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. 2019 Jul 4;9(1):59.
doi: 10.1186/s13550-019-0528-3.

Diagnostic performance of regional cerebral blood flow images derived from dynamic PIB scans in Alzheimer's disease

Affiliations

Diagnostic performance of regional cerebral blood flow images derived from dynamic PIB scans in Alzheimer's disease

Débora E Peretti et al. EJNMMI Res. .

Abstract

Background: In clinical practice, visual assessment of glucose metabolism images is often used for the diagnosis of Alzheimer's disease (AD) through 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) scans. However, visual assessment of the characteristic AD hypometabolic pattern relies on the expertise of the reader. Therefore, user-independent pipelines are preferred to evaluate the images and to classify the subjects. Moreover, glucose consumption is highly correlated with cerebral perfusion. Regional cerebral blood flow (rCBF) images can be derived from dynamic 11C-labelled Pittsburgh Compound B PET scans, which are also used for the assessment of the deposition of amyloid-β plaques on the brain, a fundamental characteristic of AD. The aim of this study was to explore whether these rCBF PIB images could be used for diagnostic purposes through the PMOD Alzheimer's Discrimination Tool.

Results: Both tracer relative cerebral flow (R1) and early PIB (ePIB) (20-130 s) uptake presented a good correlation when compared to FDG standardized uptake value ratio (SUVR), while ePIB (1-8 min) showed a worse correlation. All receiver operating characteristic curves exhibited a similar shape, with high area under the curve values, and no statistically significant differences were found between curves. However, R1 and ePIB (1-8 min) had the highest sensitivity, while FDG SUVR had the highest specificity.

Conclusion: rCBF images were suggested to be a good surrogate for FDG scans for diagnostic purposes considering an adjusted threshold value.

Keywords: Alzheimer’s disease; PALZ; PIB; Relative cerebral blood flow.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Representative studies. Transaxial slices of the brain are shown. From left to right: FDG SUVR images, R1 parametric maps, ePIB (20–130 s), and ePIB (1–8 min) images. On the first row, images from an AD patient; on the second, an MCI+ subject; on the third, an MCI− subject; and at the bottom, an HC subject. All colour scales are adjusted to the same range
Fig. 2
Fig. 2
Distribution of PETSCORES per method. Distribution of subjects’ PETSCORES from FDG SUVR, R1, ePIB (20–130 s), and ePIB (1–8 min) respectively from left to right. Darkest grey boxes represent data from the AD group; dark grey represents MCI+ subjects; light grey represents MCI−; and white represents HC. A dashed line at PETSCORE = 1 represents the threshold from PALZ for the classification of AD patients. The stars represent the differences between the groups that are statistically significant
Fig. 3
Fig. 3
Scatter and Bland-Altman plots. Scatter plots (first column) showing PETSCORES from R1 parametric maps (top), ePIB (20–130 s) (middle), and ePIB (1–8 min) images (bottom) (y-axis), and from FDG SUVR (x-axis). The dashed lines display the identity line. Results of the linear regression are given in boxes at the bottom right corner. Bland-Altman plots (second column) showing the difference between the PETSCORES provided by R1 (top), ePIB (20–130 s) (middle), and ePIB (1–8 min) and FDG SUVR. The full line is at the mean difference value for all scores, and the dashed lines delimit the 95% agreement interval (at mean ± 1.96 × standard deviation). Data are arranged according to subject group: circles represent the AD, triangles the MCI+, squares the MCI−, and cross the HC group
Fig. 4
Fig. 4
ROC plots. ROC plot with the curves of FDG SUVR (solid line), R1 (dashed line), ePIB (20–130 s) (dotted line), and ePIB (1–8 min) (dot dashed line)

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