ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
Cardiac amyloidosis characterization by kinetic model fitting on [18F]florbetaben PET images.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Positron emission tomography in the diagnosis and follow-up of transthyretin amyloid cardiomyopathy patients: A systematic review.European journal of nuclear medicine and molecular imaging · 2023Pooled it
- Kinetic modeling with total body PET-current status and future applications.The British journal of radiology · 2026Review
- Diagnostic value of positron emission tomography using amyloid-binding radiotracers for cardiac amyloidosis: a systematic review and meta-analysis.European heart journal. Imaging methods and practice · 2026Article
- Article
- Automated Neural Architecture Search for Cardiac Amyloidosis Classification from [18F]-Florbetaben PET Images.Journal of imaging informatics in medicine · 2025Article
- Positron emission tomography in cardiac amyloidosis: current evidence and future directions.Heart failure reviews · 2025Review
- 2025 Update Consensus ofActa Cardiologica Sinica · 2025Article
- The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine.The British journal of radiology · 2023Review
- Molecular Imaging of Systemic and Cardiac Amyloidosis: Recent Advances and Focus on the Future.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2023Review
- Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: positron emission tomography, computed tomography, and magnetic resonance.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- The flutemetamol analogue cyano-flutemetamol detects myocardial AL and ATTR amyloid deposits: a post-mortem histofluorescence analysis.Amyloid : the international journal of experimental and clinical investigation : the official journal of the International Society of Amyloidosis · 2023Article
- AL Amyloidosis for Cardiologists: Awareness, Diagnosis, and Future Prospects:JACC. CardioOncology · 2022Review
- Nuclear Molecular Imaging of Disease Burden and Response to Treatment for Cardiac Amyloidosis.Biology · 2022Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the feasibility of kinetic modeling-based approaches from [18F]-Flobetaben dynamic PET images as a non-invasive diagnostic method for cardiac amyloidosis (CA) and to identify the two AL- and ATTR-subtypes. METHODS AND
resultsTwenty-one patients with diagnoses of CA (11 patients with AL-subtype and 10 patients with ATTR-subtype of CA) and 15 Control patients with no-CA conditions underwent PET/CT imaging after [18F]Florbetaben bolus injection. A two-tissue-compartment (2TC) kinetic model was fitted to time-activity curves (TAC) obtained from left ventricle wall and left atrium cavity ROIs to estimate kinetic micro- and macro-parameters. Combinations of kinetic parameters were evaluated with the purpose of distinguishing Control subjects and CA patients, and to correctly label the last ones as AL- or ATTR-subtype. Resulting sensitivity, specificity, and accuracy for Control subjects were: 0.87, 0.9, 0.89; as far as CA patients, the sensitivity, specificity, and accuracy were respectively 0.9, 1, and 0.97 for AL-CA patients and 0.9, 0.92, 0.97 for ATTR-CA patients.
conclusionPharmacokinetic analysis based on a 2TC model allows cardiac amyloidosis characterization from dynamic [18F]Florbetaben PET images. Estimated model parameters allows to not only distinguish between Control subjects and patients, but also between AL- and ATTR-amyloid patients.
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