Gated SPECT/CT uptake patterns in ATTR-CA: Association with myocardial burden, function and prognosis

Gated SPECT/CT uptake patterns in ATTR-CA: Association with myocardial burden, function and prognosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gated SPECT/CT uptake patterns in ATTR-CA: Association with myocardial burden, function and prognosis FRANCISCO SEBASTIAN PALACID, Noelia Álvarez Mena, Berta Pérez López, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8243972/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Gated SPECT/CT (gSPECT/CT) offers detailed myocardial radiotracer distribution imaging in cardiac transthyretin amyloidosis (ATTR-CA), but lacks a standardized classification. We aimed to develop and validate a gSPECT/CT-based classification system for ATTR-CA and to explore its correlation with clinical profile, cardiac function, and outcomes. Methods We conducted an observational study in 130 patients (76.2% male) with confirmed ATTR-CA who underwent planar [99mTc]Tc-DPD scintigraphy and thoracic gSPECT/CT. Myocardial polar maps were constructed and uptake patterns were classified as septal, waning moon, vault, waxing moon, diffuse, or doughnut. Clinical, echocardiographic and genetic data were compared across patterns. A quantitative parameter—the percentage of affected myocardium—was derived from SPECT segmentation. Statistical analyses included Kruskal–Wallis and Fisher’s exact tests (Monte Carlo simulation). Results The proportion of female patients differed significantly among patterns (p = 0.041). Diffuse and doughnut patterns predominated in patients with Perugini grade 3 uptake. Myocardial involvement increased progressively across patterns (47% in septal to 85% in diffuse; p < 0.001). LVEF distribution varied: preserved LVEF was more frequent in the waning moon group, while reduced LVEF predominated in septal and doughnut patterns. Heart failure was more common in diffuse and doughnut groups (p = 0.040), and mortality was highest in waxing moon and diffuse groups (p = 0.047). Genetic variants were detected exclusively in vault, waxing moon and diffuse patterns (p = 0.021). Conclusions We propose a novel gSPECT/CT-based classification for ATTR-CA that combines qualitative uptake patterns with quantitative myocardial involvement. This system correlates strongly with sex, LVEF, heart failure, genetic findings and mortality, improving phenotypic characterization and potentially aiding risk stratification. Gated SPECT/CT transthyretin cardiac amyloidosis myocardial uptake patterns [⁹⁹ᵐTc]Tc-DPD scintigraphy cardiac imaging amyloid burden quantification Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Cardiac transthyretin amyloidosis (ATTR-CA) results from progressive myocardial deposition of transthyretin fibrils, producing restrictive cardiomyopathy characterized by impaired compliance and evolving systolic dysfunction. Early stages typically present with diastolic impairment and preserved ejection fraction, while advanced disease may involve overt systolic compromise. Atrial infiltration is common and increases the risk of arrhythmias, thromboembolic complications and electromechanical dissociation¹². Non-invasive imaging plays a central role in the diagnosis and characterization of ATTR-CA. Echocardiography and cardiac MRI provide structural and functional information, while [99mTc]-labelled bisphosphonate scintigraphy offers high diagnostic specificity and has become the cornerstone of non-biopsy diagnosis. The Perugini visual scale (grades 0–3) remains the reference method for evaluating myocardial uptake; however, its semiquantitative nature does not account for spatial heterogeneity or pattern-based differences in radiotracer distribution that may carry clinical significance³⁻⁵. Hybrid SPECT/CT with attenuation correction enhances quantitative accuracy and enables refined visualization of myocardial tracer localization. Despite these advances, the prognostic value of specific distribution patterns, their relationship with gated functional parameters and their association with disease severity remain incompletely understood⁶⁻⁹. A classification framework that integrates both qualitative and quantitative information derived from SPECT/CT could improve phenotypic characterization and support risk stratification. In this study, we introduce a gated SPECT/CT–based classification system for ATTR-CA designed to systematically define myocardial uptake phenotypes. We aimed to validate these patterns and evaluate their associations with left ventricular ejection fraction (LVEF), clinical status and major outcomes. This approach seeks to extend the Perugini framework by providing additional imaging biomarkers relevant to disease staging and prognostic assessment in Nuclear Cardiology. MATERIALS AND METHODS Study population This prospective observational study included 130 consecutive patients with confirmed ATTR cardiac amyloidosis, defined by [99mTc]Tc-DPD scintigraphy showing Perugini grade ≥ 2 and exclusion of AL amyloidosis through serum and urine immunofixation and serum free light-chain analysis. All imaging studies were performed between January 2022 and December 2023, and clinical follow-up continued until manuscript preparation to assess all-cause mortality. All participants provided written informed consent prior to inclusion. Patients were stratified by sex, age, Perugini grade (2 vs. 3), and left ventricular ejection fraction (LVEF) derived from transthoracic echocardiography. Additional clinical variables included the presence of TTR gene mutations, heart failure, arrhythmias, neurological involvement attributable to amyloidosis, and aortic stenosis. Bone scintigraphy and ECG-gated SPECT/CT acquisition Whole-body planar scintigraphy was performed 180 minutes after intravenous administration of 736–762 MBq of [99mTc]Tc-DPD. All patients underwent thoracic ECG-gated SPECT/CT using an NM/CT 870 DR system (GE Healthcare, Chicago, USA). The CT acquisition was used solely for attenuation correction. Planar imaging was performed using LEHR collimators at a scan speed of 14 cm/min. Gated SPECT/CT was acquired using a 180° step-and-shoot protocol (120 projections), 3-step frames of 25 s per projection, and 16 frames per cardiac cycle. CT parameters included 120 kV, 205 mA, and a slice thickness of 2.5 mm. Image processing and classification Reconstruction of tomographic data was performed using Myovation Evolution (GE Healthcare) with an ordered-subset expectation-maximization (OSEM) algorithm (12 iterations, 10 subsets), including attenuation and scatter correction. Butterworth and Ramp filters were applied. Images were generated in IRACRR format and processed using the Emory Cardiac Toolbox (Emory University, Atlanta, USA) to obtain polar maps. Based on radiotracer distribution, six predefined uptake patterns were identified (Fig. 1 ): Septal: focal septal uptake; Waning moon: uptake in septal, anterior and inferior walls; Vault: uptake restricted to the anterior wall; Waxing moon: uptake in lateral, anterior and inferior walls; Diffuse: homogeneous uptake across the polar map; Doughnut: diffuse uptake with relative apical sparing. Quantitative analysis was performed using QPS/QGS (Cedars-Sinai Medical Center), obtaining the percentage of affected myocardium, defined as the proportion of left ventricular myocardium exhibiting increased radiotracer uptake. Statistical analysis Statistical analyses were performed using SPSS Statistics (IBM Corp., Armonk, NY), version 10.1 for macOS. Associations between categorical variables and uptake patterns were evaluated with Fisher’s exact test applied to 2×6 contingency tables. Owing to low expected frequencies in several subgroups, Monte Carlo simulation with 10,000 replicates was systematically applied. Continuous variables (age, LVEF, percentage of affected myocardium) were compared using the Kruskal–Wallis test due to unequal sample sizes and potential non-normality. A p-value < 0.05 was considered statistically significant. RESULTS Clinical and demographic characteristics A total of 130 patients (76.2% male, 23.8% female) were diagnosed with ATTR-CA based on positive [99mTc]Tc-DPD scintigraphy between January 2022 and December 2023 and were clinically followed until May 2025. Clinical and demographic characteristics are summarized in Table 1 . Table 1 Patient demographics, clinical features, and scintigraphic parameters according to uptake patterns. Uptake patterns Characteristic All patients Septal Waning moon Vault Waxing moon Diffuse Doughnut p-value Number of patients 130 (100%) 14 (10.8%) 24 (18.5%) 8 (6.1%) 16 (12.3%) 30 (23.1%) 38 (29.2%) Male 99 (76.2%) 12 (85.7%) 21 (87.5%) 8 (100%) 11 (68.8%) 18 (60%) 29 (76.3%) .041* Female 31 (23.8%) 2 (14.3%) 3 (12.5%) 0 5 (31.2%) 12 (40%) 9 (23.7%) Age (years) 83.8 ± 5.7 85.6 ± 5.5 83.8 ± 6 84.1 ± 4.2 84.7 ± 5.4 82.6 ± 6.4 83.8 ± 5.1 .471 Perugini score grade 2 24 (18.5%) 2 (14.3%) 3 (12.5%) 0 4 (25%) 5 (16.7%) 10 (26.3%) .211 Perugini score grade 3 106 (81.5%) 12 (85.7%) 21 (87.5%) 8 (100%) 12 (75%) 25 (83.3%) 28 (73.7%) LVEF (%) 52.9 ± 15.3 47.5 ± 12.1 56.6 ± 12.4 47.6 ± 14.2 53.8 ± 18.8 55.5 ± 17 51.4 ± 13.9 .291 Normal 69 (53.1%) 5 (35.7%) 20 (83.3%) 3 (37.5%) 9 (56.3%) 16 (53.4%) 16 (42.2%) .022* Mildly depressed 35 (26.9%) 4 (28.6%) 4 (16.7%) 3 (37.5%) 3 (18.7%) 10 (33.4%) 11 (28.9%) .699 Moderately-severely depressed 26 (20.0%) 5 (35.7%) 0 2 (25%) 4 (25%) 4 (13.2%) 11 (28.9%) .046* % Affected myocardium 70 ± 18.1 47 ± 7.9 64.8 ± 12.6 63.4 ± 9.4% 69 ± 15.8 85.1 ± 13.2 71.7 ± 18.1 < .001* Clinical features Genetic mutations 6 (4.6%) 0 0 2 (25%) 1 (6.2%) 3 (10%) 0 .021* Heart failure 85 (65.4%) 8 (57.1%) 12 (50%) 5 (62.5%) 10 (62.5%) 27 (90%) 23 (60.5%) .040* Arrythmia 75 (57.7%) 8 (57.1%) 13 (54.2%) 7 (87.5%) 8 (50%) 17 (56.7%) 22 (57.9%) .651 Amyloidosis-related neurological involvement 41 (31.5%) 4 (28.6%) 4 (16.7%) 4 (50%) 7 (43.8%) 9 (30%) 13 (34.2%) .418 Aortic stenosis 20 (15.4%) 3 (21.4%) 2 (8.3%) 1 (12.5%) 4 (25%) 5 (16.7%) 5 (13.2%) .769 Mortality 13 (10.0%) 0 3 (12.5%) 1 (12.5%) 5 (31.2%) 2 (6.7%) 2 (5.3%) .047* Demographic differences across myocardial uptake patterns Sex distribution varied significantly across the six radiotracer uptake patterns (p = 0.041). Septal, waning moon and vault patterns demonstrated a marked male predominance, with the vault pattern consisting exclusively of men. In contrast, diffuse and waxing moon patterns presented the highest proportions of female patients (40% and 31.2%, respectively), indicating a more balanced sex distribution within these phenotypes (Fig. 2 ). Age did not differ significantly among the uptake patterns (p = 0.471). Radiotracer uptake characteristics Perugini grade distribution showed a non-significant trend across patterns (p = 0.211). Diffuse and doughnut patterns predominated in patients with Perugini grade 3, whereas septal and waning moon patterns were more common among grade 2 patients. The vault pattern was observed exclusively in grade 3 individuals. Quantitative assessment of myocardial involvement (% affected myocardium) demonstrated significant differences across patterns (p < 0.001). Septal pattern exhibited the lowest involvement (47.0 ± 7.9%), followed by vault (63.4 ± 9.4%) and waning moon (64.8 ± 12.6%). Higher values were observed in doughnut (71.7 ± 18.1%) and diffuse patterns (85.1 ± 13.2%), with waxing moon showing intermediate involvement (69.0 ± 15.8%). Left ventricular function LVEF categories differed across uptake patterns. Normal LVEF (≥ 50%) was distributed unevenly (p = 0.022), occurring most frequently in the waning moon pattern (83.3%). Moderately reduced LVEF (< 40%) also varied significantly (p = 0.046), being more prevalent in septal (35.7%) and doughnut (28.9%) patterns. Mildly reduced LVEF (40–49%) showed no significant differences (p = 0.699). Clinical manifestations No significant differences were observed across patterns in the prevalence of arrhythmias (p = 0.651), amyloidosis-related neurological involvement (p = 0.418), or aortic stenosis (p = 0.769). Conversely, several variables showed statistically significant associations: Genetic mutations occurred exclusively in vault, waxing moon and diffuse patterns, and were absent in septal, waning moon and doughnut groups (p = 0.021). Heart failure was more common in diffuse and doughnut patterns (p = 0.040). Mortality differed significantly across patterns (p = 0.047), occurring predominantly in waxing moon and diffuse patterns and not observed in septal pattern. The distribution of these findings is presented in Fig. 4 . DISCUSSION ATTR-CA encompasses a spectrum of clinical phenotypes, the most characteristic being heart failure with preserved ejection fraction (HFpEF). Other manifestations, including autonomic or sensorineural neuropathy, aortic stenosis, atrial fibrillation and microvascular dysfunction, are also frequently reported. Several studies have demonstrated that these phenotypes correlate with findings on multimodality imaging, with the cardiac-predominant form commonly associated with more pronounced abnormalities on echocardiography, ECG and MRI¹⁰⁻¹³. The principal contribution of this study is the development of a gated SPECT/CT–based classification system for ATTR-CA that enables stepwise characterization of myocardial radiotracer distribution. This framework overcomes inherent limitations of the traditional visual Perugini grading system and, to our knowledge, represents the first tomographic pattern-based approach described for ATTR-CA, offering potential diagnostic and prognostic utility. Previous studies evaluating bisphosphonate scintigraphy have primarily focused on uptake intensity and its association with prognosis or echocardiographic parameters such as LVEF, strain or ventricular mass. In contrast, our classification integrates spatial distribution patterns with a broader range of clinical and pathophysiological features, expanding upon prior work¹⁴⁻¹⁵. Sex-related differences were evident across uptake patterns. Male patients predominantly exhibited phenotypes with greater myocardial involvement, whereas female patients more frequently demonstrated intermediate patterns, reinforcing emerging evidence of sex-specific variability in disease expression and progression¹⁶⁻¹⁷. Heart failure prevalence differed significantly among patterns. The septal and doughnut patterns demonstrated comparable HF rates despite markedly different extents of amyloid burden, suggesting that strategic localization—rather than solely total burden—plays a key role in functional impairment. Septal involvement, even when focal, may disrupt ventricular synchrony, whereas the doughnut pattern reflects more extensive structural compromise. The diffuse pattern showed the highest HF prevalence despite relatively preserved LVEF, consistent with an HFpEF-like phenotype driven primarily by diastolic dysfunction. These findings extend the conventional Perugini-based stratification by demonstrating that spatial tracer distribution provides additional prognostic information independent of global burden¹⁸⁻¹⁹. Mortality also varied across patterns, with waxing moon, waning moon and vault patterns showing the highest event rates, supporting previously reported associations between cardiac amyloid burden and adverse outcomes²⁰. This study also introduces a novel quantitative SPECT parameter—the percentage of affected myocardium—which provides a volumetric assessment of amyloid infiltration beyond traditional planar intensity measures or earlier SPECT quantification approaches. This parameter increased progressively across patterns and showed strong associations with clinical severity and heart failure, underscoring its potential utility as a biomarker of disease burden²¹⁻²⁴. Taken together, these findings highlight the added value of combining tomographic pattern classification with quantitative metrics to enhance phenotypic characterization and refine risk stratification in patients with ATTR-CA. Nevertheless, several limitations should be considered when interpreting our results. First, both the proposed gSPECT/CT classification and the quantitative parameter “percentage of affected myocardium” require external and multicenter validation, particularly across different scanner platforms, acquisition settings and reconstruction protocols. Second, the single-center design and the limited representation of some uptake patterns restrict generalizability and underscore the need for larger cohorts to confirm these observations. Although such constraints are expected in early-stage imaging biomarker development, the present framework establishes a reproducible basis for future studies, and its strong associations with clinical severity further support its potential utility pending validation against histology and long-term outcomes. CONCLUSION This study demonstrates that gSPECT/CT-derived myocardial uptake patterns are closely associated with distinct clinical phenotypes and functional profiles in ATTR-CA. The proposed classification system reveals meaningful relationships between specific spatial distribution patterns, quantitative myocardial involvement and key clinical outcomes, including heart failure and mortality. By integrating pattern-based interpretation with volumetric burden assessment, this approach provides a more comprehensive evaluation than Perugini grading alone. These findings support the incorporation of tomographic pattern stratification into clinical practice to enhance disease phenotyping and improve prognostic assessment in patients with ATTR-CA. Declarations Ethics approval The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional Ethics Committee of our center (reference PI-24-527-C). Informed consent Written informed consent was obtained from all participants prior to inclusion in the study. Conflict of interest The authors declare that they have no conflicts of interest related to this work. Funding No external funding was received for this study. Data availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. References Morfino P, Aimo A, Franzini M, Vergaro G, Castiglione V, Panichella G, et al. Pathophysiology of cardiac amyloidosis. Heart Fail Clin. 2024;20:261–270. 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Description of a different quantification method for amyloid burden (DPDload) and validation of SPECT/CT in cardiac amyloidosis. Rev Esp Med Nucl Imagen Mol. 2023;42:171–177. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8243972","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553170420,"identity":"fb1a6086-13b1-45f5-b04d-491e413fb734","order_by":0,"name":"FRANCISCO SEBASTIAN PALACID","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-6112-5227","institution":"Hospital Clínico Universitario Valladolid","correspondingAuthor":true,"prefix":"","firstName":"FRANCISCO","middleName":"SEBASTIAN","lastName":"PALACID","suffix":""},{"id":553170421,"identity":"131d1243-0c7b-42d8-a851-e9a6ad87ba6a","order_by":1,"name":"Noelia Álvarez Mena","email":"","orcid":"","institution":"Hospital Clínico Universitario of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Noelia","middleName":"Álvarez","lastName":"Mena","suffix":""},{"id":553170422,"identity":"c3e5ca68-8a98-4740-a3db-692cd7828da7","order_by":2,"name":"Berta Pérez López","email":"","orcid":"","institution":"Hospital Clínico Universitario of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Berta","middleName":"Pérez","lastName":"López","suffix":""},{"id":553170423,"identity":"c8b07242-3044-41a3-8467-79dee224df65","order_by":3,"name":"Claudia Gamazo Laherrán","email":"","orcid":"","institution":"Hospital Clínico Universitario of Valladolid","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"Gamazo","lastName":"Laherrán","suffix":""},{"id":553170424,"identity":"5b12de7a-444c-4eef-9dc3-691a56e8f530","order_by":4,"name":"María Paz Redondo Del Río","email":"","orcid":"","institution":"Faculty of Medicine. 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00:52:40","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60364,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/52b3eb5f636e2db4513883d6.png"},{"id":97299700,"identity":"7e2bd9fe-99d7-4ac3-a2a9-174723bbfaf8","added_by":"auto","created_at":"2025-12-03 00:52:40","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80791,"visible":true,"origin":"","legend":"","description":"","filename":"ANMED25005030structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/7ca59a3f4a2db99b610157a6.xml"},{"id":97299706,"identity":"4ec8acde-a060-4fff-968d-671d2a281d98","added_by":"auto","created_at":"2025-12-03 00:52:40","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":93359,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/c083e6f52031ff99c7f7f264.html"},{"id":97370332,"identity":"480d723f-6d6f-4b5b-b072-f98225287f4b","added_by":"auto","created_at":"2025-12-03 16:27:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1215854,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the proposed [⁹⁹ᵐTc]Tc-DPD myocardial uptake patterns on gSPECT/CT polar maps: (A) septal; (B) waning moon; (C) waxing moon; (D) vault; (E) diffuse; and (F) doughnut. Black-shaded areas indicate regions with absent radiotracer uptake.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/2051f37a3bda831a7c8545d9.png"},{"id":97369518,"identity":"daffdfcf-f947-4e0a-8f52-e4e2209013a8","added_by":"auto","created_at":"2025-12-03 16:25:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":186482,"visible":true,"origin":"","legend":"\u003cp\u003eStacked bar chart showing the distribution of patients by sex across the six myocardial radiotracer uptake patterns.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/b41c4da66ac9fdcd3ca468a0.png"},{"id":97368796,"identity":"604730a6-ddce-43ed-bddd-79b9db5aa8be","added_by":"auto","created_at":"2025-12-03 16:22:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":190695,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Boxplots showing the distribution of the percentage of affected myocardium across the six myocardial uptake patterns. The horizontal line represents the median and the box corresponds to the interquartile range. (B) Heat map illustrating the distribution of left ventricular ejection fraction (normal, mildly reduced and moderately reduced) across the different uptake patterns.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/3177efccec373fdc4e253e0e.png"},{"id":97368720,"identity":"c5d6e9ea-459a-4cfe-a81a-de826911c1c1","added_by":"auto","created_at":"2025-12-03 16:22:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":225437,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map showing the distribution of patients according to the assessed clinical variables across the six myocardial radiotracer uptake patterns.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/0708ed3d5ebe8e6c2b4da28e.png"},{"id":101753385,"identity":"ec60abce-c9eb-4327-9ec7-5516f8a02367","added_by":"auto","created_at":"2026-02-03 10:39:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2470428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8243972/v1/878903f4-2283-4a42-83dd-1cac23da1f7c.pdf"}],"financialInterests":"","formattedTitle":"Gated SPECT/CT uptake patterns in ATTR-CA: Association with myocardial burden, function and prognosis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCardiac transthyretin amyloidosis (ATTR-CA) results from progressive myocardial deposition of transthyretin fibrils, producing restrictive cardiomyopathy characterized by impaired compliance and evolving systolic dysfunction. Early stages typically present with diastolic impairment and preserved ejection fraction, while advanced disease may involve overt systolic compromise. Atrial infiltration is common and increases the risk of arrhythmias, thromboembolic complications and electromechanical dissociation\u0026sup1;\u0026sup2;.\u003c/p\u003e\u003cp\u003eNon-invasive imaging plays a central role in the diagnosis and characterization of ATTR-CA. Echocardiography and cardiac MRI provide structural and functional information, while [99mTc]-labelled bisphosphonate scintigraphy offers high diagnostic specificity and has become the cornerstone of non-biopsy diagnosis. The Perugini visual scale (grades 0\u0026ndash;3) remains the reference method for evaluating myocardial uptake; however, its semiquantitative nature does not account for spatial heterogeneity or pattern-based differences in radiotracer distribution that may carry clinical significance\u0026sup3;⁻⁵.\u003c/p\u003e\u003cp\u003eHybrid SPECT/CT with attenuation correction enhances quantitative accuracy and enables refined visualization of myocardial tracer localization. Despite these advances, the prognostic value of specific distribution patterns, their relationship with gated functional parameters and their association with disease severity remain incompletely understood⁶⁻⁹. A classification framework that integrates both qualitative and quantitative information derived from SPECT/CT could improve phenotypic characterization and support risk stratification.\u003c/p\u003e\u003cp\u003eIn this study, we introduce a gated SPECT/CT\u0026ndash;based classification system for ATTR-CA designed to systematically define myocardial uptake phenotypes. We aimed to validate these patterns and evaluate their associations with left ventricular ejection fraction (LVEF), clinical status and major outcomes. This approach seeks to extend the Perugini framework by providing additional imaging biomarkers relevant to disease staging and prognostic assessment in Nuclear Cardiology.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis prospective observational study included 130 consecutive patients with confirmed ATTR cardiac amyloidosis, defined by [99mTc]Tc-DPD scintigraphy showing Perugini grade\u0026thinsp;\u0026ge;\u0026thinsp;2 and exclusion of AL amyloidosis through serum and urine immunofixation and serum free light-chain analysis. All imaging studies were performed between January 2022 and December 2023, and clinical follow-up continued until manuscript preparation to assess all-cause mortality. All participants provided written informed consent prior to inclusion.\u003c/p\u003e\u003cp\u003ePatients were stratified by sex, age, Perugini grade (2 vs. 3), and left ventricular ejection fraction (LVEF) derived from transthoracic echocardiography. Additional clinical variables included the presence of TTR gene mutations, heart failure, arrhythmias, neurological involvement attributable to amyloidosis, and aortic stenosis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBone scintigraphy and ECG-gated SPECT/CT acquisition\u003c/h3\u003e\n\u003cp\u003eWhole-body planar scintigraphy was performed 180 minutes after intravenous administration of 736\u0026ndash;762 MBq of [99mTc]Tc-DPD. All patients underwent thoracic ECG-gated SPECT/CT using an NM/CT 870 DR system (GE Healthcare, Chicago, USA). The CT acquisition was used solely for attenuation correction.\u003c/p\u003e\u003cp\u003ePlanar imaging was performed using LEHR collimators at a scan speed of 14 cm/min. Gated SPECT/CT was acquired using a 180\u0026deg; step-and-shoot protocol (120 projections), 3-step frames of 25 s per projection, and 16 frames per cardiac cycle. CT parameters included 120 kV, 205 mA, and a slice thickness of 2.5 mm.\u003c/p\u003e\n\u003ch3\u003eImage processing and classification\u003c/h3\u003e\n\u003cp\u003eReconstruction of tomographic data was performed using Myovation Evolution (GE Healthcare) with an ordered-subset expectation-maximization (OSEM) algorithm (12 iterations, 10 subsets), including attenuation and scatter correction. Butterworth and Ramp filters were applied. Images were generated in IRACRR format and processed using the Emory Cardiac Toolbox (Emory University, Atlanta, USA) to obtain polar maps.\u003c/p\u003e\u003cp\u003eBased on radiotracer distribution, six predefined uptake patterns were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eSeptal: focal septal uptake;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWaning moon: uptake in septal, anterior and inferior walls;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eVault: uptake restricted to the anterior wall;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWaxing moon: uptake in lateral, anterior and inferior walls;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDiffuse: homogeneous uptake across the polar map;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDoughnut: diffuse uptake with relative apical sparing.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eQuantitative analysis was performed using QPS/QGS (Cedars-Sinai Medical Center), obtaining the percentage of affected myocardium, defined as the proportion of left ventricular myocardium exhibiting increased radiotracer uptake.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS Statistics (IBM Corp., Armonk, NY), version 10.1 for macOS. Associations between categorical variables and uptake patterns were evaluated with Fisher\u0026rsquo;s exact test applied to 2\u0026times;6 contingency tables. Owing to low expected frequencies in several subgroups, Monte Carlo simulation with 10,000 replicates was systematically applied.\u003c/p\u003e\u003cp\u003eContinuous variables (age, LVEF, percentage of affected myocardium) were compared using the Kruskal\u0026ndash;Wallis test due to unequal sample sizes and potential non-normality. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClinical and demographic characteristics\u003c/h2\u003e\u003cp\u003eA total of 130 patients (76.2% male, 23.8% female) were diagnosed with ATTR-CA based on positive [99mTc]Tc-DPD scintigraphy between January 2022 and December 2023 and were clinically followed until May 2025. Clinical and demographic characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics, clinical features, and scintigraphic parameters according to uptake patterns.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eUptake patterns\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSeptal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaning moon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVault\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWaxing moon\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDiffuse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDoughnut\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (10.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e38\u003c/p\u003e\u003cp\u003e(29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99 (76.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (85.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (87.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (68.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003cp\u003e(60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29\u003c/p\u003e\u003cp\u003e(76.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.041*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e(23.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.8 \u0026plusmn; 5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.6 \u0026plusmn; 5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.8 \u0026plusmn; 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.1 \u0026plusmn; 4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.7 \u0026plusmn; 5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e82.6 \u0026plusmn; 6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.8 \u0026plusmn; 5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerugini score grade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10\u003c/p\u003e\u003cp\u003e(26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerugini score grade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e106 (81.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (85.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (87.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25 (83.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28\u003c/p\u003e\u003cp\u003e(73.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLVEF (%)\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.9 \u0026plusmn; 15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.5 \u0026plusmn; 12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.6 \u0026plusmn; 12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.6 \u0026plusmn; 14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.8 \u0026plusmn; 18.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.5 \u0026plusmn; 17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51.4 \u0026plusmn; 13.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.291\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (53.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (83.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e(56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003cp\u003e(42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.022*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMildly depressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10 (33.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003cp\u003e(28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately-severely depressed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (35.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003cp\u003e(28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.046*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% Affected myocardium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 \u0026plusmn; 18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 \u0026plusmn; 7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.8 \u0026plusmn; 12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.4 \u0026plusmn; 9.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69 \u0026plusmn; 15.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85.1 \u0026plusmn; 13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e71.7 \u0026plusmn; 18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eClinical features\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenetic mutations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003cp\u003e(4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003cp\u003e(6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.021*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003cp\u003e(50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5 (62.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10 (62.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27\u003c/p\u003e\u003cp\u003e(90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23\u003c/p\u003e\u003cp\u003e(60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.040*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrythmia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (54.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7 (87.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003cp\u003e(50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17 (56.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e22\u003c/p\u003e\u003cp\u003e(57.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.651\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmyloidosis-related neurological involvement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003cp\u003e(43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003cp\u003e(30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13\u003c/p\u003e\u003cp\u003e(34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAortic stenosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (21.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003cp\u003e(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(16.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.769\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003cp\u003e(12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003cp\u003e(31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003cp\u003e(5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.047*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDemographic differences across myocardial uptake patterns\u003c/h3\u003e\n\u003cp\u003eSex distribution varied significantly across the six radiotracer uptake patterns (p\u0026thinsp;=\u0026thinsp;0.041). Septal, waning moon and vault patterns demonstrated a marked male predominance, with the vault pattern consisting exclusively of men. In contrast, diffuse and waxing moon patterns presented the highest proportions of female patients (40% and 31.2%, respectively), indicating a more balanced sex distribution within these phenotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAge did not differ significantly among the uptake patterns (p\u0026thinsp;=\u0026thinsp;0.471).\u003c/p\u003e\n\u003ch3\u003eRadiotracer uptake characteristics\u003c/h3\u003e\n\u003cp\u003ePerugini grade distribution showed a non-significant trend across patterns (p\u0026thinsp;=\u0026thinsp;0.211). Diffuse and doughnut patterns predominated in patients with Perugini grade 3, whereas septal and waning moon patterns were more common among grade 2 patients. The vault pattern was observed exclusively in grade 3 individuals.\u003c/p\u003e\u003cp\u003eQuantitative assessment of myocardial involvement (% affected myocardium) demonstrated significant differences across patterns (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Septal pattern exhibited the lowest involvement (47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9%), followed by vault (63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4%) and waning moon (64.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6%). Higher values were observed in doughnut (71.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1%) and diffuse patterns (85.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2%), with waxing moon showing intermediate involvement (69.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8%).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eLeft ventricular function\u003c/h2\u003e\u003cp\u003eLVEF categories differed across uptake patterns. Normal LVEF (\u0026ge;\u0026thinsp;50%) was distributed unevenly (p\u0026thinsp;=\u0026thinsp;0.022), occurring most frequently in the waning moon pattern (83.3%). Moderately reduced LVEF (\u0026lt;\u0026thinsp;40%) also varied significantly (p\u0026thinsp;=\u0026thinsp;0.046), being more prevalent in septal (35.7%) and doughnut (28.9%) patterns. Mildly reduced LVEF (40\u0026ndash;49%) showed no significant differences (p\u0026thinsp;=\u0026thinsp;0.699).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eClinical manifestations\u003c/h2\u003e\u003cp\u003eNo significant differences were observed across patterns in the prevalence of arrhythmias (p\u0026thinsp;=\u0026thinsp;0.651), amyloidosis-related neurological involvement (p\u0026thinsp;=\u0026thinsp;0.418), or aortic stenosis (p\u0026thinsp;=\u0026thinsp;0.769).\u003c/p\u003e\u003cp\u003eConversely, several variables showed statistically significant associations:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGenetic mutations occurred exclusively in vault, waxing moon and diffuse patterns, and were absent in septal, waning moon and doughnut groups (p\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHeart failure was more common in diffuse and doughnut patterns (p\u0026thinsp;=\u0026thinsp;0.040).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMortality differed significantly across patterns (p\u0026thinsp;=\u0026thinsp;0.047), occurring predominantly in waxing moon and diffuse patterns and not observed in septal pattern.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe distribution of these findings is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eATTR-CA encompasses a spectrum of clinical phenotypes, the most characteristic being heart failure with preserved ejection fraction (HFpEF). Other manifestations, including autonomic or sensorineural neuropathy, aortic stenosis, atrial fibrillation and microvascular dysfunction, are also frequently reported. Several studies have demonstrated that these phenotypes correlate with findings on multimodality imaging, with the cardiac-predominant form commonly associated with more pronounced abnormalities on echocardiography, ECG and MRI\u0026sup1;⁰⁻\u0026sup1;\u0026sup3;.\u003c/p\u003e\u003cp\u003eThe principal contribution of this study is the development of a gated SPECT/CT\u0026ndash;based classification system for ATTR-CA that enables stepwise characterization of myocardial radiotracer distribution. This framework overcomes inherent limitations of the traditional visual Perugini grading system and, to our knowledge, represents the first tomographic pattern-based approach described for ATTR-CA, offering potential diagnostic and prognostic utility.\u003c/p\u003e\u003cp\u003ePrevious studies evaluating bisphosphonate scintigraphy have primarily focused on uptake intensity and its association with prognosis or echocardiographic parameters such as LVEF, strain or ventricular mass. In contrast, our classification integrates spatial distribution patterns with a broader range of clinical and pathophysiological features, expanding upon prior work\u0026sup1;⁴⁻\u0026sup1;⁵.\u003c/p\u003e\u003cp\u003eSex-related differences were evident across uptake patterns. Male patients predominantly exhibited phenotypes with greater myocardial involvement, whereas female patients more frequently demonstrated intermediate patterns, reinforcing emerging evidence of sex-specific variability in disease expression and progression\u0026sup1;⁶⁻\u0026sup1;⁷.\u003c/p\u003e\u003cp\u003eHeart failure prevalence differed significantly among patterns. The septal and doughnut patterns demonstrated comparable HF rates despite markedly different extents of amyloid burden, suggesting that strategic localization\u0026mdash;rather than solely total burden\u0026mdash;plays a key role in functional impairment. Septal involvement, even when focal, may disrupt ventricular synchrony, whereas the doughnut pattern reflects more extensive structural compromise. The diffuse pattern showed the highest HF prevalence despite relatively preserved LVEF, consistent with an HFpEF-like phenotype driven primarily by diastolic dysfunction. These findings extend the conventional Perugini-based stratification by demonstrating that spatial tracer distribution provides additional prognostic information independent of global burden\u0026sup1;⁸⁻\u0026sup1;⁹.\u003c/p\u003e\u003cp\u003eMortality also varied across patterns, with waxing moon, waning moon and vault patterns showing the highest event rates, supporting previously reported associations between cardiac amyloid burden and adverse outcomes\u0026sup2;⁰.\u003c/p\u003e\u003cp\u003eThis study also introduces a novel quantitative SPECT parameter\u0026mdash;the percentage of affected myocardium\u0026mdash;which provides a volumetric assessment of amyloid infiltration beyond traditional planar intensity measures or earlier SPECT quantification approaches. This parameter increased progressively across patterns and showed strong associations with clinical severity and heart failure, underscoring its potential utility as a biomarker of disease burden\u0026sup2;\u0026sup1;⁻\u0026sup2;⁴.\u003c/p\u003e\u003cp\u003eTaken together, these findings highlight the added value of combining tomographic pattern classification with quantitative metrics to enhance phenotypic characterization and refine risk stratification in patients with ATTR-CA. Nevertheless, several limitations should be considered when interpreting our results. First, both the proposed gSPECT/CT classification and the quantitative parameter \u0026ldquo;percentage of affected myocardium\u0026rdquo; require external and multicenter validation, particularly across different scanner platforms, acquisition settings and reconstruction protocols. Second, the single-center design and the limited representation of some uptake patterns restrict generalizability and underscore the need for larger cohorts to confirm these observations. Although such constraints are expected in early-stage imaging biomarker development, the present framework establishes a reproducible basis for future studies, and its strong associations with clinical severity further support its potential utility pending validation against histology and long-term outcomes.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that gSPECT/CT-derived myocardial uptake patterns are closely associated with distinct clinical phenotypes and functional profiles in ATTR-CA. The proposed classification system reveals meaningful relationships between specific spatial distribution patterns, quantitative myocardial involvement and key clinical outcomes, including heart failure and mortality. By integrating pattern-based interpretation with volumetric burden assessment, this approach provides a more comprehensive evaluation than Perugini grading alone. These findings support the incorporation of tomographic pattern stratification into clinical practice to enhance disease phenotyping and improve prognostic assessment in patients with ATTR-CA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthics approval\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional Ethics Committee of our center (reference PI-24-527-C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eInformed consent\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflict of interest\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData availability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorfino P, Aimo A, Franzini M, Vergaro G, Castiglione V, Panichella G, et al. Pathophysiology of cardiac amyloidosis. Heart Fail Clin. 2024;20:261\u0026ndash;270.\u003c/li\u003e\n\u003cli\u003eGriffin JM, Rosenblum M, Maurer MS. Pathophysiology and therapeutic approaches to cardiac amyloidosis. Circ Res. 2021;128:1554\u0026ndash;1575.\u003c/li\u003e\n\u003cli\u003eAhluwalia N, Roshankar G, Draycott L, Jim\u0026eacute;nez-Zepeda V, Fine N, Chan D, et al. Diagnostic accuracy of bone scintigraphy imaging for cardiac amyloidosis: systematic review and meta-analysis. J Nucl Cardiol. 2023;30:2464\u0026ndash;2476.\u003c/li\u003e\n\u003cli\u003eGotuzzo I, Slart RHJA, Gimelli A, Ashri N, Anagnostopoulos C, Bucerius J, et al. Nuclear medicine practice for the assessment of cardiac sarcoidosis and amyloidosis: a survey endorsed by the EANM and EACVI. Eur J Nucl Med Mol Imaging. 2024;51:1809\u0026ndash;1815.\u003c/li\u003e\n\u003cli\u003eZhao M, Calabretta R, Yu J, Binder P, Hu S, Hacker M, et al. Nuclear molecular imaging of disease burden and response to treatment for cardiac amyloidosis. Biology (Basel). 2022;11:1395.\u003c/li\u003e\n\u003cli\u003eGherghe M, Lazar AM, Sterea MC, Spiridon PM, Motas N, Gales LN, et al. Quantitative SPECT/CT parameters in the assessment of transthyretin cardiac amyloidosis: a new dimension of molecular imaging. J Cardiovasc Dev Dis. 2023;10:242.\u003c/li\u003e\n\u003cli\u003eKessler L, Fragoso P, Kersting D, Jentzen W, Weber M, L\u0026uuml;dike P, et al. Quantitative 99mTc-DPD SPECT/CT assessment of cardiac amyloidosis. J Nucl Cardiol. 2023;30:101\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eBen-Haim S, Chicheportiche A, Goshen E, Arad M, Smekhov M, Menezes LJ, et al. Quantitative SPECT/CT parameters of myocardial 99mTc-DPD uptake in suspected cardiac transthyretin amyloidosis. Eur J Nucl Med Mol Imaging. 2021;48:86.\u003c/li\u003e\n\u003cli\u003eSebasti\u0026aacute;n F, \u0026Aacute;lvarez N, Garc\u0026iacute;a M, Zambrano RC, Jaramillo BM, G\u0026oacute;mez J, et al. Role of [99mTc]Tc-DPD gated SPECT/CT in the assessment of myocardial uptake patterns in transthyretin amyloidosis (TTR-CA). Rev Esp Med Nucl Imagen Mol. 2025;44:500081.\u003c/li\u003e\n\u003cli\u003ePour-Ghaz I, Bath A, Kayali S, Alkhatib D, Yedlapati N, Rhea I, et al. A review of cardiac amyloidosis: presentation, diagnosis and treatment. Curr Probl Cardiol. 2022;47:101366.\u003c/li\u003e\n\u003cli\u003eHealy L, Giblin G, Gray A, Starr N, Murphy L, O\u0026rsquo;Sullivan D, et al. Prevalence of transthyretin cardiac amyloidosis in undifferentiated heart failure with preserved ejection fraction. ESC Heart Fail. 2025;12:1176\u0026ndash;1182.\u003c/li\u003e\n\u003cli\u003eAbouezzeddine OF, Davies DR, Scott CG, Fayyaz AU, Askew JW, McKie PM, et al. Prevalence of transthyretin amyloid cardiomyopathy in heart failure with preserved ejection fraction. JAMA Cardiol. 2021;6:1267\u0026ndash;1274.\u003c/li\u003e\n\u003cli\u003eIoannou A, Patel RK, Razvi Y, Porcari A, Knight D, Mart\u0026iacute;nez-Naharro A, et al. Multi-imaging characterization of cardiac phenotype in different types of amyloidosis. JACC Cardiovasc Imaging. 2023;16:464\u0026ndash;477.\u003c/li\u003e\n\u003cli\u003eHarapoz M, Evans S, Geenty P, Kwok F, Stewart G, Taylor MS, et al. Correlation of quantitative 99mTc-DPD scintigraphy with echocardiographic alterations in left atrial parameters in transthyretin amyloidosis. Heart Lung Circ. 2022;31:804\u0026ndash;814.\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;fbacka V, Axelsson J, Pilebro B, Suhr OB, Lindqvist P, Sundstr\u0026ouml;m T. Cardiac transthyretin amyloidosis 99mTc-DPD SPECT correlates with strain echocardiography and biomarkers. Eur J Nucl Med Mol Imaging. 2021;48:1822\u0026ndash;1832.\u003c/li\u003e\n\u003cli\u003eAimo A, Panichella G, Garofalo M, Gasparini S, Arzilli C, Castiglione V, et al. Sex differences in transthyretin cardiac amyloidosis. Heart Fail Rev. 2024;29:321\u0026ndash;330.\u003c/li\u003e\n\u003cli\u003eVilches S, Mart\u0026iacute;nez-Avial M, M\u0026eacute;ndez I, G\u0026oacute;mez C, Espinosa MA. Sex differences in transthyretin cardiac amyloidosis: unraveling the complexities in epidemiology, pathophysiology, diagnosis and treatment. Curr Heart Fail Rep. 2024;21:344\u0026ndash;353.\u003c/li\u003e\n\u003cli\u003eNitsche C, Mascherbauer K, Calabretta R, Koschutnik M, Dona C, Dannernberg V, et al. Prevalence and outcomes of cardiac amyloidosis in all-comer referrals for bone scintigraphy. J Nucl Med. 2022;63:1906\u0026ndash;1911.\u003c/li\u003e\n\u003cli\u003eSpielvogel CP, Haberl D, Mascherbauer K, Ning J, Kluge K, Traub-Weidinger T, et al. Diagnosis and prognosis of abnormal cardiac scintigraphy uptake suggestive of cardiac amyloidosis using artificial intelligence: a retrospective, international, multicenter, cross-tracer development and validation study. Lancet Digit Health. 2024;6:e251\u0026ndash;e260.\u003c/li\u003e\n\u003cli\u003eCho SG, Han S. Prognostic value of bone scintigraphy in cardiac amyloidosis: a systematic review and meta-analysis. Clin Nucl Med. 2025;50:e34\u0026ndash;e40.\u003c/li\u003e\n\u003cli\u003eRettl R, Duca F, Kronberger C, Binder C, Willixhofer R, Ermolaev N, et al. Prognostic implication of DPD quantification in transthyretin cardiac amyloidosis. Eur Heart J Cardiovasc Imaging. 2025;26:251\u0026ndash;260.\u003c/li\u003e\n\u003cli\u003eMiller RJH, Shanbhag A, Michalowska AM, Kavanagh P, Liang JX, Builoff V, et al. Deep learning-enabled quantification of 99mTc-pyrophosphate SPECT/CT for cardiac amyloidosis. J Nucl Med. 2024;65:1144\u0026ndash;1150.\u003c/li\u003e\n\u003cli\u003eSebasti\u0026aacute;n F, \u0026Aacute;lvarez N, Zambrano RC, Garc\u0026iacute;a M, Alonso M, P\u0026eacute;rez B, et al. Absolute quantification of myocardial uptake of 99mTc-DPD in patients with cardiac amyloidosis due to transthyretin deposits (ATTR). Rev Esp Med Nucl Imagen Mol. 2023;42:302\u0026ndash;309.\u003c/li\u003e\n\u003cli\u003eMall\u0026oacute;n MC, Abou Johk E, Abou Johk C, Aguad\u0026eacute; S, Ruibal A, Pubul V. Description of a different quantification method for amyloid burden (DPDload) and validation of SPECT/CT in cardiac amyloidosis. Rev Esp Med Nucl Imagen Mol. 2023;42:171\u0026ndash;177.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gated SPECT/CT, transthyretin cardiac amyloidosis, myocardial uptake patterns, [⁹⁹ᵐTc]Tc-DPD scintigraphy, cardiac imaging, amyloid burden quantification","lastPublishedDoi":"10.21203/rs.3.rs-8243972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8243972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGated SPECT/CT (gSPECT/CT) offers detailed myocardial radiotracer distribution imaging in cardiac transthyretin amyloidosis (ATTR-CA), but lacks a standardized classification. We aimed to develop and validate a gSPECT/CT-based classification system for ATTR-CA and to explore its correlation with clinical profile, cardiac function, and outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted an observational study in 130 patients (76.2% male) with confirmed ATTR-CA who underwent planar [99mTc]Tc-DPD scintigraphy and thoracic gSPECT/CT. Myocardial polar maps were constructed and uptake patterns were classified as septal, waning moon, vault, waxing moon, diffuse, or doughnut. Clinical, echocardiographic and genetic data were compared across patterns. A quantitative parameter\u0026mdash;the percentage of affected myocardium\u0026mdash;was derived from SPECT segmentation. Statistical analyses included Kruskal\u0026ndash;Wallis and Fisher\u0026rsquo;s exact tests (Monte Carlo simulation).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe proportion of female patients differed significantly among patterns (p\u0026thinsp;=\u0026thinsp;0.041). Diffuse and doughnut patterns predominated in patients with Perugini grade 3 uptake. Myocardial involvement increased progressively across patterns (47% in septal to 85% in diffuse; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). LVEF distribution varied: preserved LVEF was more frequent in the waning moon group, while reduced LVEF predominated in septal and doughnut patterns. Heart failure was more common in diffuse and doughnut groups (p\u0026thinsp;=\u0026thinsp;0.040), and mortality was highest in waxing moon and diffuse groups (p\u0026thinsp;=\u0026thinsp;0.047). Genetic variants were detected exclusively in vault, waxing moon and diffuse patterns (p\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe propose a novel gSPECT/CT-based classification for ATTR-CA that combines qualitative uptake patterns with quantitative myocardial involvement. This system correlates strongly with sex, LVEF, heart failure, genetic findings and mortality, improving phenotypic characterization and potentially aiding risk stratification.\u003c/p\u003e","manuscriptTitle":"Gated SPECT/CT uptake patterns in ATTR-CA: Association with myocardial burden, function and prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 00:52:35","doi":"10.21203/rs.3.rs-8243972/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fd14c678-dbe6-44f5-9cf1-6cfa7218dd70","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T02:58:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 00:52:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8243972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8243972","identity":"rs-8243972","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}