Dataset Information

Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD.

ABSTRACT:

Objective

To compare the diagnostic performance of PET with the amyloid ligand Pittsburgh compound B (PiB-PET) to fluorodeoxyglucose (FDG-PET) in discriminating between Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD).

Methods

Patients meeting clinical criteria for AD (n = 62) and FTLD (n = 45) underwent PiB and FDG-PET. PiB scans were classified as positive or negative by 2 visual raters blinded to clinical diagnosis, and using a quantitative threshold derived from controls (n = 25). FDG scans were visually rated as consistent with AD or FTLD, and quantitatively classified based on the region of lowest metabolism relative to controls.

Results

PiB visual reads had a higher sensitivity for AD (89.5% average between raters) than FDG visual reads (77.5%) with similar specificity (PiB 83%, FDG 84%). When scans were classified quantitatively, PiB had higher sensitivity (89% vs 73%) while FDG had higher specificity (83% vs 98%). On receiver operating characteristic analysis, areas under the curve for PiB (0.888) and FDG (0.910) were similar. Interrater agreement was higher for PiB (κ = 0.96) than FDG (κ = 0.72), as was agreement between visual and quantitative classification (PiB κ = 0.88-0.92; FDG κ = 0.64-0.68). In patients with known histopathology, overall classification accuracy (2 visual and 1 quantitative classification per patient) was 97% for PiB (n = 12 patients) and 87% for FDG (n = 10).

Conclusions

PiB and FDG showed similar accuracy in discriminating AD and FTLD. PiB was more sensitive when interpreted qualitatively or quantitatively. FDG was more specific, but only when scans were classified quantitatively. PiB slightly outperformed FDG in patients with known histopathology.

SUBMITTER: Rabinovici GD

PROVIDER: S-EPMC3236517 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (? 2?years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-? (A?) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included A?1-42, A?1-40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ?4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., ?story recall: ??=?+0.43 [p?<?0.001] and +?0.37 [p?=?0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline A? burden only if SUVR was referenced to the subcortical white matter (e.g., ?story recall: ??=?-0.28 [p?=?0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters.