Chareonthaitawee, Panithaya

Abstract Background Cardiac masses encompass a broad differential, including benign tumours, malignancies, and pseudotumours. Accurate diagnosis requires integration of clinical context, patient characteristics, lesion location, and imaging features. In select cases, tissue characterization is necessary. We describe a case of a rapidly enlarging interatrial mass following transcatheter aortic valve implantation (TAVI). Case summary A 70-year-old man underwent TAVI for severe low-flow, low-gradient aortic stenosis. Preprocedural imaging revealed an incidental interatrial mass consistent with lipomatous hypertrophy of the atrial septum (LHAS). Six months later, the mass showed significant interval growth, raising concern for malignancy. Multimodality imaging, including cardiac magnetic resonance imaging and FDG positron emission tomography yielded indeterminate results. An echocardiography-guided biopsy was performed, and histopathologic analysis confirmed LHAS with intralesional haemorrhage likely incurred during the TAVI procedure. Discussion While LHAS is typically benign and stable or slowly progressive, rare cases may present with atypical features and rapid growth, mimicking malignant cardiac tumours. This case represents the first reported instance of LHAS with haemorrhagic expansion.

Abstract Background Quantitative myocardial blood flow (MBF) and myocardial flow reserve (MFR) provide incremental diagnostic and prognostic value in cardiac PET, but their widespread use is limited by the technical demands of dynamic imaging protocols. We evaluated the feasibility of using artificial intelligence (AI) to predict MBF and MFR from static and gated PET images, without the need for dynamic acquisition. Methods A machine learning (XGBoost) model was trained on 82Rb PET multi-center dataset using static perfusion imaging, injected dose, hemodynamic measures, clinical data and CT-derived features (including body composition) from the hybrid CT attenuation scan. Model performance was evaluated externally in an independent cohort. Results In total, 10,566 (derivation-cohort) and 7,842 (external-cohort) patients were included in this multi-center study. On the external-cohort, AI approach achieved an Area under the curve (AUC) of 0.92 (0.92–0.93) for abnormal stress MBF and 0.91 (0.90–0.92) for abnormal MFR; Intra-class correlation (ICC) 0.80 (0.78–0.82) and 0.78 (0.76–0.79), respectively. AI MFR closely mirrored the prognostic performance of measured MFR, showing nearly identical Kaplan–Meier risk stratification (both p<0.0001) and maintaining strong, and independently significant associations with all-cause mortality (HR 3.4 [2.8–4.2] vs. 4.6 [3.6–5.8]; both p<0.001), and demonstrated similar added value to perfusion for mortality prediction. Conclusion AI-predicted virtual stress MBF and MFR assessment using static and gated PET data is feasible and generalizable across cohorts. By removing the dependency on dynamic acquisitions, this approach has the potential to broaden the clinical adoption of flow quantification. STRUCTURED GRAPHICAL ABSTRACT PET: Positron Emission Tomography, CT: Computed Tomography, MFR: Myocardial Flow Reserve Key Question: Can machine learning models trained on dynamic PET datasets accurately predict regional stress myocardial blood flow (MBF) and myocardial flow reserve (MFR) from static image features, physiological parameters, and CT-based anatomical measures? Key Finding: Artificial intelligence can accurately estimate MBF and MFR from non-dynamic PET data, with strong agreement to reference standards. Take-home Message: By eliminating reliance on dynamic PET acquisitions, machine-learning has the potential to broaden clinical adoption of quantitative flow assessment.

Abstract Background 99mTc-labelled cardiac scintigraphy enables noninvasive diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM). Guidelines recommend using planar semiquantitative (heart to contralateral ratio, H/CL) and single photon emission computed tomography (SPECT) qualitative visual grading for diagnostic interpretation. However, planar imaging has inherent limitations, and visual grading is prone to interobserver variability, underscoring the need for standardized quantitative assessment. We developed a 3D volumetric quantitative score (3D Score) and assessed its diagnostic performance compared to myocardial tissue, the gold standard for ATTR-CM diagnosis. Methods At a single, tertiary referral center, we retrospectively identified consecutive patients from 06/2017 to 06/2024 who underwent 3-hour 99mTc-pyrophosphate or 99mTc-hydroxymethylene diphosphonate planar and SPECT/CT for suspected ATTR-CM and had archived myocardial tissue (biopsy or myectomy). On planar imaging, H/CL was calculated. On SPECT/CT, volumes of interest were drawn around the entire left ventricle (LV), avoiding calcifications, and within the right atrium (RA) blood pool. Mean uptake values were used to calculate the 3D Score (LVmean:RAmean), Fig 1A. Myocardial tissue was stained via sulfated Alcian blue and/or Congo red histochemistry. Once histologically confirmed, amyloid deposits were subtyped by liquid chromatography tandem mass spectrometry. The diagnostic performance of H/CL and 3D Score was assessed using receiver operating characteristic (ROC) curve analysis and predictive characteristics were defined. ROC areas under the curve (AUC) were compared using Delong test. Optimal cutoffs were determined using Youden’s J statistic. Baseline characteristics (demographics, echocardiogram, biomarkers) and H/CL were compared between patients above and below 3D Score cutoff. Results 239 patients were identified (median age 72 [65-78], 78% male, 46% ATTR positive). Median time between cardiac scintigraphy and myocardial tissue collection was 22 (6-65) days. Distribution of the 3D Score stratified by ATTR-CM status is illustrated in Fig 1B-C. The discriminative ability of the 3D Score was excellent (AUC 0.92) and superior to H/CL (0.87, p =0.006, Fig 1D-E). The optimal 3D Score cutoff for identifying ATTR-CM was ≥1.0 with sensitivity of 86%, specificity of 92%, positive predictive value (PPV) 90%, and negative predictive value (NPV) 89%. The optimal H/CL ratio cutoff for identifying ATTR-CM was ≥1.4 with sensitivity of 72%, specificity of 86%, PPV 81%, NPV 78%. Baseline characteristics of the overall cohort stratified by 3D Score are displayed in Table 1. Conclusions The 3D Score demonstrated excellent diagnostic performance for detecting ATTR-CM and was superior to current quantitative metrics. Future studies are needed to externally validate its performance across diverse patient cohorts and imaging centers and to investigate correlation with myocardial amyloid burden. Figure 1

BACKGROUND: Myocardial flow reserve (MFR), measured by positron emission tomography (PET) myocardial perfusion imaging, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of 13 N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease. METHODS: We considered patients from 5 sites in the REFINE PET (Registry of Flow and Perfusion Imaging for Artificial Intelligence with PET) registry who underwent 13 N-ammonia PET myocardial perfusion imaging for coronary artery disease. Clinical and imaging data were collected at the time of myocardial perfusion imaging. MFR was quantified as the ratio of stress to rest myocardial blood flow, using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA). The primary outcome was all-cause mortality. Survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinical and imaging covariates. RESULTS: In total, 6277 patients were included (median age of 65 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] versus n=537 [12.3%], respectively; P <0.001). Annualized all-cause mortality rates by MFR and summed stress score ranged from 1.7 to 15.8. In multivariable analysis, MFR ≤2 was independently associated with increased all-cause mortality in the overall population (hazard ratio, 2.70 [95% CI, 2.41–3.03]; P <0.001), even among patients with no perfusion defects (hazard ratio, 2.36 [95% CI, 1.93–2.89]; P <0.001). Mortality risk decreased across increasing MFR deciles, ranging from hazard ratio, 2.73 (95% CI, 2.39–3.11) to hazard ratio, 0.35 (95% CI, 0.25–0.49). CONCLUSIONS: In this large multicenter cohort, MFR derived from 13 N-ammonia PET myocardial perfusion imaging is a strong, independent predictor of all-cause mortality, even in patients with normal perfusion. An MFR of ≤2 identifies elevated risk, while higher values are associated with improved survival. These findings support the routine integration of MFR to enhance risk stratification in patients with suspected or known coronary artery disease.

Abstract Aims Left bundle branch block (LBBB) is associated with mechanical dyssynchrony, heterogeneous perfusion, and adverse left ventricular (LV) remodeling. However, not all patients with LBBB develop cardiomyopathy, and dyssynchrony can occur without conduction defects. The role of microvascular dysfunction remains uncertain. We aimed to assess how mechanical dyssynchrony and perfusion heterogeneity relate to LV remodeling and function in patients with and without LBBB. Methods and results We retrospectively analyzed 233 patients with isolated LBBB and 932 matched controls who underwent PET myocardial perfusion imaging, assessing mechanical dyssynchrony (phase entropy), myocardial blood flow (MBF), coronary vascular resistance (CVR), myocardial flow reserve (MFR), septal-to-lateral MBF ratio (SLR) for perfusion heterogeneity, LV volumes, and ejection fraction (EF). Compared to controls, patients with LBBB had greater dyssynchrony (56% vs. 40%), larger LV volumes, and lower EF (54% vs. 67%) (all p <0.001), and had higher stress CVR (37 vs. 34 mmHg/mL·min⁻¹·g⁻¹), lower stress MBF (2.4 vs. 2.6 mL/min/g), reduced MFR (2.4 vs. 2.6), and lower SLR (0.95 vs. 1.00) (all p <0.05). Among patients with dyssynchrony, SLR<1.0 identified those with more adverse remodeling. In multivariable regression, phase entropy and SLR independently predicted LV volumes and EF, with adverse effects of SLR reduction amplified in LBBB (interaction p <0.01). In the Cox proportional hazards analysis, phase entropy (HR:1.02, p =0.01), MFR (HR:0.62, p <0.001), and LVEF (HR:0.97, p <0.001) were independently associated with mortality and heart failure hospitalization, whereas LBBB was not. Conclusions Mechanical dyssynchrony and perfusion heterogeneity independently predict adverse LV remodeling, irrespective of LBBB. Integrated imaging enhances cardiomyopathy stratification.

ABSTRACT Introduction Positron emission tomography (PET) myocardial flow reserve (MFR) is a robust indicator of coronary vascular health and a strong predictor of cardiovascular risk. Clinical guidelines typically use fixed MFR thresholds (e.g., <2.0) to stratify risk, yet this approach overlooks individual variation, particularly by age and sex. We aimed to establish age- and sex-adjusted MFR percentiles and to evaluate their prognostic and predictive performance for cardiovascular risk assessment, in comparison with conventional fixed-threshold MFR approach. Methods Using data from the REFINE PET registry (24,820 patients from 12 sites), we measured PET MFR and derived age- and sex-adjusted MFR reference percentiles using quantile regression in patients without known coronary artery disease. All patients were categorized into percentile-based quartile groups. The primary outcome for prognostic and prediction analyses was major adverse cardiovascular events (MACE), defined as all-cause mortality, myocardial infarction, or heart-failure hospitalization. Time-to-event associations were evaluated using covariate-adjusted survival models, with cumulative incidence and hazard ratios (HR) estimated at 1 and 5 years in the derivation dataset, an independent but similar validation dataset A, and a high-risk validation dataset B. Predictive performance for MACE was assessed using discrimination, calibration, and reclassification metrics, comparing percentile-based models with models using a fixed MFR threshold (<2.0). Results Among participants (mean age 66.5 years; median follow-up 3.6 years), age- and sex-adjusted MFR quartile groups were strong independent predictors of MACE, with adjusted HR increasing stepwise across quartile groups at both early and later follow-up. At 1 year, HR (95% CI) comparing the lowest to the highest quartile group were 4.06 (3.41–4.82) in the derivation cohort, 3.31 (2.32–4.71) in validation cohort A, and 2.35 (2.05–2.70) in validation cohort B. At 5 years, the corresponding HR were 2.18 (1.86–2.56), 1.77 (1.31–2.40), and 1.59 (1.36–1.86). Percentile-based models demonstrated consistently higher discrimination, better calibration, and greater net reclassification for MACE at both time points compared with fixed-threshold MFR models. Although 67.2% of patients had preserved MFR (>2.0), cardiovascular risk increased steadily across MFR percentiles even within this range. Conclusion Age- and sex-adjusted MFR percentiles provide a reliable, clinically actionable measure of vascular health, improving cardiovascular risk stratification by better capturing age- and sex-related heterogeneity in vascular risk. Compared with traditional fixed-threshold approaches, MFR percentiles demonstrate improved predictive performance for cardiovascular risk assessment across diverse patient populations. CLINICAL PERSPECTIVE What is new? In a large, multinational real-world registry of more than 24,000 patients undergoing PET MPI, we derived age- and sex-adjusted myocardial flow reserve (MFR) percentiles, establishing a lifespan-based reference framework with age-specific reference values. Age- and sex-adjusted MFR percentiles showed a strong, graded association with 1- and 5-year cardiovascular risk and demonstrated improved prognostic and predictive performance compared with fixed MFR thresholds and models without MFR across independent derivation and validation datasets, including a higher-risk cohort. Lower adjusted MFR percentiles were consistently associated with higher risk of major adverse cardiovascular events, including all-cause mortality, myocardial infarction, and heart-failure hospitalization. What are the clinical implications? Age- and sex-adjusted MFR percentiles provide a personalized, dynamic measure of coronary vascular health that improves cardiovascular risk stratification by accounting for age- and sex-related heterogeneity. A percentile-based approach may help clinicians better identify patients at elevated cardiovascular risk who may warrant closer monitoring or intensified preventive strategies, while avoiding unnecessary intervention in lower-risk individuals. By integrating macrovascular and microvascular dysfunction, age- and sex-adjusted MFR percentiles offer a clinically interpretable marker of vascular aging that may inform future research on aging biomarkers, novel therapeutics and cardiovascular risk assessment across the spectrum of atherosclerotic and microvascular disease.

BACKGROUND: Guidelines recommend 3-hour cardiac amyloid radionuclide imaging (CARI) for transthyretin amyloid cardiomyopathy. Citing rapid blood clearance of 99m Tc-hydroxymethylene-diphosphonate (HMDP) and efficient laboratory throughput, 1-hour imaging is increasingly practiced despite limited supporting evidence. We sought to compare diagnostic performance and interpreter experience of 1-hour versus 3-hour HMDP-CARI. METHODS: Consecutive patients with suspected transthyretin amyloid cardiomyopathy (n=114) underwent both 1-hour and 3-hour HMDP single photon emission computed tomography (CT)/CT. Two cardiologist-radiologist reader teams, blinded to imaging timepoint (1 versus 3 hours), assessed overall interpretation, single photon emission CT–based Perugini grade, interpretation difficulty, interpreter confidence, and need for CT-fused images for anatomic localization. Discordant, equivocal, and difficult cases were arbitrated by a third tie-breaking team. The myocardial-to-blood-pool radiotracer uptake ratio (3-dimensional Score) was measured as a surrogate of contrast resolution. RESULTS: Interinterpreter agreement was high at both time points (κ≥0.81), with more cases requiring arbitration at 3 hours versus 1 hour (22% versus 13%; P =0.049). Overall interpretation and Perugini grades were concordant between time points in 111/114 (97%) and 106/114 (93%) patients, respectively. Three patients (3%) were negative at 1 hour but equivocal at 3 hours, all of which were clinically ruled out for transthyretin amyloid cardiomyopathy. Interpreter confidence was comparable at both timepoints (97% versus 95%; P =0.317). Compared with 3-hour imaging, contrast resolution was inferior (lower 3-dimensional score, P <0.001) and CT fusion was more frequently needed (57% versus 31%, P <0.001) at 1-hour imaging. CONCLUSIONS: In a prospective, blinded comparison of 1-hour versus 3-hour HMDP-CARI, diagnostic performance and interpreter experience were similar, with readers requesting CT fusion more frequently at 1 hour to optimize myocardial-to-blood pool discrimination.

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. A deep learning model estimated cardiac chamber volumes and left ventricular mass from CT attenuation correction imaging. Associations between deep learning-derived CT mass and volumes with heart failure hospitalization and reduced myocardial flow reserve were assessed in a multivariable analysis. RESULTS: During a median follow-up of 4.3 years, 1721 (9.5%) patients experienced heart failure hospitalization. Patients with 3 or 4 abnormal chamber volumes were 7× more likely to be hospitalized for heart failure compared with patients with normal volumes. In adjusted analyses, left atrial volume (hazard ratio [HR], 1.25 [95% CI, 1.19–1.30]), right atrial volume (HR, 1.29 [95% CI, 1.23–1.35]), right ventricular volume (HR, 1.25 [95% CI, 1.20–1.31]), left ventricular volume (HR, 1.27 [95% CI, 1.23–1.35]), and left ventricular mass (HR, 1.25 [95% CI, 1.18–1.32]) were independently associated with heart failure hospitalization. In multivariable analyses, left atrial volume (odds ratio, 1.14 [95% CI, 1.0–1.19]) and ventricular mass (odds ratio, 1.12 [95% CI, 1.6–1.17]) were independent predictors of reduced myocardial flow reserve. CONCLUSIONS: Deep learning–derived chamber volumes and left ventricular mass from CT attenuation correction were predictive of heart failure hospitalization and reduced myocardial flow reserve in patients undergoing cardiac PET perfusion imaging. This anatomic data can be routinely reported along with other PET/CT parameters to improve risk prediction.

Abstract Background and Aims Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI). Methods In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors. Results This study included 10 085 patients, with median age 68 (interquartile range 59–76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37–1.43] and adjusted (HR 1.24, 95% CI 1.19–1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23–2.77). Conclusions Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Background: Angina with no obstructive coronary arteries (ANOCA) is clinically challenging to diagnose and manage. The agreement between Doppler-derived coronary flow reserve (CFR Doppl ) and PET-derived CFR (CFR PET ) in patients with ANOCA remains unclear. Aims: To evaluate the correlation between Doppler- and PET-derived CFR in patients with ANOCA. Methods: We conducted a single-center, retrospective study of adult patients (age ≥18 years) with ANOCA who were referred for evaluation of possible microvascular ischemia with Doppler- and PET-derived CFR at the Mayo Clinic (Rochester, MN) between January 1, 2018, and May 1, 2024. Pearson correlation was used to quantify the association between Doppler- and PET-derived CFR. Agreement between Doppler- and PET-derived CFR was assessed by Bland-Altman analysis using a 2-way mixed effects model with measures of absolute agreement. Results: A total of 66 patients were initially identified, of which 40 were included in the final analysis (66.7% female, median age 58 years [Q1-Q3: 45-66 years]). Median time between PET- and Doppler-derived CFR was 8 days (Q1-Q3: 1-31 days). Median CFR Doppl was 2.75 (IQR: 2.23-3.28) and median CFR PET was 2.50 (IQR: 2.10-3.10). Doppler-derived CFR displayed a wider range than PET-derived CFR (0.8-5.2 vs 1.2-4.0). A strong correlation was found between Doppler- and PET-derived CFR ( r= 0.81; p<0.001). The corresponding Bland-Altman plot showed an overall bias of 0.27±0.56 (intraclass coefficient=0.85; p<0.001). Conclusions: While invasive coronary angiography with CFR Doppl is the gold-standard for the diagnostic evaluation of ANOCA, PET imaging with CFR PET offers an excellent first line non-invasive modality for diagnosis of coronary microvascular dysfunction.