Optimisation and usefulness of quantitative analysis of (18)F-florbetapir PET
| Autor: | Paresh Malhotra, Neva Patel, William Svensson, Christopher Carswell, Richard Perry, Sameer Khan, Zarni Win, Kuldip Nijran, Tara Barwick, Sairah Khan, Daniel Fakhry-Darian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male MILD COGNITIVE IMPAIRMENT Fluorine Radioisotopes Amyloid pet Brain mapping Sensitivity and Specificity 030218 nuclear medicine & medical imaging 03 medical and health sciences POSITRON-EMISSION-TOMOGRAPHY 0302 clinical medicine Nuclear magnetic resonance Alzheimer Disease Advances in neurodegenerative and psychiatric imaging special feature: Full paper medicine Humans Radiology Nuclear Medicine and imaging Aged Aged 80 and over Brain Mapping Science & Technology Aniline Compounds medicine.diagnostic_test Chemistry Radiology Nuclear Medicine & Medical Imaging Brain Reproducibility of Results 1103 Clinical Sciences General Medicine QUANTIFICATION Middle Aged medicine.disease FLORBETAPIR F 18 AMYLOID PET ALZHEIMERS-DISEASE Nuclear Medicine & Medical Imaging Positron emission tomography Positron-Emission Tomography Ethylene Glycols Female Alzheimer's disease Radiopharmaceuticals Life Sciences & Biomedicine Quantitative analysis (chemistry) 030217 neurology & neurosurgery |
| Zdroj: | Br J Radiol |
| Popis: | Objectives:This study investigates the usefulness of quantitative SUVR thresholds on sub types of typical (type A) and atypical (non-type A) positive (Aβ+) and negative (Aβ-)18F-florbetapir scans and aims to optimise the thresholds.Methods:Clinical18F-florbetapir scans (n = 100) were categorised by sub type and visual reads were performed independently by three trained readers. Inter-reader agreement and reader-to-reference agreement were measured. Optimal SUVR thresholds were derived by ROC analysis and were compared with thresholds derived from a healthy control group and values from published literature.Results:Sub type division of18F-florbetapir PET scans improves accuracy and agreement of visual reads for type A: accuracy 90%, 96% and 70% and agreement κ > 0.7, κ ≥ 0.85 and −0.1 < κ < 0.9 for all data, type A and non-type A respectively. Sub type division also improves quantitative classification accuracy of type A: optimum mcSUVR thresholds were found to be 1.32, 1.18 and 1.48 with accuracy 86%, 92% and 76% for all data, type A and non-type A respectively.Conclusions:Aβ+/Aβ- mcSUVR threshold of 1.18 is suitable for classification of type A studies (sensitivity = 97%, specificity = 88%). Region-wise SUVR thresholds may improve classification accuracy in non-type A studies. Amyloid PET scans should be divided by sub type before quantification.Advances in knowledge:We have derived and validated mcSUVR thresholds for Aβ+/Aβ-18F-florbetapir studies. This work demonstrates that division into sub types improves reader accuracy and agreement and quantification accuracy in scans with typical presentation and highlights the atypical presentations not suited to global SUVR quantification. |
| Databáze: | OpenAIRE |
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