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. 2025 Nov 29.
doi: 10.1007/s00259-025-07673-6. Online ahead of print.

Impact of disease-modifying therapy on [99mTc]Tc-DPD SPECT/CT markers in transthyretin cardiac amyloidosis enabled by artificial intelligence

Clemens P Spielvogel  1   2 , Markus Köfler  3 , Zewen Jiang  3 , Jing Ning  3 , Josef Yu  3 , David Haberl  3   4 , Christina Kronberger  5 , Michael Poledniczek  5 , Lena Marie Schmid  5 , David Kersting  6 , Nikita Ermolaev  5 , Roza B Eslam  5 , Michaela Auer-Grumbach  7 , Christina Binder  5 , Franz Duca  5 , Christian Nitsche  5 , Johannes Kastner  5 , Jutta Bergler-Klein  5 , Andreas A Kammerlander  5 , Christian Hengstenberg  5 , Marcus Hacker  3 , Raffaella Calabretta  3 , René Rettl  5
Affiliations

Impact of disease-modifying therapy on [99mTc]Tc-DPD SPECT/CT markers in transthyretin cardiac amyloidosis enabled by artificial intelligence

Clemens P Spielvogel et al. Eur J Nucl Med Mol Imaging. .

Abstract

Purpose: Transthyretin cardiac amyloidosis (ATTR-CM) is a progressive, underdiagnosed disease with high morbidity and mortality. While disease-modifying therapies (DMTs) slow progression, early treatment response markers remain scarce. This study assessed AI-quantified thoracic [99mTc]Tc-DPD SPECT/CT markers as potential non-invasive biomarkers for monitoring therapeutic efficacy.

Methods: This longitudinal study included ATTR-CM patients receiving DMTs (transthyretin stabilizers, RNA interference, or antisense oligonucleotides). [99mTc]Tc-DPD SPECT/CT scans were acquired at baseline and after treatment (median interval 9 months, IQR 7-10). AI-driven segmentation and quantification extracted 26 markers (SUV metrics, retention index, amyloid-affected volume, and amyloid activity). Functional, clinical, and blood parameters, as well as clinical outcomes, were evaluated for their association with changes in imaging markers.

Results: In 45 patients (37 ATTRwt-CM, 8 ATTRv-CM), 65% (17/26) of AI-extracted SPECT/CT markers significantly decreased after treatment (all p < 0.001), including SUVmax reductions in the left ventricle (18.6 to 14.1) and myocardium (19.5 to 15.5). None of the markers significantly increased (p > 0.05). Six of the imaging markers, most notably SUVpeak (p = 0.007) of the myocardium and amyloid activity of the left ventricle (p = 0.009), were associated with reductions in NT-proBNP. Lower values for three markers, including amyloid activity of the myocardium, retention index, and SUVmean of the left atrium (all p = 0.016), were associated with improved NYHA class. An increase in amyloid-affected volume of the right ventricle (HR 3.19, 95% CI [1.29; 7.86], p = 0.005) and a decrease in right ventricular SUVmean (adjHR 0.15 95% CI [0.02;1.10], logrank p = 0.030) were associated with death or heart failure-associated hospitalization before and after multivariate adjustment. AI-driven analysis extracted imaging markers substantially faster and eliminated inter-rater variability.

Conclusion: AI-driven [99mTc]Tc-DPD SPECT/CT analysis effectively detects treatment-induced reductions in cardiac amyloid burden, offering a non-invasive biomarker for early response assessment in ATTR-CM. AI-enabled imaging markers enhance reproducibility and efficiency, providing valuable support for personalized treatment strategies as new therapeutic options for ATTR-CM become available.

Keywords: Artificial intelligence; Cardiac amyloidosis; SPECT/CT; Transthyretin; Treatment response.

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Conflict of interest statement

Declarations. Ethics approval: The study was conducted in alignment with the Declaration of Helsinki, and approved by the institutional review board of the Medical University of Vienna (1079/2023). Consent to participate: Written informed consent was obtained from all participants prior to their inclusion in the registry for baseline and follow-up assessments. Consent to publish: All individuals agreed to the publication of this manuscript. Competing interests: CPS received consulting and speaker fees from Pfizer. RR received speaker fees and congress support from Akcea Therapeutics, Alnylam Pharmaceuticals, AstraZeneca, Bayer, Pfizer Inc., and Swedish Orphan Biovitrum, as well as research grants from Pfizer Inc. FD received Speaker fees and congress support from Bayer, Novartis, Alnylam, Pfizer, and AOP, as well as research grants from the Austrian Society of Cardiology, Pfizer, Alnylam, and Bayer. CN reports speaker/consulting honoraria from Pfizer, Bayer, Prothena, and Böhringer Ingelheim and research contracts with Pfizer, AstraZeneca, the Austrian Society of Cardiology, the European Association of Cardiovascular Imaging, and the Austrian Science Fund. DK has received support from the German Research Foundation (UMEA Clinician Scientist Academy, FU356/12 − 2, and KE2933/1–1) and reports fees from Pfizer (research funding, speaker), Novartis (speaker), and GE Healthcare (consultant) outside the submitted work. MH has received lecture fees from Siemens Healthineers and GE Healthcare. MH has received consulting fees from Evomics. The remaining authors have nothing to declare.

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