Machine learning to predict high-risk coronary artery disease on CT in the SCOT-HEART trial.

Background: Machine learning based on clinical characteristics has the potential to predict coronary CT angiography (CCTA) findings and help guide resource utilisation.

Methods: From the SCOT-HEART (Scottish Computed Tomography of the HEART) trial, data from 1769 patients was used to train and to test machine learning models (XGBoost, 10-fold cross validation, grid search hyperparameter selection). Two models were separately generated to predict the presence of coronary artery disease (CAD) and an increased burden of low-attenuation coronary artery plaque (LAP) using symptoms, demographic and clinical characteristics, electrocardiography and exercise tolerance testing (ETT).

General Purpose Deep Learning Attenuation Correction Improves Diagnostic Accuracy of SPECT MPI: A Multicenter Study.

Background: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) uses computed tomography (CT)-based attenuation correction (AC) to improve diagnostic accuracy. Deep learning (DL) has the potential to generate synthetic AC images, as an alternative to CT-based AC.

Objectives: This study evaluated whether DL-generated synthetic SPECT images could enhance accuracy of conventional SPECT MPI.

The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET(REFINE PET): Rationale and Design.

Background: The REgistry of Flow and Perfusion Imaging for Artificial Intelligence with PET (REFINE PET) was established to collect multicenter PET and associated computed tomography (CT) images, together with clinical data and outcomes, into a comprehensive research resource. REFINE PET will enable validation and development of both standard and novel cardiac PET/CT processing methods.

Methods: REFINE PET is a multicenter, international registry that contains both clinical and imaging data.

Incremental diagnostic value of AI-derived coronary artery calcium in F-flurpiridaz PET Myocardial Perfusion Imaging.

Background: Positron Emission Tomography (PET) myocardial perfusion imaging (MPI) is a powerful tool for predicting coronary artery disease (CAD). Coronary artery calcium (CAC) provides incremental risk stratification to PET-MPI and enhances diagnostic accuracy. We assessed additive value of CAC score, derived from PET/CT attenuation maps to stress TPD results using the novel 18F-flurpiridaz tracer in detecting significant CAD.

The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET): Rationale and Design.

Rationale: The REgistry of Flow and Perfusion Imaging for Artificial INtelligEnce with PET (REFINE PET) was established to aggregate PET and associated computed tomography (CT) images with clinical data from hospitals around the world into one comprehensive research resource.

Methods: REFINE PET is a multicenter, international registry that contains both clinical and imaging data. The PET scans were processed using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA), while the CT scans were processed using deep learning (DL) to detect coronary artery calcium (CAC).

AI-Derived Splenic Response in Cardiac PET Predicts Mortality: A Multi-Site Study.

Background: Inadequate pharmacologic stress may limit the diagnostic and prognostic accuracy of myocardial perfusion imaging (MPI). The splenic ratio (SR), a measure of stress adequacy, has emerged as a potential imaging biomarker.

Objectives: To evaluate the prognostic value of artificial intelligence (AI)-derived SR in a large multicenter Rb-PET cohort undergoing regadenoson stress testing.

The effects of low-dose colchicine on the progression of aortic valve stenosis: Rationale, design, and baseline characteristics of the Colchicine and Inflammation in Aortic Stenosis (CHIANTI) trial.

Background: Aortic valve stenosis (AS) is one of the most common valvular heart diseases worldwide. Its prevalence increases with age and is expected to rise further as the population ages. Untreated severe AS carries a 2-year mortality rate exceeding 50%.

Dynamic frame-by-frame motion correction for F-flurpiridaz PET-MPI using convolution neural network.

Purpose: Precise quantification of myocardial blood flow (MBF) and flow reserve (MFR) in F-flurpiridaz PET significantly relies on motion correction (MC). However, the manual frame-by-frame correction leads to significant inter-observer variability, time-consuming, and requires significant experience. We propose a deep learning (DL) framework for automatic MC of F-flurpiridaz PET.

Multicenter Evaluation of Interpretable AI for Coronary Artery Disease Diagnosis from PET Biomarkers.

Background: Positron emission tomography (PET)/CT for myocardial perfusion imaging (MPI) provides multiple imaging biomarkers, often evaluated separately. We developed an artificial intelligence (AI) model integrating key clinical PET MPI parameters to improve the diagnosis of obstructive coronary artery disease (CAD).

Methods: From 17,348 patients undergoing cardiac PET/CT across four sites, we retrospectively enrolled 1,664 subjects who had invasive coronary angiography within 180 days and no prior CAD.

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study.

Background: Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for all-cause mortality prediction.

Methods: This study included 27039 consecutive patients [57% male] with MPI scans from nine sites.

AI-based identification of patients who benefit from revascularization: a multicenter study.

Background And Aims: Revascularization in stable coronary artery disease often relies on ischemia severity, but we introduce an AI-driven approach that uses clinical and imaging data to estimate individualized treatment effects and guide personalized decisions.

Methods: Using a large, international registry from 13 centers, we developed an AI model to estimate individual treatment effects by simulating outcomes under alternative therapeutic strategies. The model was trained on an internal cohort constructed using 1:1 propensity score matching to emulate randomized controlled trials (RCTs), creating balanced patient pairs in which only the treatment strategy-early revascularization (defined as any procedure within 90 days of MPI) versus medical therapy-differed.

Pericoronary Adipose Tissue Attenuation in Patients with Future Acute Coronary Syndromes: The ICONIC Study.

Purpose Pericoronary adipose tissue attenuation (PCATa) measured at coronary CT angiography (CCTA) is an imaging biomarker of coronary inflammation associated with long-term adverse cardiac events. The authors hypothesized that PCATa may independently identify patients at risk for acute coronary syndromes (ACS). Materials and Methods The authors performed a retrospective substudy of the Incident Coronary Syndromes Identified by Computed Tomography (ICONIC) study, a propensity-matched case-control study of patients with CCTA followed by ACS.

Coronary inflammation and atherosclerosis by CCTA in young adults (aged 18-45).

Background: Peri-coronary adipose tissue attenuation (PCAT), a marker of coronary inflammation, is linked to coronary artery disease (CAD), but data on the role of PCAT in young individuals are limited.

Aims: This study explores the interplay of PCAT and CAD by CCTA, in a symptomatic young cohort.

Methods And Results: Patients aged 18-45 years without prior CAD, from Montefiore CCTA registry (2016-2022), were studied retrospectively.

Roadmap analysis for coronary artery stenosis detection and percutaneous coronary intervention prediction in cardiac CT for transcatheter aortic valve replacement.

Background: The new artificial intelligence-based software, Roadmap (HeartFlow), may assist in evaluating coronary artery stenosis during cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR).

Methods: Consecutive TAVR candidates who underwent both cardiac CT angiography (CTA) and invasive coronary angiography were enrolled. We evaluated the ability of three methods to predict obstructive coronary artery disease (CAD), defined as ≥50 ​% stenosis on quantitative coronary angiography (QCA), and the need for percutaneous coronary intervention (PCI) within one year: Roadmap, clinician CT specialists with Roadmap, and CT specialists alone.

Rb positron emission tomography myocardial blood flow quantification: Influence of arterial input curve quality on diagnostic accuracy.

Background: We investigated whether the shape of arterial blood input curves affects the diagnostic performance of myocardial blood flow (MBF) on rubidium-82 (Rb) positron emission tomography (PET) myocardial perfusion imaging (MPI) for obstructive coronary artery disease (CAD).

Methods And Results: We retrospectively enrolled 386 patients without prior CAD who underwent Rb PET-MPI and invasive coronary angiography within 6 months, from 2010 to 2018. Abnormal shapes of stress left atrial blood pool (BP) time activity curve were characterized into five categories based on visual/quantitative assessment: (1) low stress/rest peak ratio (SRPR), (2) slow activity rise, (3) slow activity decline, (4) broad peak and (5) multiple peaks.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study.

Background: CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.

Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues.

Reassessing Cardiovascular Risk in Patients With Peripheral Artery Disease Undergoing Myocardial Perfusion Imaging.

Background: Coronary artery disease (CAD) and peripheral artery disease (PAD) are often regarded as analogous risk factors for major adverse cardiovascular events (MACE), given their shared pathophysiology. We aimed to investigate whether the elevated MACE risk in PAD is driven by myocardial perfusion abnormalities or through other PAD-specific mediators.

Methods: We analyzed 45,252 patients from an international, multicentre registry who underwent SPECT myocardial perfusion imaging, excluding those with early coronary revascularization (< 90 days).

Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.

Background: Computed tomography (CT) attenuation correction scans are an intrinsic part of positron emission tomography (PET) myocardial perfusion imaging using PET/CT, but anatomic information is rarely derived from these ultralow-dose CT scans. We aimed to assess the association between deep learning-derived cardiac chamber volumes (right atrial, right ventricular, left ventricular, and left atrial) and mass (left ventricular) from these scans with myocardial flow reserve and heart failure hospitalization.

Methods: We included 18 079 patients with cardiac PET/CT from 6 sites.

Characterization of Aortic Valve Stenosis by CT Angiography in a Diverse US Cohort.

Background: Aortic stenosis (AS) involves calcific and fibrotic degeneration of the valve tissue. The only noninvasive method for evaluating both processes is contrast-enhanced computed tomography angiography. We aimed to explore the differences in aortic valve (AV) tissue composition across sex, race/ethnicity, and AS hemodynamic phenotype in US patients referred for transcatheter AV replacement planning.

Association of medical therapies with survival according to SPECT MPI findings.

Background: Myocardial perfusion imaging (MPI) results in downstream changes to medication prescription. While the benefits of medical therapy for coronary artery disease (CAD) are established, how this varies with MPI findings is unknown. Our goal was to evaluate the association of medical therapy with survival among patients undergoing MPI, including differential associations as a function of imaging findings.

Hybrid strategy of coronary atherosclerosis characterization with T1-weighted MRI and CT angiography to non-invasively predict periprocedural myocardial injury.

Aims: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predict periprocedural myocardial injury (PMI) after percutaneous coronary intervention (PCI). We aimed to investigate whether integrating MRI with CCTA, using the latest imaging and quantitative techniques, improves PMI prediction and to explore a potential hybrid CCTA-MRI strategy.

Methods And Results: This prospective, multi-centre study conducted coronary atherosclerosis T1-weighted characterization MRI for patients scheduled for elective PCI for an atherosclerotic lesion detected on CCTA without prior revascularization.

Effects of Evolocumab on Coronary Plaque Composition and Microcalcification Activity by Coronary PET and CT Angiography.

Background: The effects of evolocumab on the underlying coronary disease activity by positron emission tomography (PET) and coronary tree plaque composition by coronary computed tomography angiography (CTA) have not been described.

Objectives: This prospective imaging study aimed to evaluate changes in coronary plaque composition on coronary CTA and coronary microcalcification, a marker of plaque activity, on F-sodium fluoride (NaF) positron emission tomography (PET) after evolocumab treatment.

Methods: This single-arm, prospective, open-label study enrolled patients with baseline extensive noncalcified plaque volume by coronary CTA (>440 µL overall coronary artery or >250 µL in any single plaque).