative Coronary CT Angiography Model for Diagnosis of Vessel-Specific Coronary Ischemia.

BACKGROUND: Noninvasive stress testing is commonly used for detection of coronary ischemia but possesses variable accuracy and may result in excessive health care costs. OBJECTIVES: This study aimed to derive and validate an artificial intelligence-guided quantitative coronary computed tomography angiography (AI-QCT) model for the diagnosis of coronary ischemia that integrates atherosclerosis and vascular morphology measures (AI-QCTISCHEMIA) and to evaluate its prognostic utility for major adverse cardiovascular events (MACE). METHODS: A post hoc analysis of the CREDENCE (Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia) and PACIFIC-1 (Comparison of Coronary Computed Tomography Angiography, Single Photon Emission Computed Tomography [SPECT], Positron Emission Tomography [PET], and Hybrid Imaging for Diagnosis of Ischemic Heart Disease Determined by Fractional Flow Reserve) studies was performed. In both studies, symptomatic patients with suspected stable coronary artery disease had prospectively undergone coronary computed tomography angiography (CTA), myocardial perfusion imaging (MPI), SPECT, or PET, fractional flow reserve by CT (FFRCT), and invasive coronary angiography in conjunction with invasive FFR measurements. The AI-QCTISCHEMIA model was developed in the derivation cohort of the CREDENCE study, and its diagnostic performance for coronary ischemia (FFR ≤0.80) was evaluated in the CREDENCE validation cohort and PACIFIC-1. Its prognostic value was investigated in PACIFIC-1. RESULTS: In CREDENCE validation (n = 305, age 64.4 ± 9.8 years, 210 [69%] male), the diagnostic performance by area under the receiver-operating characteristics curve (AUC) on per-patient level was 0.80 (95% CI: 0.75-0.85) for AI-QCTISCHEMIA, 0.69 (95% CI: 0.63-0.74; P < 0.001) for FFRCT, and 0.65 (95% CI: 0.59-0.71; P < 0.001) for MPI. In PACIFIC-1 (n = 208, age 58.1 ± 8.7 years, 132 [63%] male), the AUCs were 0.85 (95% CI: 0.79-0.91) for AI-QCTISCHEMIA, 0.78 (95% CI: 0.72-0.84; P = 0.037) for FFRCT, 0.89 (95% CI: 0.84-0.93; P = 0.262) for PET, and 0.72 (95% CI: 0.67-0.78; P < 0.001) for SPECT. Adjusted for clinical risk factors and coronary CTA-determined obstructive stenosis, a positive AI-QCTISCHEMIA test was associated with an HR of 7.6 (95% CI: 1.2-47.0; P = 0.030) for MACE. CONCLUSIONS: This newly developed coronary CTA-based ischemia model using coronary atherosclerosis and vascular morphology characteristics accurately diagnoses coronary ischemia by invasive FFR and provides robust prognostic utility for MACE beyond presence of stenosis.

Recovery Rates of Diagnostic Cardiac Procedural Volume in Oceania 1 Year Into COVID-19: The IAEA Non-Invasive Cardiology Protocol Survey on COVID-19 (INCAPS COVID 2).

AIM: The aim of this study was to assess the recovery rates of diagnostic cardiac procedure volumes in the Oceania Region, midway through the coronavirus disease 2019 (COVID-19) pandemic. METHODS: A survey was performed comparing procedure volumes between March 2019 (pre-pandemic), April 2020 (during first wave of COVID-19 pandemic), and April 2021 (1 year into the COVID-19 pandemic). A total of 31 health care facilities within Oceania that perform cardiac diagnostic procedures were surveyed, including a mixture of metropolitan and regional, hospital and outpatient, public and private sites, as well as teaching and non-teaching hospitals. A comparison was made with 549 centres in 96 countries in the rest of the world (RoW) outside of Oceania. The total number and median percentage change in procedure volume were measured between the three timepoints, compared by test type and by facility. RESULTS: A total of 11,902 cardiac diagnostic procedures were performed in Oceania in April 2021 as compared with 11,835 pre-pandemic in March 2019 and 5,986 in April 2020; whereas, in the RoW, 499,079 procedures were performed in April 2021 compared with 497,615 pre-pandemic in March 2019 and 179,014 in April 2020. There was no significant difference in the median recovery rates for total procedure volumes between Oceania (-6%) and the RoW (-3%) (p=0.81). While there was no statistically significant difference in percentage recovery been functional ischaemia testing and anatomical coronary testing in Oceania as compared with the RoW, there was, however, a suggestion of poorer recovery in anatomical coronary testing in Oceania as compared with the RoW (CT coronary angiography -16% in Oceania vs -1% in RoW, and invasive coronary angiography -20% in Oceania vs -9% in RoW). There was no statistically significant difference in recovery rates in procedure volume between metropolitan vs regional (p=0.44), public vs private (p=0.92), hospital vs outpatient (p=0.79), or teaching vs non-teaching centres (p=0.73). CONCLUSIONS: Total cardiology procedure volumes in Oceania normalised 1 year post-pandemic compared to pre-pandemic levels, with no significant difference compared with the RoW and between the different types of health care facilities.

Disparities in Noninvasive Traditional and Advanced Testing for Coronary Artery Disease: Findings from the INCAPS-COVID 2 Study.

The COVID-19 pandemic disrupted the delivery of cardiovascular care, including noninvasive testing protocols and test selection for the evaluation of coronary artery disease (CAD). Trends in test selection in traditional versus advanced noninvasive tests for CAD during the pandemic and in countries of varying income status have not been well studied. The International Atomic Energy Agency conducted a global survey to assess the pandemic-related changes in the practice of cardiovascular diagnostic testing. Site procedural volumes for noninvasive tests to evaluate CAD from March 2019 (prepandemic), April 2020 (onset), and April 2021 (initial recovery) were collected. We considered traditional testing modalities, such as exercise electrocardiography, stress echocardiography, and stress single-photon emission computed tomography, and advanced testing modalities, such as stress cardiac magnetic resonance, coronary computed tomography angiography, and stress positron emission tomography. Survey data were obtained from 669 centers in 107 countries, reporting the performance of 367,933 studies for CAD during the study period. Compared with 2019, traditional tests were performed 14% less frequently (recovery rate 82%) in 2021 versus advanced tests, which were performed 15% more frequently (128% recovery rate). Coronary computed tomography angiography, stress cardiac magnetic resonance, and stress positron emission tomography showed 14%, 25%, and 25% increases in volumes from 2019 to 2021, respectively. The increase in advanced testing was isolated to high- and upper middle-income countries, with 132% recovery in advanced tests by 2021 compared with 55% in lower income nations. The COVID-19 pandemic exacerbated economic disparities in CAD testing practice between wealthy and poorer countries. Greater recovery rates and even new growth were observed for advanced imaging modalities; however, this growth was restricted to wealthy countries. Efforts to reduce practice variations in CAD testing because of economic status are warranted.

Impact of Atherosclerosis Imaging-Quantitative Computed Tomography on Diagnostic Certainty, Downstream Testing, Coronary Revascularization and Medical Therapy: The CERTAIN Study.

AIMS: The incremental impact of Atherosclerosis Imaging-Quantitative Computed Tomography (AI-QCT) on diagnostic certainty and downstream patient management is not yet known. The aim of the present study was to compare the clinical utility of routine implementation of AI-QCT versus conventional visual coronary CT angiography (CCTA) interpretation. METHODS AND RESULTS: In this multicenter crossover study in 5 expert CCTA sites, 750 consecutive adult patients referred for CCTA were prospectively recruited. Blinded to the AI-QCT analysis, site physicians established patient diagnosis and plans for downstream non-invasive testing, coronary intervention and medication management based on the conventional site assessment. Next, physicians were asked to repeat their assessments based upon AI-QCT results. The included patients had an age of 63.8 ± 12.2 years, 433 (57.7%) were male. Compared to conventional site CCTA evaluation, AI-QCT analysis improved physician's confidence 2-5-fold at every step of the care pathway and was associated with change in diagnosis or management in the majority of patients (428; 57.1%; p < 0.001), including for such measures as Coronary Artery Disease-Reporting and Data System (CAD-RADS) (295; 39.3%; p < 0.001) and plaque burden (197; 26.3%; p < 0.001). After AI-QCT including ischemia assessment, the need for downstream non-invasive and invasive testing was reduced by 37.1% (p < 0.001), compared with the conventional site CCTA evaluation. Incremental to the site CCTA evaluation alone, AI-QCT resulted in statin initiation/increase an aspirin initiation in an additional 28.1% (p < 0.001) and 23.0% (p < 0.001) of patients, respectively. CONCLUSIONS: Use of AI-QCT improves diagnostic certainty, and may result in reduced downstream need for non-invasive testing and increased rates of preventive medical therapy.

Rationale and design of the CONFIRM2 (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) study.

BACKGROUND: In the last 15 years, large registries and several randomized clinical trials have demonstrated the diagnostic and prognostic value of coronary computed tomography angiography (CCTA). Advances in CT scanner technology and developments of analytic tools now enable accurate quantification of coronary artery disease (CAD), including total coronary plaque volume and low attenuation plaque volume. The primary aim of CONFIRM2, (Quantitative COroNary CT Angiography Evaluation For Evaluation of Clinical Outcomes: An InteRnational, Multicenter Registry) is to perform comprehensive quantification of CCTA findings, including coronary, non-coronary cardiac, non-cardiac vascular, non-cardiac findings, and relate them to clinical variables and cardiovascular clinical outcomes. DESIGN: CONFIRM2 is a multicenter, international observational cohort study designed to evaluate multidimensional associations between quantitative phenotype of cardiovascular disease and future adverse clinical outcomes in subjects undergoing clinically indicated CCTA. The targeted population is heterogenous and includes patients undergoing CCTA for atherosclerotic evaluation, valvular heart disease, congenital heart disease or pre-procedural evaluation. Automated software will be utilized for quantification of coronary plaque, stenosis, vascular morphology and cardiac structures for rapid and reproducible tissue characterization. Up to 30,000 patients will be included from up to 50 international multi-continental clinical CCTA sites and followed for 3-4 years. SUMMARY: CONFIRM2 is one of the largest CCTA studies to establish the clinical value of a multiparametric approach to quantify the phenotype of cardiovascular disease by CCTA using automated imaging solutions.

AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD.

BACKGROUND: The recent development of artificial intelligence-guided quantitative coronary computed tomography angiography analysis (AI-QCT) has enabled rapid analysis of atherosclerotic plaque burden and characteristics. OBJECTIVES: This study set out to investigate the 10-year prognostic value of atherosclerotic burden derived from AI-QCT and to compare the spectrum of plaque to manually assessed coronary computed tomography angiography (CCTA), coronary artery calcium scoring (CACS), and clinical risk characteristics. METHODS: This was a long-term follow-up study of 536 patients referred for suspected coronary artery disease. CCTA scans were analyzed with AI-QCT and plaque burden was classified with a plaque staging system (stage 0: 0% percentage atheroma volume [PAV]; stage 1: >0%-5% PAV; stage 2: >5%-15% PAV; stage 3: >15% PAV). The primary major adverse cardiac event (MACE) outcome was a composite of nonfatal myocardial infarction, nonfatal stroke, coronary revascularization, and all-cause mortality. RESULTS: The mean age at baseline was 58.6 years and 297 patients (55%) were male. During a median follow-up of 10.3 years (IQR: 8.6-11.5 years), 114 patients (21%) experienced the primary outcome. Compared to stages 0 and 1, patients with stage 3 PAV and percentage of noncalcified plaque volume of >7.5% had a more than 3-fold (adjusted HR: 3.57; 95% CI 2.12-6.00; P < 0.001) and 4-fold (adjusted HR: 4.37; 95% CI: 2.51-7.62; P < 0.001) increased risk of MACE, respectively. Addition of AI-QCT improved a model with clinical risk factors and CACS at different time points during follow-up (10-year AUC: 0.82 [95% CI: 0.78-0.87] vs 0.73 [95% CI: 0.68-0.79]; P < 0.001; net reclassification improvement: 0.21 [95% CI: 0.09-0.38]). Furthermore, AI-QCT achieved an improved area under the curve compared to Coronary Artery Disease Reporting and Data System 2.0 (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.023) and manual QCT (10-year AUC: 0.78; 95% CI: 0.73-0.83; P = 0.040), although net reclassification improvement was modest (0.09 [95% CI: -0.02 to 0.29] and 0.04 [95% CI: -0.05 to 0.27], respectively). CONCLUSIONS: Through 10-year follow-up, AI-QCT plaque staging showed important prognostic value for MACE and showed additional discriminatory value over clinical risk factors, CACS, and manual guideline-recommended CCTA assessment.

Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

How early can atherosclerosis be detected by coronary CT angiography? Insights from quantitative CT analysis of serial scans in the PARADIGM trial.

BACKGROUND: Non-obstructing small coronary plaques may not be well recognized by expert readers during coronary computed tomography angiography (CCTA) evaluation. Recent developments in atherosclerosis imaging quantitative computed tomography (AI-QCT) enabled by machine learning allow for whole-heart coronary phenotyping of atherosclerosis, but its diagnostic role for detection of small plaques on CCTA is unknown. METHODS: We performed AI-QCT in patients who underwent serial CCTA in the multinational PARADIGM study. AI-QCT results were verified by a level III experienced reader, who was blinded to baseline and follow-up status of CCTA. This retrospective analysis aimed to characterize small plaques on baseline CCTA and evaluate their serial changes on follow-up imaging. Small plaques were defined as a total plaque volume <50 â€‹mm3. RESULTS: A total of 99 patients with 502 small plaques were included. The median total plaque volume was 6.8 â€‹mm3 (IQR 3.5-13.9 â€‹mm3), most of which was non-calcified (median 6.2 â€‹mm3; 2.9-12.3 â€‹mm3). The median age at the time of baseline CCTA was 61 years old and 63% were male. The mean interscan period was 3.8 â€‹± â€‹1.6 years. On follow-up CCTA, 437 (87%) plaques were present at the same location as small plaques on baseline CCTA; 72% were larger and 15% decreased in volume. The median total plaque volume and non-calcified plaque volume increased to 18.9 â€‹mm3 (IQR 8.3-45.2 â€‹mm3) and 13.8 â€‹mm3 (IQR 5.7-33.4 â€‹mm3), respectively, among plaques that persisted on follow-up CCTA. Small plaques no longer visualized on follow-up CCTA were significantly more likely to be of lower volume, shorter in length, non-calcified, and more distal in the coronary artery, as compared with plaques that persisted at follow-up. CONCLUSION: In this retrospective analysis from the PARADIGM study, small plaques (<50 â€‹mm3) identified by AI-QCT persisted at the same location and were often larger on follow-up CCTA.

Emerging Roles for Artificial Intelligence in Heart Failure Imaging.

Artificial intelligence (AI) applications are expanding in cardiac imaging. AI research has shown promise in workflow optimization, disease diagnosis, and integration of clinical and imaging data to predict patient outcomes. The diagnostic and prognostic paradigm of heart failure is heavily reliant on cardiac imaging. As AI becomes increasingly validated and integrated into clinical practice, AI influence on heart failure management will grow. This review discusses areas of current research and potential clinical applications in AI as applied to heart failure cardiac imaging.

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity.

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

Contemporary cardiovascular computed tomography (CCT) training: Serial surveys of the international CCT community by the Fellow and Resident Leaders of the Society of Cardiovascular Computed Tomography (SCCT) Committee (FiRST) and SCCT Future Leaders Program (FLP).

BACKGROUND: As cardiovascular computed tomography (CCT) practice evolves, the demand for specialists continues to increase. However, CCT training remains variable globally with limited contemporaneous data to understand this heterogeneity. We sought to understand the role of CCT globally and the training available to underpin its use. METHODS: We performed two consecutive surveys of cardiology and radiology physicians, two years apart, utilizing the Society of Cardiovascular Computed Tomography (SCCT) website, weblinks, social media platforms, and meeting handouts to maximize our response rate. We compared United States (US)-based vs. international responses to understand global similarities and differences in practice and training in the surveys. RESULTS: 235 respondents (37% trainees and 63% educators/non-trainees) initiated the first survey with 174 (74%) completing the core survey, with 205 providing their work location (114 US and 91 international). Eighty-four percent (92/110) of educator respondents stated a need for increased training opportunities to meet growing demand. Dedicated training fellowships are heterogenous, with limited access to structural heart imaging training, despite structural scanning being performed within institutions. The lack of a standardized curriculum was identified as the main obstacle to effective CCT learning, particularly in the US, with web-based learning platforms being the most popular option for improving access to CCT training. 148 trainees initiated the second survey with 107 (72%) completing the core components (51% North America, 49% international). Only 68% said they would be able to meet their required CCT education needs via their training program. Obstacles in obtaining CCT training again included a lack of a developed curriculum (51%), a lack of dedicated training time (35%), and a lack of local faculty expertise (31%). There was regional variability in access to CCT training, and, in contrast to the first survey, most (89%) felt 1:1 live review of cases with trained/expert reader was most useful for improving CCT training alongside formal curriculum/live lectures (72%). CONCLUSIONS: There is a need to expand dedicated CCT training globally to meet the demand for complex CCT practice. Access to CCT education (didactic and 1:1 case-based teaching from expert faculty), implementation of recently published global training curricula, and increased teaching resources (web-based) as an adjunct to existing experiential learning opportunities, are all deemed necessary to address current educational shortfalls.

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Cardiac computed tomographic imaging in cardio-oncology: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT). Endorsed by the International Cardio-Oncology Society (ICOS).

Cardio-Oncology is a rapidly growing sub-specialty of medicine, however, there is very limited guidance on the use of cardiac CT (CCT) in the care of Cardio-Oncology patients. In order to fill in the existing gaps, this Expert Consensus statement comprised of a multidisciplinary collaboration of experts in Cardiology, Radiology, Cardiovascular Multimodality Imaging, Cardio-Oncology, Oncology and Radiation Oncology aims to summarize current evidence for CCT applications in Cardio-Oncology and provide practice recommendations for clinicians.