2020
DOI: 10.1038/s41380-020-0815-4
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[18F]THK5317 imaging as a tool for predicting prospective cognitive decline in Alzheimer’s disease

Abstract: Cross-sectional studies have indicated potential for positron emission tomography (PET) in imaging tau pathology in Alzheimer’s disease (AD); however, its prognostic utility remains unproven. In a longitudinal, multi-modal, prognostic study of cognitive decline, 20 patients with a clinical biomarker-based diagnosis in the AD spectrum (mild cognitive impairment or dementia and a positive amyloid-beta PET scan) were recruited from the Cognitive Clinic at Karolinska University Hospital. The participants underwent… Show more

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Cited by 16 publications
(15 citation statements)
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“…The ligands of the THK family have been the only ones from all developed tau PET ligands that were tested longitudinally in a phase 3 study at the time of writing this review [69]. The generalizability of these promising results is, however, subject to several gaps that remain to be filled as research priorities for assessing the clinical validity of the THK ligands: namely, the assessment of (1) the discriminative ability of the assay in pathology confirmed AD and HC samples for phase 2; (2) the accuracy of the biomarker to discriminate between AD and non-AD neurodegenerative disease in adequately powered studies; (3) the consistency of the antemortem and the histopathological measurements of tau pathology, which remains, so far, inconclusive; (4) covariates that are associated with the biomarker status in patients with AD and HCs; (5) the replicability of the available evidence about the biomarker's diagnostic performance at the MCI stage; (6) criteria of biomarker positivity.…”
Section: Discussionmentioning
confidence: 99%
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“…The ligands of the THK family have been the only ones from all developed tau PET ligands that were tested longitudinally in a phase 3 study at the time of writing this review [69]. The generalizability of these promising results is, however, subject to several gaps that remain to be filled as research priorities for assessing the clinical validity of the THK ligands: namely, the assessment of (1) the discriminative ability of the assay in pathology confirmed AD and HC samples for phase 2; (2) the accuracy of the biomarker to discriminate between AD and non-AD neurodegenerative disease in adequately powered studies; (3) the consistency of the antemortem and the histopathological measurements of tau pathology, which remains, so far, inconclusive; (4) covariates that are associated with the biomarker status in patients with AD and HCs; (5) the replicability of the available evidence about the biomarker's diagnostic performance at the MCI stage; (6) criteria of biomarker positivity.…”
Section: Discussionmentioning
confidence: 99%
“…Phase 3: primary aim 1: to evaluate the biomarker ability in the detection of the disease at the earliest clinical stage (MCI due to AD) using conversion to AD dementia as the reference standard A single small-scale monocentric study has, so far, investigated the accuracy of THK5317 in discriminating between amyloid-β positive cognitively impaired patients that will remain cognitively stable (considered being non-AD-related cognitive impairment) from those who deteriorated further cognitively to clinical AD dementia, with an average followup interval of 4 years [69]. According to the latter study, the accuracy of THK5317 in temporal regions of interest was excellent, up to 100%.…”
Section: Phase 3: Prospective Longitudinal Repository Studiesmentioning
confidence: 99%
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“…Phase 3. Secondary aim 4: To determine an interval able to detect a meaningful change of biomarker status or level in progressing MCI While longitudinal studies for the firstgeneration tau PET tracer have been examined up to f4 year follow-up in MCI patients [52,53], longitudinal studies for the second-generation tau PET tracers including prodromal disease stages are still underway.…”
Section: Phase 3 Secondary Aimmentioning
confidence: 99%
“…The same image processing protocol was applied to image data from both tracers. For all participants from both the KI and the ADNI cohorts, we segmented the T1-weighted MRI images into grey and white matter (GM and WM, respectively) tissue classes using SPM8 software's unified segmentation, using a corroborated pipeline from our group [24,29,30]. We then used the inverse non-linear transformation from the T1-weighted MRI segmentation step to warp the Hammers probabilistic atlas [31] from the Montreal Neurological Institute (MNI) space to each individual's native T1-weighted MRI space.…”
Section: Pet Image Processing and Quantificationmentioning
confidence: 99%