Relationship between epicardial adipose tissue and coronary atherosclerosis by CCTA in young adults (18-45).

Objective: Epicardial adipose tissue (EAT) is implicated in the pathogenesis and progression of coronary artery disease (CAD). Limited data exists on the interplay between EAT and atherosclerosis in young individuals. Our study aims to explore the relationship between EAT and CAD in a young cohort. Methods: All young (18-45 years) patients without prior CAD, referred for coronary computed tomography angiography (CCTA) from 2016 to 2022 were included. EAT volume and coronary artery calcium (CAC) were calculated from dedicated non-contrast scans. Coronary plaque presence, extent, and volume were quantified from CCTA. Multivariable logistic regression models for the presence of CAD, defined as any coronary atherosclerosis, were performed. Results: Overall, 712 patients (39±4.8 years, 54 % female) with 45 % Hispanic, and 21 % non-Hispanic Black were included. Patients with CAD had higher EAT volume than those without (80.80 mL ± 36.00 vs 55.16 mL ± 27.92; P < 0.001). In those with CAC=0, higher EAT was associated with the presence of CAD compared to lower EAT volume (P < 0.001). An EAT volume >76 mL was associated with higher CAC (P < 0.001), segment involvement score (P < 0.001), and quantitative total, non-calcified, and low-attenuation plaque volumes (P < 0.002). At multivariable analysis, EAT volume (per 10 mL, OR: 1.21; 95 %CI: 1.12-1.30; P < 0.0001) was independently associated with the presence of CAD. Conclusion: In a diverse cohort of young adults without history of CAD and undergoing a clinically indicated CCTA, EAT volume was independently associated with the presence of CAD. Our findings highlight EAT potential as a novel marker for CAD risk-assessment and a potential therapeutic target in young patients.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study.

Background: Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized 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 evaluate these measures for all-cause mortality (ACM) risk stratification. Methods: We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves. Findings: End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001). Interpretations: CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value. Research in context: Evidence before this study: Fully automated volumetric body composition analysis of chest computed tomography attenuation correction (CTAC) can be obtained in patients undergoing myocardial perfusion imaging. This new information has potential to significantly improve risk stratification and patient management. However, the CTAC scans have not been utilized for body composition analysis to date. We searched PubMed and Google Scholar for existing body composition related literature on June 5, 2024, using the search term ("mortality") AND ("risk stratification" OR "survival analysis" OR "prognostic prediction" OR "prognosis") AND ("body composition quantification" OR "body composition analysis" OR "body composition segmentation"). We identified 34 articles either exploring body composition segmentation or evaluating clinical value of body composition quantification. However, to date, all the prognostic evaluation is performed for quantification of three or fewer types of body composition tissues. Within the prognostic studies, only one used chest CT scans but utilized only a few specified slices selected from the scans, and not a standardized volumetric analysis. None of these previous efforts utilized CTAC scans, and none included epicardial adipose tissue in comprehensive body composition analysis.Added value of this study: In this international multi-center study, we demonstrate a novel artificial intelligence-based annotation-free approach for segmenting six key body composition tissues (bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue, epicardial adipose tissue, and visceral adipose tissue) from low-dose ungated CTAC scans, by exploiting existing CT segmentation models and image processing techniques. We evaluate the prognostic value of metrics derived from volumetric quantification of CTAC scans obtained during cardiac imaging, for all-cause mortality prediction in a large cohort of patients. We reveal strong and independent associations between several volumetric body composition metrics and all-cause mortality after adjusting for existing clinical factors, and available cardiac perfusion and atherosclerosis biomarkers.Implications of all the available evidence: The comprehensive body composition analysis can be routinely performed, at the point of care, for all cardiac perfusion scans utilizing CTAC. Automatically-obtained volumetric body composition quantification metrics provide added value over existing risk factors, using already-obtained scans to significantly improve the risk stratification of patients and clinical decision-making.

Rapidly progressive coronary atherosclerosis in a young male: a retrospective advanced CCT phenotype analysis.

We present a real-life case of a very young man with multiple risk factors who progressed rapidly from minimally obstructive non-calcified plaque on computed tomography angiography (CCTA) to severe three-vessel coronary disease presenting with STEMI. It questions the reliability of zero coronary calcium in high-risk subgroups like familial hypercholesterolemia, high Lp(a), and the young. While CCTA can accurately visualize non-calcified plaque, its interpretation requires expertise and clinical judgment should consider both imaging and clinical risk factors for management. Advanced plaque quantification, peri-coronary (PCAT), and epicardial (EAT) adipose tissue could help better-stratified patients but the evidence-based clinical application remains unknown.

Deep Learning Phenotyping of Tricuspid Regurgitation for Automated High Throughput Assessment of Transthoracic Echocardiography.

Background and Aims: Diagnosis of tricuspid regurgitation (TR) requires careful expert evaluation. This study developed an automated deep learning pipeline for assessing TR from transthoracic echocardiography. Methods: An automated deep learning workflow was developed using 47,312 studies (2,079,898 videos) from Cedars-Sinai Medical Center (CSMC) between 2011 and 2021. The pipeline was tested on a temporally distinct test set of 2,462 studies (108,138 videos) obtained in 2022 at CSMC and a geographically distinct cohort of 5,549 studies (278,377 videos) from Stanford Healthcare (SHC). Results: In the CSMC test dataset, the view classifier demonstrated an AUC of 1.000 (0.999 - 1.000) and identified at least one A4C video with colour Doppler across the tricuspid valve in 2,410 of 2,462 studies with a sensitivity of 0.975 (0.968-0.982) and a specificity of 1.000 (1.00-1.000). In the CSMC test cohort, moderate-or-severe TR was detected with an AUC of 0.928 (0.913 - 0.943) and severe TR was detected with an AUC of 0.956 (0.940 - 0.969). In the SHC cohort, the view classifier correctly identified at least one TR colour Doppler video in 5,268 of the 5,549 studies, resulting in an AUC of 0.999 (0.998 - 0.999), a sensitivity of 0.949 (0.944 - 0.955) and specificity of 0.999 (0.999 - 0.999). The AI model detected moderate-or-severe TR with an AUC of 0.951 (0.938 - 0.962) and severe TR with an AUC of 0.980 (0.966 - 0.988). Conclusions: We developed an automated pipeline to identify clinically significant TR with excellent performance. This approach carries potential for automated TR detection and stratification for surveillance and screening. Key Question: Can an automated deep learning model assess tricuspid regurgitation severity from echocardiography? Key Finding: We developed and validated an automated tricuspid regurgitation detection algorithm pipeline across two healthcare systems with high volume echocardiography labs. The algorithm correctly identifies apical-4-chamber view videos with colour Doppler across the tricuspid valve and grades clinically significant TR with strong agreement to expert clinical readers. Take Home message: A deep learning pipeline could automate TR screening, facilitating reproducible accurate assessment of TR severity, allowing rapid triage or re-review and expand access in low-resource or primary care settings.

The lack of ethnic and racial diversity among patients undergoing coronary artery calcium scanning.

BACKGROUND: While coronary artery calcium (CAC) CAC scanning has become increasingly used as a tool for primary cardiovascular disease prevention, there has been little study regarding its comparative utilization among ethnic and racial minorities. METHODS: We contrasted the temporal trends in the ethnoracial composition for 73,856 out-patients undergoing stress/rest radionuclide myocardial perfusion imaging (MPI) between 1991 and 2020 and 32,906 undergoing CAC scanning between 1998 and 2020. Both groups were divided into those below and above 65 years. Initial medical insurance claims were used to identify which patients self-paid for SPECT-MPI and CAC studies. RESULTS: Among stress-MPI patients <65 years, the prevalence of White patients declined from 85.5% to 54.0% over the temporal span of our study while the prevalence of Blacks increased from 7.2% to 15.1% and that of Hispanics from 2.3 to 21.6%. Increasing ethnoracial diversification was also noted for SPECT-MPI patients ≥65 years. By contrast, over four-fifths of CAC studies were performed in White patients in each temporal period among both younger and older patients. Among CAC patients <65 years, over 95% of studies were self-paid by patients. For CAC patients ≥65 years, nearly two-third of studies were first submitted to Medicare, but there was no difference in the ethnoracial composition in this group versus initial self-paying patients. CONCLUSIONS: While the ethnoracial diversity of patients undergoing SPECT-MPI markedly increased at our Institution over recent decades, CAC scanning has been disproportionately and consistently utilized by self-paying White patients. These findings highlight the need to make CAC scanning more available among ethnoracial minorities.

Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures.

BACKGROUND: Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection. METHODS: This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis. RESULTS: Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation. CONCLUSIONS: We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Coronary Atherosclerotic Plaque Activity and Risk of Myocardial Infarction.

BACKGROUND: Total coronary atherosclerotic plaque activity across the entire coronary arterial tree is associated with patient-level clinical outcomes. OBJECTIVES: We aimed to investigate whether vessel-level coronary atherosclerotic plaque activity is associated with vessel-level myocardial infarction. METHODS: In this secondary analysis of an international multicenter study of patients with recent myocardial infarction and multivessel coronary artery disease, we assessed vessel-level coronary atherosclerotic plaque activity using coronary 18F-sodium fluoride positron emission tomography to identify vessel-level myocardial infarction. RESULTS: Increased 18F-sodium fluoride uptake was found in 679 of 2,094 coronary arteries and 414 of 691 patients. Myocardial infarction occurred in 24 (4%) vessels with increased coronary atherosclerotic plaque activity and in 25 (2%) vessels without increased coronary atherosclerotic plaque activity (HR: 2.08; 95% CI: 1.16-3.72; P = 0.013). This association was not demonstrable in those treated with coronary revascularization (HR: 1.02; 95% CI: 0.47-2.25) but was notable in untreated vessels (HR: 3.86; 95% CI: 1.63-9.10; Pinteraction = 0.024). Increased coronary atherosclerotic plaque activity in multiple coronary arteries was associated with heightened patient-level risk of cardiac death or myocardial infarction (HR: 2.43; 95% CI: 1.37-4.30; P = 0.002) as well as first (HR: 2.19; 95% CI: 1.18-4.06; P = 0.013) and total (HR: 2.50; 95% CI: 1.42-4.39; P = 0.002) myocardial infarctions. CONCLUSIONS: In patients with recent myocardial infarction and multivessel coronary artery disease, coronary atherosclerotic plaque activity prognosticates individual coronary arteries and patients at risk for myocardial infarction.

Alterations in genes associated with cytosolic RNA sensing in whole blood are associated with coronary microvascular disease in SLE.

Systemic lupus erythematosus (SLE) patients are 90% women and over three times more likely to die of cardiovascular disease than women in the general population. Chest pain with no obstructive cardiac disease is associated with coronary microvascular disease (CMD), where narrowing of the small blood vessels can lead to ischemia, and frequently reported by SLE patients. Using whole blood RNA samples, we asked whether gene signatures discriminate SLE patients with coronary microvascular dysfunction (CMD) on cardiac MRI (n=4) from those without (n=7) and whether any signaling pathway is linked to the underlying pathobiology of SLE CMD. RNA-seq analysis revealed 143 differentially expressed (DE) genes between the SLE and healthy control (HC) groups, with virus defense and interferon (IFN) signaling being the key pathways identified as enriched in SLE as expected. We next conducted a comparative analysis of genes differentially expressed in SLE-CMD and SLE-non-CMD relative to HC samples. Our analysis highlighted differences in IFN signaling, RNA sensing and ADP-ribosylation pathways between SLE-CMD and SLE-non-CMD. This is the first study to investigate possible gene signatures associating with CMD in SLE, and our data strongly suggests that distinct molecular mechanisms underly vascular changes in CMD and non-CMD involvement in SLE.

Expanding antibiotic, vaccine, and diagnostics development and access to tackle antimicrobial resistance.

The increasing number of bacterial infections globally that do not respond to any available antibiotics indicates a need to invest in-and ensure access to-new antibiotics, vaccines, and diagnostics. The traditional model of drug development, which depends on substantial revenues to motivate investment, is no longer economically viable without push and pull incentives. Moreover, drugs developed through these mechanisms are unlikely to be affordable for all patients in need, particularly in low-income and middle-income countries. New, publicly funded models based on public-private partnerships could support investment in antibiotics and novel alternatives, and lower patients' out-of-pocket costs, making drugs more accessible. Cost reductions can be achieved with public goods, such as clinical trial networks and platform-based quality assurance, manufacturing, and product development support. Preserving antibiotic effectiveness relies on accurate and timely diagnosis; however scaling up diagnostics faces technological, economic, and behavioural challenges. New technologies appeared during the COVID-19 pandemic, but there is a need for a deeper understanding of market, physician, and consumer behaviour to improve the use of diagnostics in patient management. Ensuring sustainable access to antibiotics also requires infection prevention. Vaccines offer the potential to prevent infections from drug-resistant pathogens, but funding for vaccine development has been scarce in this context. The High-Level Meeting of the UN General Assembly in 2024 offers an opportunity to rethink how research and development can be reoriented to serve disease management, prevention, patient access, and antibiotic stewardship.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

PURPOSE: To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. METHODS: A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. RESULTS: In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. CONCLUSION: The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Using Deep learning to Predict Cardiovascular Magnetic Resonance Findings from Echocardiography Videos.

Background: Echocardiography is the most common modality for assessing cardiac structure and function. While cardiac magnetic resonance (CMR) imaging is less accessible, CMR can provide unique tissue characterization including late gadolinium enhancement (LGE), T1 and T2 mapping, and extracellular volume (ECV) which are associated with tissue fibrosis, infiltration, and inflammation. While deep learning has been shown to uncover findings not recognized by clinicians, it is unknown whether CMR-based tissue characteristics can be derived from echocardiography videos using deep learning. We hypothesized that deep learning applied to echocardiography could predict CMR-based measurements. Methods: In a retrospective single-center study, adult patients with CMRs and echocardiography studies within 30 days were included. A video-based convolutional neural network was trained on echocardiography videos to predict CMR-derived labels including wall motion abnormality (WMA) presence, LGE presence, and abnormal T1, T2 or ECV across echocardiography views. The model performance was evaluated in a held-out test dataset not used for training. Results: The study population included 1,453 adult patients (mean age 56±18 years, 42% female) with 2,556 paired echocardiography studies occurring on average 2 days after CMR (interquartile range 2 days prior to 6 days after). The model had high predictive capability for presence of WMA (AUC 0.873 [95%CI 0.816-0.922]), however, the model was unable to reliably detect the presence of LGE (AUC 0.699 [0.613-0.780]), native T1 (AUC 0.614 [0.500-0.715]), T2 0.553 [0.420-0.692], or ECV 0.564 [0.455-0.691]). Conclusions: Deep learning applied to echocardiography accurately identified CMR-based WMA, but was unable to predict tissue characteristics, suggesting that signal for these tissue characteristics may not be present within ultrasound videos, and that the use of CMR for tissue characterization remains essential within cardiology. Clinical Perspective: Tissue characterization of the heart muscle is useful for clinical diagnosis and prognosis by identifying myocardial fibrosis, inflammation, and infiltration, and can be measured using cardiac MRI. While echocardiography is highly accessible and provides excellent functional information, its ability to provide tissue characterization information is limited at this time. Our study using a deep learning approach to predict cardiac MRI-based tissue characteristics from echocardiography showed limited ability to do so, suggesting that alternative approaches, including non-deep learning methods should be considered in future research.

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.

Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.

Background: While low-dose computed tomography scans are traditionally used for attenuation correction in hybrid myocardial perfusion imaging (MPI), they also contain additional anatomic and pathologic information not utilized in clinical assessment. We seek to uncover the full potential of these scans utilizing a holistic artificial intelligence (AI)-driven image framework for image assessment. Methods: Patients with SPECT/CT MPI from 4 REFINE SPECT registry sites were studied. A multi-structure model segmented 33 structures and quantified 15 radiomics features for each on CT attenuation correction (CTAC) scans. Coronary artery calcium and epicardial adipose tissue scores were obtained from separate deep-learning models. Normal standard quantitative MPI features were derived by clinical software. Extreme Gradient Boosting derived all-cause mortality risk scores from SPECT, CT, stress test, and clinical features utilizing a 10-fold cross-validation regimen to separate training from testing data. The performance of the models for the prediction of all-cause mortality was evaluated using area under the receiver-operating characteristic curves (AUCs). Results: Of 10,480 patients, 5,745 (54.8%) were male, and median age was 65 (interquartile range [IQR] 57-73) years. During the median follow-up of 2.9 years (1.6-4.0), 651 (6.2%) patients died. The AUC for mortality prediction of the model (combining CTAC, MPI, and clinical data) was 0.80 (95% confidence interval [0.74-0.87]), which was higher than that of an AI CTAC model (0.78 [0.71-0.85]), and AI hybrid model (0.79 [0.72-0.86]) incorporating CTAC and MPI data (p<0.001 for all). Conclusion: In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.

Deep Learning-Enabled Quantification of 99mTc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.

Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99mTc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99mTc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99mTc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99mTc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99mTc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction.