Effect of Denosumab or Alendronate on Vascular Calcification: Secondary Analysis of SALTIRE2 Randomized Controlled Trial.

BACKGROUND: Patients with osteoporosis demonstrate increased vascular calcification but the effect of osteoporosis treatments on vascular calcification remains unclear. The present study aimed to examine whether coronary or aortic calcification are influenced by denosumab and alendronic acid treatment. METHODS AND RESULTS: In a double-blind randomized controlled SALTIRE2 (Study Investigating the Effect of Drugs Used to Treat Osteoporosis on the Progression of Calcific Aortic Stenosis) trial, patients with aortic stenosis were randomized 2:1:2:1 to denosumab, placebo injection, alendronic acid, or placebo capsule. Participants underwent serial imaging with computed tomography and 18F-sodium fluoride positron emission tomography for the assessment of vascular calcium burden and calcification activity, respectively. We report the prespecified secondary analyses of 24-month change in coronary calcium score, and 12-month changes in thoracic aorta calcium score, coronary and aortic 18F-sodium fluoride activity. One hundred fifty patients with aortic stenosis (72±8 years; 21% female) were randomized to denosumab (n=49), alendronic acid (n=51), and placebo (injection n=25, capsule n=25). There were no differences in change in coronary calcium scores between placebo (16 [-64 to 148] Agatston units) and either denosumab (94 [0-212] Agatston units, P=0.24) or alendronic acid (34 [-62 to 134], P=0.99). There were no differences in change in thoracic aorta calcium scores between placebo (132 [22-512] Agatston units) and either denosumab (118 [11-340], P=0.75) or alendronic acid (116 [26-498] Agatston units, P=0.62). There were no differences in changes in coronary or aortic 18F-sodium fluoride activity between treatment groups. CONCLUSIONS: Neither alendronic acid nor denosumab are associated with changes in the activity or progression of coronary or aortic calcification. Osteoporosis treatments do not appear to have major impact on vascular calcification of atherosclerosis. REGISTRATION: https://www.clinicaltrials.gov; Unique identifier: NCT02132026.

AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening.

Background Incidental extrapulmonary findings are commonly detected on chest CT scans and can be clinically important. Purpose To integrate artificial intelligence (AI)-based segmentation for multiple structures, coronary artery calcium (CAC), and epicardial adipose tissue with automated feature extraction methods and machine learning to detect extrapulmonary abnormalities and predict all-cause mortality (ACM) in a large multicenter cohort. Materials and Methods In this post hoc analysis, baseline chest CT scans in patients enrolled in the National Lung Screening Trial (NLST) from August 2002 to September 2007 were included from 33 participating sites. Per scan, 32 structures were segmented with a multistructure model. For each structure, 15 clinically interpretable radiomic features were quantified. Four general codes describing abnormalities reported by NLST radiologists were applied to identify extrapulmonary significant incidental findings on the CT scans. Death at 2-year and 10-year follow-up and the presence of extrapulmonary significant incidental findings were predicted with ensemble AI models, and individualized structure risk scores were evaluated. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the performance of the models for prediction of ACM and extrapulmonary significant incidental findings. The Pearson χ2 test and Kruskal-Wallis rank sum test were used for statistical analyses. Results A total of 24 401 participants (median age, 61 years [IQR, 57-65 years]; 14 468 male) were included. In 3880 of 24 401 participants (16%), 4283 extrapulmonary significant incidental findings were reported. During the 10-year follow-up, 3389 of 24 401 participants (14%) died. CAC had the highest feature importance for predicting the three study end points. The 10-year ACM model demonstrated the best AUC performance (0.72; per-year mortality of 2.6% above and 0.8% below the risk threshold), followed by 2-year ACM (0.71; per-year mortality of 1.13% above and 0.3% below the risk threshold) and prediction of extrapulmonary significant incidental findings (0.70; probability of occurrence of 25.4% above and 9.6% below the threshold). Conclusion A fully automated AI model indicated extrapulmonary structures at risk on chest CT scans and predicted ACM with explanations. ClinicalTrials.gov Identifier: NCT00047385 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yanagawa and Hata in this issue.

Coronary Plaque Characterization with T1-weighted MRI and Near-Infrared Spectroscopy to Predict Periprocedural Myocardial Injury.

Purpose To clarify the predominant causative plaque constituent for periprocedural myocardial injury (PMI) following percutaneous coronary intervention: (a) erythrocyte-derived materials, indicated by a high plaque-to-myocardium signal intensity ratio (PMR) at coronary atherosclerosis T1-weighted characterization (CATCH) MRI, or (b) lipids, represented by a high maximum 4-mm lipid core burden index (maxLCBI4 mm) at near-infrared spectroscopy intravascular US (NIRS-IVUS). Materials and Methods This retrospective study included consecutive patients who underwent CATCH MRI before elective NIRS-IVUS-guided percutaneous coronary intervention at two facilities. PMI was defined as post-percutaneous coronary intervention troponin T values greater than five times the upper reference limit. Multivariable analysis was performed to identify predictors of PMI. Finally, the predictive capabilities of MRI, NIRS-IVUS, and their combination were compared. Results A total of 103 lesions from 103 patients (median age, 72 years [IQR, 64-78]; 78 male patients) were included. PMI occurred in 36 lesions. In multivariable analysis, PMR emerged as the strongest predictor (P = .001), whereas maxLCBI4 mm was not a significant predictor (P = .07). When PMR was excluded from the analysis, maxLCBI4 mm emerged as the sole independent predictor (P = .02). The combination of MRI and NIRS-IVUS yielded the largest area under the receiver operating curve (0.86 [95% CI: 0.64, 0.83]), surpassing that of NIRS-IVUS alone (0.75 [95% CI: 0.64, 0.83]; P = .02) or MRI alone (0.80 [95% CI: 0.68, 0.88]; P = .30). Conclusion Erythrocyte-derived materials in plaques, represented by a high PMR at CATCH MRI, were strongly associated with PMI independent of lipids. MRI may play a crucial role in predicting PMI by offering unique pathologic insights into plaques, distinct from those provided by NIRS. Keywords: Coronary Plaque, Periprocedural Myocardial Injury, MRI, Near-Infrared Spectroscopy Intravascular US Supplemental material is available for this article. © RSNA, 2024.

Relationship of Subendocardial Perfusion to Myocardial Injury, Cardiac Structure, and Clinical Outcomes Among Patients With Hypertension.

BACKGROUND: Coronary microvascular dysfunction has been implicated in the development of hypertensive heart disease and heart failure, with subendocardial ischemia identified as a driver of sustained myocardial injury and fibrosis. We aimed to evaluate the relationships of subendocardial perfusion with cardiac injury, structure, and a composite of major adverse cardiovascular and cerebral events consisting of death, heart failure hospitalization, myocardial infarction, and stroke. METHODS: Layer-specific blood flow and myocardial flow reserve (MFR; stress/rest myocardial blood flow) were assessed by 13N-ammonia perfusion positron emission tomography in consecutive patients with hypertension without flow-limiting coronary artery disease (summed stress score <3) imaged at Brigham and Women's Hospital (Boston, MA) from 2015 to 2021. In this post hoc observational study, biomarkers, echocardiographic parameters, and longitudinal clinical outcomes were compared by tertiles of subendocardial MFR (MFRsubendo). RESULTS: Among 358 patients, the mean age was 70.6±12.0 years, and 53.4% were male. The median MFRsubendo was 2.57 (interquartile range, 2.08-3.10), and lower MFRsubendo was associated with older age, diabetes, lower renal function, greater coronary calcium burden, and higher systolic blood pressure (P<0.05 for all). In cross-sectional multivariable regression analyses, the lowest tertile of MFRsubendo was associated with myocardial injury and with greater left ventricular wall thickness and volumes compared with the highest tertile. Relative to the highest tertile, low MFRsubendo was independently associated with an increased rate of major adverse cardiovascular and cerebral events (adjusted hazard ratio, 2.99 [95% CI, 1.39-6.44]; P=0.005) and heart failure hospitalization (adjusted hazard ratio, 2.76 [95% CI, 1.04-7.32; P=0.042) over 1.1 (interquartile range, 0.6-2.8) years median follow-up. CONCLUSIONS: Among patients with hypertension without flow-limiting coronary artery disease, impaired MFRsubendo was associated with cardiovascular risk factors, elevated cardiac biomarkers, cardiac structure, and clinical events.

The prognostic value of single photon emission computed tomography (SPECT) myocardial perfusion imaging is independent of left ventricular size.

Many variables have been shown to impact accuracy and prognostic power of myocardial perfusion imaging (MPI); however, effects of left ventricular size have not been extensively studied. In particular, perfusion defects in smaller hearts could be overlooked due to partial volume averaging, potentially reducing the prognostic power of MPI. We determined stress total perfusion deficit (TPD) and rest end diastolic volume (REDV) from single photon emission computed tomography (SPECT) MPI in consecutive patients without pathologically dilated left ventricles. Area under the curve (AUC) and Cox regression analysis were used to assess prediction of subsequent major adverse cardiac events [MACE-death, hospitalized acute myocardial infarction (AMI), hospitalized unstable angina, late revascularization]. Analyses were stratified by sex and REDV tertile. The analytic population included 2,503 patients (965 men and 1,538 women). Outcomes were assessed over an average of 6.4±2.3 years. MACE was observed in 254 (26.3%) of 965 men and 261 (17.0%) of 1,538 women. Stress TPD showed significant AUCs for stratifying MACE risk regardless of sex and REDV tertile (all P<0.05). In Cox regression analysis, increasing stress TPD (but not REDV) was associated with MACE in both men and women. There was no significant TPD*REDV interaction. In conclusion, we found the prognostic power of SPECT MPI to be independent of left ventricular size.

Quantification of Aortic Valve Fibrotic and Calcific Tissue from CTA: Prospective Comparison with Histology.

Background Quantifying the fibrotic and calcific composition of the aortic valve at CT angiography (CTA) can be useful for assessing disease severity and outcomes of patients with aortic stenosis (AS); however, it has not yet been validated against quantitative histologic findings. Purpose To compare quantification of aortic valve fibrotic and calcific tissue composition at CTA versus histologic examination. Materials and Methods This prospective study included patients who underwent CTA before either surgical aortic valve replacement for AS or orthotopic heart transplant (controls) at two centers between January 2022 and April 2023. At CTA, fibrotic and calcific tissue composition were quantified using automated Gaussian mixture modeling applied to the density of aortic valve tissue components, calculated as [(volume/total tissue volume) × 100]. For histologic evaluation, explanted valve cusps were stained with Movat pentachrome as well as hematoxylin and eosin. For each cusp, three 5-µm slices were obtained. Fibrotic and calcific tissue composition were quantified using a validated artificial intelligence tool and averaged across the aortic valve. Correlations were assessed using the Spearman rank correlation coefficient. Intermodality and interobserver variability were measured using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Twenty-nine participants (mean age, 63 years ± 10 [SD]; 23 male) were evaluated: 19 with severe AS, five with moderate AS, and five controls. Fibrocalcific tissue composition strongly correlated with histologic findings (r = 0.92; P < .001). The agreement between CTA and histologic findings for fibrocalcific tissue quantification was excellent (ICC, 0.94; P = .001), with underestimation of fibrotic composition at CTA (bias, -4.9%; 95% limits of agreement [LoA]: -18.5%, 8.7%). Finally, there was excellent interobserver repeatability for fibrotic (ICC, 0.99) and calcific (ICC, 0.99) aortic valve tissue volume measurements, with no evidence of a difference in measurements between readers (bias, -0.04 cm3 [95% LoA: -0.27 cm3, 0.19 cm3] and 0.02 cm3 [95% LoA: -0.14 cm3, 0.19 cm3], respectively). Conclusion In a direct comparison, standardized quantitative aortic valve tissue characterization at CTA showed excellent concordance with histologic findings and demonstrated interobserver reproducibility. Clinical trial registration no. NCT06136689 Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Almeida in this issue.

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.

Quantitative Computed Tomography Angiography for the Evaluation of Valvular Fibrocalcific Volume in Aortic Stenosis.

BACKGROUND: Aortic stenosis (AS) is characterized by calcification and fibrosis. The ability to quantify these processes simultaneously has been limited with previous imaging methods. OBJECTIVES: The purpose of this study was to evaluate the aortic valve fibrocalcific volume by computed tomography (CT) angiography in patients with AS, in particular, to assess its reproducibility, association with histology and disease severity, and ability to predict/track progression. METHODS: In 136 patients with AS, fibrocalcific volume was calculated on CT angiograms at baseline and after 1 year. CT attenuation distributions were analyzed using Gaussian-mixture-modeling to derive thresholds for tissue types enabling the quantification of calcific, noncalcific, and fibrocalcific volumes. Scan-rescan reproducibility was assessed and validation provided against histology and in an external cohort. RESULTS: Fibrocalcific volume measurements took 5.8 ± 1.0 min/scan, demonstrating good correlation with ex vivo valve weight (r = 0.51; P < 0.001) and excellent scan-rescan reproducibility (mean difference -1%, limits of agreement -4.5% to 2.8%). Baseline fibrocalcific volumes correlated with mean gradient on echocardiography in both male and female participants (rho = 0.64 and 0.69, respectively; both P < 0.001) and in the external validation cohort (n = 66, rho = 0.58; P < 0.001). The relationship was driven principally by calcific volume in men and fibrotic volume in women. After 1 year, fibrocalcific volume increased by 17% and correlated with progression in mean gradient (rho = 0.32; P = 0.003). Baseline fibrocalcific volume was the strongest predictor of subsequent mean gradient progression, with a particularly strong association in female patients (rho = 0.75; P < 0.001). CONCLUSIONS: The aortic valve fibrocalcific volume provides an anatomic assessment of AS severity that can track disease progression precisely. It correlates with disease severity and hemodynamic progression in both male and female patients.

Current status and future directions in artificial intelligence for nuclear cardiology.

INTRODUCTION: Myocardial perfusion imaging (MPI) is one of the most commonly ordered cardiac imaging tests. Accurate motion correction, image registration, and reconstruction are critical for high-quality imaging, but this can be technically challenging and has traditionally relied on expert manual processing. With accurate processing, there is a rich variety of clinical, stress, functional, and anatomic data that can be integrated to guide patient management. AREAS COVERED: PubMed and Google Scholar were reviewed for articles related to artificial intelligence in nuclear cardiology published between 2020 and 2024. We will outline the prominent roles for artificial intelligence (AI) solutions to provide motion correction, image registration, and reconstruction. We will review the role for AI in extracting anatomic data for hybrid MPI which is otherwise neglected. Lastly, we will discuss AI methods to integrate the wealth of data to improve disease diagnosis or risk stratification. EXPERT OPINION: There is growing evidence that AI will transform the performance of MPI by automating and improving on aspects of image acquisition and reconstruction. Physicians and researchers will need to understand the potential strengths of AI in order to benefit from the full clinical utility of MPI.

Evaluating AI Proficiency in Nuclear Cardiology: Large Language Models take on the Board Preparation Exam.

Background: Previous studies evaluated the ability of large language models (LLMs) in medical disciplines; however, few have focused on image analysis, and none specifically on cardiovascular imaging or nuclear cardiology. Objectives: This study assesses four LLMs - GPT-4, GPT-4 Turbo, GPT-4omni (GPT-4o) (Open AI), and Gemini (Google Inc.) - in responding to questions from the 2023 American Society of Nuclear Cardiology Board Preparation Exam, reflecting the scope of the Certification Board of Nuclear Cardiology (CBNC) examination. Methods: We used 168 questions: 141 text-only and 27 image-based, categorized into four sections mirroring the CBNC exam. Each LLM was presented with the same standardized prompt and applied to each section 30 times to account for stochasticity. Performance over six weeks was assessed for all models except GPT-4o. McNemar's test compared correct response proportions. Results: GPT-4, Gemini, GPT4-Turbo, and GPT-4o correctly answered median percentiles of 56.8% (95% confidence interval 55.4% - 58.0%), 40.5% (39.9% - 42.9%), 60.7% (59.9% - 61.3%) and 63.1% (62.5 - 64.3%) of questions, respectively. GPT4o significantly outperformed other models (p=0.007 vs. GPT-4Turbo, p<0.001 vs. GPT-4 and Gemini). GPT-4o excelled on text-only questions compared to GPT-4, Gemini, and GPT-4 Turbo (p<0.001, p<0.001, and p=0.001), while Gemini performed worse on image-based questions (p<0.001 for all). Conclusion: GPT-4o demonstrated superior performance among the four LLMs, achieving scores likely within or just outside the range required to pass a test akin to the CBNC examination. Although improvements in medical image interpretation are needed, GPT-4o shows potential to support physicians in answering text-based clinical questions.

Arterial effects of anthracycline: structural and inflammatory assessments in non-human primates and lymphoma patients using 18F-FDG positron emission tomography.

Background: Anthracyclines, such as doxorubicin, are important anti-cancer therapies but are associated with arterial injury. Histopathological insights have been limited to small animal models and the role of inflammation in the arterial toxic effects of anthracycline is unclear in humans. Our aims were: 1) To evaluate aortic media fibrosis and injury in non-human primates treated with anthracyclines; 2) To assess the effect of anthracycline on aortic inflammation in patients treated for lymphoma. Methods: 1) African Green monkeys (AGM) received doxorubicin (30-60 mg/m2/biweekly IV, cumulative dose: 240 mg/m2). Blinded histopathologic analyses of collagen deposition and cell vacuolization in the ascending aorta were performed 15 weeks after the last doxorubicin dose and compared to 5 age- and gender-matched healthy, untreated AGMs. 2) Analysis of the thoracic aorta of patients with diffuse large B-cell lymphoma (DLBCL), at baseline and after doxorubicin exposure, was performed using 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) in this observational study. The primary outcome was change in maximal tissue-to-background ratio (TBRmax) of the thoracic aorta from baseline to their end-of-treatment clinical PET/CT. Results: In AGMs, doxorubicin exposure was associated with greater aortic fibrosis (collagen deposition: doxorubicin cohort 6.23±0.88% vs. controls 4.67±0.54%; p=0.01) and increased intracellular vacuolization (doxorubicin 66.3 ± 10.1 vs controls 11.5 ± 4.2 vacuoles/field, p<0.0001) than untreated controls.In 101 patients with DLBCL, there was no change in aortic TBRmax after anthracycline exposure (pre-doxorubicin TBRmax 1.46±0.16 vs post-doxorubicin TBRmax 1.44±0.14, p=0.14). The absence of change in TBRmax was consistent across all univariate analyses. Conclusions: In a large animal model, anthracycline exposure was associated with aortic fibrosis. In patients with lymphoma, anthracycline exposure was not associated with aortic inflammation.Further research is required to elucidate the mechanisms of anthracycline-related vascular harm.

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.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease.

PURPOSE OF REVIEW: Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease. RECENT FINDINGS: Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

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.

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.

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.

Duration of adenosine-induced myocardial hyperemia - Insights from quantitative 13N-ammonia positron emission tomography myocardial perfusion imaging.

AIMS: To assess the impact of adenosine on quantitative myocardial blood flow (MBF) in a rapid stress-rest protocol compared to a rest-stress protocol using 13N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) and to gain insights into the time dependency of such effects. METHODS AND RESULTS: Quantitative MBF at rest (rMBF), during adenosine-induced stress (sMBF) and myocardial flow reserve (MFR) were obtained from 331 retrospectively identified patients who underwent 13N-ammonia PET-MPI for suspected chronic coronary syndrome and who all exhibited no perfusion defects. Of these, 146 (44.1%) underwent a rapid stress-rest protocol with a time interval (Δtstress-rest) of 20 ± 4 minutes between adenosine infusion offset and rest-imaging, as per clinical routine. The remaining 185 (55.9%) patients underwent a rest-stress protocol and served as the reference. Groups did not differ regarding demographics, risk factors, medication, left ventricular function, and calcium scores. rMBF was significantly higher in the stress-rest vs. the rest-stress group (0.80 [IQR 0.66-1.00] vs. 0.70 [0.58-0.83] ml·min-1·g-1, p < 0.001) and, as sMBF was identical between groups (2.52 [2.20-2.96] vs. 2.50 [1.96-3.11], p = 0.347), MFR was significantly lower in the stress-rest group (3.07 [2.43-3.88] vs. 3.50 [2.63-4.10], p < 0.001). There was a weak correlation between Δtstress-rest and rMBF (r = -0.259, p = 0.002) and between Δtstress-rest and MFR (r = 0.163, p = 0.049), and the proportion of patients with abnormally high rMBF was significantly decreasing with increasing Δtstress-rest. CONCLUSIONS: Intravenously applied adenosine induces a long-lasting hyperemic effect on the myocardium. Consequently, rapid stress-rest protocols could lead to an overestimation of rMBF and an underestimation of MFR.

Self-reported exercise activity influences the relationship between coronary computed tomography angiographic finding and mortality.

AIM: Recent studies suggest that the application of exercise activity questionnaires, including the use of a single-item exercise question, can be additive to the prognostic efficacy of imaging findings. This study aims to evaluate the prognostic efficacy of exercise activity in patients undergoing coronary computed tomography angiography (CCTA). METHODS AND RESULTS: We assessed 9772 patients who underwent CCTA at a single center between 2007 and 2020. Patients were divided into 4 groups of physical activity as no exercise (n â€‹= â€‹1643, 17%), mild exercise (n â€‹= â€‹3156, 32%), moderate exercise (n â€‹= â€‹3542, 36%), and high exercise (n â€‹= â€‹1431,15%), based on a single-item self-reported questionnaire. Coronary stenosis was categorized as no (0%), non-obstructive (1-49%), borderline (50-69%), and obstructive (≥70%). During a median follow-up of 4.64 (IQR 1.53-7.89) years, 490 (7.6%) died. There was a stepwise inverse relationship between exercise activity and mortality (p â€‹< â€‹0.001). Compared with the high activity group, the no activity group had a 3-fold higher mortality risk (HR: 3.3, 95%CI (1.94-5.63), p â€‹< â€‹0.001) after adjustment for age, clinical risk factors, symptoms, and statin use. For any level of CCTA stenosis, mortality rates were inversely associated with the degree of patients' exercise activity. The risk of all-cause mortality was similar among the patients with obstructive stenosis with high exercise versus those with no coronary stenosis but no exercise activity (p â€‹= â€‹0.912). CONCLUSION: Physical activity as assessed by a single-item self-reported questionnaire is a strong stepwise inverse predictor of mortality risk among patients undergoing CCTA.

Predicting mortality from AI cardiac volumes mass and coronary calcium on chest computed tomography.

Chest computed tomography is one of the most common diagnostic tests, with 15 million scans performed annually in the United States. Coronary calcium can be visualized on these scans, but other measures of cardiac risk such as atrial and ventricular volumes have classically required administration of contrast. Here we show that a fully automated pipeline, incorporating two artificial intelligence models, automatically quantifies coronary calcium, left atrial volume, left ventricular mass, and other cardiac chamber volumes in 29,687 patients from three cohorts. The model processes chamber volumes and coronary artery calcium with an end-to-end time of ~18 s, while failing to segment only 0.1% of cases. Coronary calcium, left atrial volume, and left ventricular mass index are independently associated with all-cause and cardiovascular mortality and significantly improve risk classification compared to identification of abnormalities by a radiologist. This automated approach can be integrated into clinical workflows to improve identification of abnormalities and risk stratification, allowing physicians to improve clinical decision-making.