Concordance in Detecting Amyloid Positivity Between 18F-florbetaben and 18F-flutemetamol Amyloid PET Using Quantitative and Qualitative Assessments

Abstract

Background In the present study, the discrepancy in detecting amyloid beta (Aβ) positivity between ¹⁸F-florbetaben (FBB) and ¹⁸F-flutemetamol (FMM) positron emission tomography (PET) was quantitatively and qualitatively assessed.

Methods Paired FBB and FMM PET images were obtained on separate days from 107 participants. Three experts visually quantified the scans as positive or negative for Aβ deposition. Quantitative assessment was performed using global cortical standardized uptake value ratio (SUVR) with the whole cerebellum (WC) as a reference region.

Results Inter-rater agreement was excellent for FBB (Fleiss k = 0.86) and FMM (Fleiss k = 0.78). The concordance rates between FBB and FMM were 94.4% (101/107) for visual assessment and 98.1% (105/107) for SUVR cut-off categorisation. Both FBB and FMM showed high agreement rates between visual assessment and SUVR positive or negative categorisation (93.5% in FBB and 91.2% in FMM). When the two ligands were compared based on SUVR cut-off categorisation as standard of truth, although not statistically significant, the false-positive rate (visual assessment-positive, SUVR-negative) was higher in FMM (9.1%) than in FBB (1.8%) (P = 0.13). In those cases, assessing uptakes in the lateral temporal cortex was often more problematic than in other regions in visual analysis, especially near the border with the occipital lobe.

Conclusion Our findings suggested that both FBB and FMM had excellent agreement when used to quantitatively and qualitatively evaluate Aβ deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers can be useful in future research.

Background

Amyloid positron emission tomography (PET) is a widely used biomarker-supported method for diagnosing Alzheimer’s disease (AD) [1]. To determine amyloid beta (Aβ) peptide deposition positivity, visual assessment is generally performed by an expert and quantitative assessment used for research purposes [2]. Visual assessment is determined primarily by tracer uptake in grey matter. Previously, visual assessment showed high agreement with autopsy findings, however, the results may differ depending on the inter-rater discrepancy, expert's skill, and type of ligand [3].

A quantitative method for assessing amyloid deposition is performed using the cortical-to-reference region standardized uptake value ratio (SUVR). Although the SUVR method is more objective and simple, there are several limitations associated with the SUVR method including partial volume correction, image reconstruction and processing, region-of-interest (ROI) delineation method, definition of the standard of truth (SoT) and select of appropriate reference region [4].

The F-labelled ligand is an amyloid PET ligand widely used for diagnosing AD. Among them, ¹⁸F-florbetaben (FBB) [5] and ¹⁸F-flutemetamol (FMM) [6] are widely used in Europe and Asia. FBB is an ¹⁸F-labelled polyethylene glycol stilbene derivative with high in vitro affinity and specificity for Aβ plaques [7]. FMM is the ¹⁸F-labelled analogue of ¹¹C-PiB and shows strong concordance with histopathology for brain fibrillar Aβ. FMM has been useful for differentiating cognitively normal people and AD patients with high specificity and sensitivity in detection of AD [8]. Although scanning protocols are relatively similar across the tracers, FDA-approved visual rating guidelines to determine a scan positive or negative differ considerably. These differences include color scale used, intensity scaling, definitions of a region, as well as spatial and signal thresholds to determine positivity. When amyloid PET is taken using different ligands in the same person, reading results are sometimes different.

In the present study, the discrepancy in detecting amyloid positivity between FBB and FMM PET was investigated using visual assessment and SUVR cut-off categorisation. In addition, the discrepancy rate between visual assessment and SUVR cut-off categorisation in FBB and FMM was examined. Ideally, histopathological confirmation of Aβ presence in the brain should be a SoT. However, this analysis is rarely achievable because it should be performed post-mortem. Therefore, based on visual assessment or SUVR cut-off categorisation as SoT, the false-positive and false-negative rates were compared between the two ligands.

Methods

Mri Data Acquisition

Standardised three-dimensional (3D) T1turbo field echo images were acquired from all participants at SMC using the same scanner (Achieva 3.0-Tesla MRI 164 scanner, Philips, Best, the Netherlands) and the following parameters: sagittal slice thickness, 1.0 mm with 50% overlap; no gap; repetition time, 9.9 msec; echo time, 4.6 msec; flip angle, 8°, and matrix size, 240 × 240 pixels reconstructed to 480 × 480 over a field of view of 240 mm.

Aβ Pet Data Acquisition

Participants underwent FBB PET and FMM PET at SMC using a Discovery STe PET/ computed tomography (CT) scanner (GE Medical Systems, Milwaukee, WI, USA) in 3D scanning mode that examined 47 slices of 3.3-mm thickness spanning the entire brain [11]. Paired FBB and FMM PET images were acquired on two separate days; mean interval times (4.0 ± 3.4 months across all groups) among the groups were not different (P = 0.92). FBB PET was performed first in half of the participants (total 46; 7 AD, 10 MCI, 16 OCs, 9 YCs, and 4 SVaD) and FMM PET first in the other half (total 61; 22 AD, 17 MCI, 11 OCs and 11 YCs). CT images were acquired using a 16-slice helical CT system (140 KeV, 80 mA; 3.75-mm section width) for attenuation correction. A 20-min emission PET scan in dynamic mode (consisting of 4 × 5 min frames) was performed 90 min after injection of a mean dose of 311.5 MBq FBB or 185 MBq FMM. 3D PET images were reconstructed in a 128 × 128 × 48 matrix with 2 mm × 2 mm × 3.27 mm voxel size using the ordered-subsets expectation maximization (OSEM) algorithm (FBB iterations = 4 and subset = 20; FMM iterations = 4 and subsets = 20).

Aβ Pet Imaging Analysis

PET images were co-registered to individual MR images normalized to a T1-weighted MNI-152 template using SPM8 in Matlab 2014b (Mathworks, Natick, MA, USA). After standard space registration, the grey matter was divided into 116 regions using the Automated Anatomical Labeling atlas and white matter [12]. The whole cerebellum (WC) was used as an ROI to reference uptake ratio (which is identical to SUVR) and quantify FBB and FMM retention. Global cerebral cortex amyloid retention ratio was assessed from the volume-weighted average SUVR of 28 bilateral cerebral cortical volumes of interest (VOI) [11, 13]. The cerebral cortical VOI chosen for this study consisted of the following areas: bilateral frontal (superior and middle frontal gyri; medial part of the superior frontal gyrus; opercular part of the inferior frontal gyrus; triangular part of the inferior frontal gyrus; supplementary motor area; orbital part of the superior, middle, and inferior orbital frontal gyri; rectus; and olfactory cortex), posterior cingulate gyri, parietal (superior and inferior parietal, supramarginal and angular gyri, and precuneus), lateral temporal (superior, middle, and inferior temporal gyri and Heschl’s gyri), and occipital (superior, middle, and inferior occipital gyri; cuneus; calcarine fissure; and lingual and fusiform gyri).

Aβ Pet Positivity Based On Visual Assessment

Three experienced doctors (two nuclear medicine doctors and one neurologist) visually quantified FBB and FMM. For FBB, tracer uptake was assessed according to the regional cortical tracer uptake (RCTU) system in four brain regions (frontal cortex, posterior cingulate cortex/precuneus, parietal cortex, and lateral temporal cortex). The global uptake in the brain was assessed according to the brain amyloid plaque load (BAPL) system [14]. For FMM, each doctor scored the frontal, temporoparietal/insula, posterior cingulate/precuneus, lateral temporal, and striatum as positive or negative and recorded the overall amyloid status. A scan was categorized as positive if there was uptake in any region. A scan was categorized as negative if there was no uptake in all five regions [15].

Inter-rater agreement was excellent for FBB (Fleiss k = 0.86) and FMM (Fleiss k = 0.78). After individual ratings were performed, the final visual positivity was determined based on the majority of agreement regarding visual reading results.

Aβ Pet Positivity Based On Suvr Assessment

SUVR positivity was classified based on SUVR cut-off value calculated using the iterative outlier approach in different samples consisting of cognitively normal participants over 55 years of age [16]. To calculate the SUVR cut-off value of Aβ positivity, 171 FMM PET and 202 FBB PET scans were evaluated. Consequently, when WC was used as a reference region, the cortical SUVR cut-off value was 1.1 for FBB and 1.03 for FMM.

For direct comparison of the FBB-FMM conversion method, SUVR values of the FBB-FMM cortical target volume of interest (CTX VOI) were directly converted into Centiloid (CL) units using the direct comparison of FBB-FMM CL (dcCL) method based on the CL conversion equation [17, 18]:

where SUVR_ind represents the individual SUVR values of all YC-0 and ADCI-100 participants, and SUVR_YC−0 and SUVR_ADCI−100 represent each group’s mean SUVR values. The CL equation was derived for FBB and FMM PET separately and applied to the FBB and FMM SUVR, respectively, from the FBB-FMM CTX VOI. The SUVR from the FBB-FMM CTX VOI used to determine dcCL was termed dcSUVR. When WC was used as a reference region, the dcCL cut-off value was 24.9 dcCL units for FBB and 15.1 dcCL units for FMM.

Results

False-positive And False-negative Rates For Fbb And Fmm

The visual assessment was set as SoT among the 101 participants except for six participants whose visual assessment result did not match between FBB and FMM. For FBB, there were two false-negative (SUVR-negative but visual assessment-positive) and three false-positive (SUVR positive but visual assessment-negative) participants. For FMM, there was one false-negative participant (SUVR-negative but visual assessment-positive) and four false-positive (SUVR-positive but visual assessment-negative) participants (Fig. 4). The false-positive rate was 5.6% (3/54) for FBB and 7.4% (4/54) for FMM. The false-negative rate was 4.3% (2/47) for FBB and 2.1% (1/47) for FMM. In this case, differences in false-positive rate (5.6% vs. 7.4%, P = 1.0) and false-negative rate (4.3% vs. 2.1%, P = 1.0) were not observed between FBB and FMM.

The SUVR cut-off categorisation was set as SoT among the 105 participants except for two participants whose SUVR result did not match between FBB and FMM. For FBB, there was one false-positive participant (SUVR-negative but visual assessment-positive) and five false-negative participants (SUVR-positive but visual assessment-negative). For FMM, five participants were false-positive (SUVR-negative but visual assessment-positive) and three participants were false-negative (SUVR-positive but visual assessment-negative; Fig. 4). The false-positive rate was 1.8% (1/55) for FBB and 9.1% (5/55) for FMM. The false-negative rate was 10% (5/50) for FBB and 6% (3/50) for FMM. In this case, differences between the two ligands in false-positive rate (1.8% vs. 9.1%, P = 0.13) and false-negative rate (10% vs. 6%, P = 0.5) were not observed.

Discussion

In terms of visual assessment and SUVR cut-off categorisation, the concordance rate of Aβ positivity between FBB and FMM was investigated in 107 participants who underwent both FBB and FMM PET for Aβ deposits. High agreement rates were found between FBB and FMM in visual assessment (94.4%) and SUVR cut-off categorisation (98.1%). In addition, both FBB and FMM showed high agreement rates between visual assessment and SUVR cut-off categorisation (93.5% in FBB and 91.6% in FMM). Furthermore, visual assessment or SUVR cut-off categorisation as SoT were the same for the two ligands; false-positive and false-negative rates were not different between the two ligands. Taken together, the findings indicate that both FBB and FMM had excellent agreement when used to quantitatively and qualitatively evaluate Aβ deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers can be useful in future research.

In the present study, inter-rater agreement was high for FBB (Fleiss k = 0.86) and FMM (Fleiss k = 0.78). The results are consistent with previous studies in which the inter-reader agreement was high in FBB (k = 0.89–0.94) [19] and FMM (Fleiss k = 0.63–0.83) [20].

High agreement rates were found between FBB and FMM in visual assessment (94.4%) and SUVR cut-off categorisation (98.1%). The concordance rates of Aβ positivity among ¹⁸F-ligands have not yet been extensively evaluated in direct comparison studies. However, the results of the present study were supported by our previous work showing the spatial distribution of increased FMM uptake was similar to FBB [18]. In addition, FMM and FBB were highly correlated (R² = 0.97) and showed similar dynamic ranges (slope = 0.99). Notably, the SUVR cut-off values (1.1 for FBB and 1.03 for FMM) using WC as a reference region were different from SUVR cut-offs proposed in previous studies (0.96 for FBB [4] and 1.23 for FMM [21]). The discrepancy might be explained by the differences in target cortical regions, tools used for analysis, and the method of developing SUVR cut-offs (iterative outlier method with OCs in this study compared with the receiver operating characteristic method in previous studies). When using the SUVR cut-off categorisation, only two cases of Aβ positivity mismatch were observed between the two ligands although SUVR cut-off categorisation values were determined for different participants.

In addition, both FBB and FMM showed high concordance rates between visual assessment and SUVR cut-off categorisation for Aβ deposits (93.5% in FBB and 91.6% in FMM). The findings were consistent with previous studies in which the accuracies of visual assessment and quantitative assessment in evaluating Aβ positivity were comparable [2, 3]. FBB (91–96%) [4] and FMM (95.3%) [22] showed high agreement between visual assessment and SUVR quantification. In a previous study, disagreement among cases was possibly explained by severe brain atrophy [22], however, severe atrophy was not observed in the present study. Most of the discrepancies between visual assessment and SUVR results were due to amyloid focal uptake in both FBB and FMM as shown in Fig. 2 case 4.

In the present study, based on visual assessment or SUVR cut-off categorisation as SoT, both FBB and FMM showed low false-positive and false-negative rates. When the two ligands were compared based on visual assessment results as SoT, although not statistically significant, false-positive rates (visual assessment-negative, SUVR-positive, 5.6% for FBB and 7.4% for FMM) were greater than the false-negative rates (visual assessment-positive, SUVR-negative, 4.3% for FBB and 2.1% for FMM). The SUVRs in all false-positive cases were near cut-off values. All false-positive FBB cases also overlapped with FMM, therefore, these false-positive cases are likely true-positive although the visual interpretation of FBB and FMM imaging showed high sensitivity and specificity in detecting Aβ plaques [23, 24]. Due to improved sensitivity and specificity, quantitative assessment is considered more accurate than visual assessment [25]. Therefore, false-positive cases should be followed up to determine whether they clinically progress or the follow-up Aβ PET becomes positive on visual assessment.

When the two ligands were compared based on SUVR cut-off categorisation as SoT, although not statistically significant, the false-positive rate (visual assessment-positive, SUVR-negative) was higher in FMM (9.1%) than in FBB (1.8%). Because the FBB and FMM showed minimal difference in SUVR positivity, these tracers are almost identical for assessment of the amyloid deposition using quantitative measures. However, in some cases, although not many, visual interpretations were different between the tracers, possibly because regional uptakes were considered in the visual interpretation. Conversely, an average value of volume-weighted SUVRs from bilateral cerebral cortical VOIs determined positivity in the quantitative assessment, which might not reflect significant regional Aβ plaque burden. In our experience, assessing uptakes in the lateral temporal cortex was often more problematic than in other regions in visual analysis, especially near the border with the occipital lobe. The area was large and the uptake often looked different depending on image tilting. In cases of ambiguous or indeterminate scans, the reading can be altered if some regions are over- or under-evaluated.

Why some FMM images were over-estimated is not evident. However, unlike FBB, FMM was evaluated on a color scale, which might have affected the reading. Apparently, visual analysis on a color scale tends to over-estimate the reading. In Fig. 2 case 1, which was only FMM-positive based on visual assessment, there was focal uptake toward the lateral temporal uptake, while there is a visually distinguishable difference of signal activity between grey and white matter in the same area of FBB. In the present study, signal differences were better discriminated using FBB than FMM. In addition, FMM appears slightly blurrier than FBB, which might be another reason for the discrepancy. Alternatively, when pathology and visual reading in FMM were compared in previous studies, more false-positives were identified on the visual reading, apparently due to the presence of diffuse plaques [23]. In addition, FMM binds to both neuritic and diffuse Aβ plaques [23, 26]. Both plaques generally co-exist in the neocortex of AD patients and FMM PET signal corresponds predominantly to neuritic plaques but is affected by the presence of diffuse plaques [27]. The additional FMM PET signal from diffuse Aβ plaques can result in positive PET reads, which might contribute to the false positivity of FMM in addition to the aforementioned factor.

Limitation

The present study had several limitations. First, pathologic verification was lacking and pathologic Aβ burdens should be used in further studies to validate the results. Second, the generalization of the results might be difficult due to differences between PET scanners, acquisition protocols, and reconstruction methods at other sites. However, the findings provide valuable information on distinct features of FBB and FMM scans, which can be used for better evaluation of Aβ imaging.

Conclusions

In the present study, visual assessment and quantitative measurement of amyloid deposition were investigated based on direct comparison of FBB and FMM PET scans. Both FBB and FMM had excellent quantitative and qualitative agreement when evaluating Aβ deposits, thus, combining amyloid PET data associated with the use of different ligands from multi-centers can be useful in future research.

Abbreviations

Declarations

Ethics approval and consent to participate

The Institutional Review Boards approved this study at all participating centers. We obtained written, informed consent from patients and caregivers.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This research was supported by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (MSIT) (No. NRF-2019R1A5A2027340), Research of Korea Centers for Disease Control and Prevention (No. 2018-ER6203-01), grant of the Korean Health Technology R&D Project, National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST), Ministry of Health & Welfare, Republic of Korea (HI19C1132) and Chonnam National University Hospital Biomedical Research Institute (BCRI20012).

Author contributions

S.H.C., Y.S.C., S.H.M, and S.W.S. contributed to the conceptualisation of the study, analysis, and interpretation of data, and drafting. Y.J.K and S.J.K provided technical support. B.L., B.C.K., H.M.J, J.P.K., Y.H.J. and G.F. contributed to the interpretation of data. D.L.N. and H.J.K. contributed to data collection and interpretation 

Acknowledgements

Not applicable

References

1. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14:535–62.
2. Ng S, Villemagne VL, Berlangieri S, Lee ST, Cherk M, Gong SJ, et al. Visual assessment versus quantitative assessment of 11C-PIB PET and 18F-FDG PET for detection of Alzheimer's disease. J Nucl Med. 2007;48:547–52.
3. Morris E, Chalkidou A, Hammers A, Peacock J, Summers J, Keevil S. Diagnostic accuracy of (18)F amyloid PET tracers for the diagnosis of Alzheimer's disease: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging. 2016;43:374–85.
4. Bullich S, Seibyl J, Catafau AM, Jovalekic A, Koglin N, Barthel H, et al. Optimized classification of (18)F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. Neuroimage Clin. 2017;15:325–32.
5. Sabri O, Sabbagh MN, Seibyl J, Barthel H, Akatsu H, Ouchi Y, et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: phase 3 study. Alzheimers Dement. 2015;11:964–74.
6. Vandenberghe R, Van Laere K, Ivanoiu A, Salmon E, Bastin C, Triau E, et al. 18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial. Ann Neurol. 2010;68:319–29.
7. Sabri O, Seibyl J, Rowe C, Barthel H. Beta-amyloid imaging with florbetaben. Clinical translational imaging. 2015;3:13–26.
8. Rinne JO, Wong DF, Wolk DA, Leinonen V, Arnold SE, Buckley C, et al. [(18)F]Flutemetamol PET imaging and cortical biopsy histopathology for fibrillar amyloid β detection in living subjects with normal pressure hydrocephalus: pooled analysis of four studies. Acta Neuropathol. 2012;124:833–45.
9. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7:263–9.
10. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–8.
11. Jang H, Jang YK, Kim HJ, Werring DJ, Lee JS, Choe YS, et al. Clinical significance of amyloid beta positivity in patients with probable cerebral amyloid angiopathy markers. Eur J Nucl Med Mol Imaging. 2019;46:1287–98.
12. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273–89.
13. Park JH, Seo SW, Kim C, Kim SH, Kim GH, Kim ST, et al. Effects of cerebrovascular disease and amyloid beta burden on cognition in subjects with subcortical vascular cognitive impairment. Neurobiol Aging. 2014;35:254–60.
14. Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10:424–35.
15. Farrar G, Molinuevo JL, Zanette M. Is there a difference in regional read [(18)F]flutemetamol amyloid patterns between end-of-life subjects and those with amnestic mild cognitive impairment? Eur J Nucl Med Mol Imaging. 2019;46:1299–308.
16. Mormino EC, Brandel MG, Madison CM, Rabinovici GD, Marks S, Baker SL, et al. Not quite PIB-positive, not quite PIB-negative: slight PIB elevations in elderly normal control subjects are biologically relevant. Neuroimage. 2012;59:1152–60.
17. Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD, Sr., Jagust WJ, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015;11:1–15.e1-4.
18. Cho SH, Choe YS, Kim HJ, Jang H, Kim Y, Kim SE, et al. A new Centiloid method for 18F-florbetaben and 18F-flutemetamol PET without conversion to PiB. European Journal of Nuclear Medicine and Molecular Imaging. 2019.
19. Sabri O, Seibyl J, Rowe C, Barthel H. Beta-amyloid imaging with florbetaben. Clin Transl Imaging. 2015;3:13–26.
20. Mountz JM, Laymon CM, Cohen AD, Zhang Z, Price JC, Boudhar S, et al. Comparison of qualitative and quantitative imaging characteristics of [11C]PiB and [18F]flutemetamol in normal control and Alzheimer's subjects. Neuroimage Clin. 2015;9:592–8.
21. Thurfjell L, Lilja J, Lundqvist R, Buckley C, Smith A, Vandenberghe R, et al. Automated Quantification of 18F-Flutemetamol PET Activity for Categorizing Scans as Negative or Positive for Brain Amyloid: Concordance with Visual Image Reads. J Nucl Med. 2014;55:1623–8.
22. Farrar G, Molinuevo JL, Zanette M. Is there a difference in regional read [(18)F]flutemetamol amyloid patterns between end-of-life subjects and those with amnestic mild cognitive impairment? Eur J Nucl Med Mol Imaging. 2019;46:1299–308.
23. Salloway S, Gamez JE, Singh U, Sadowsky CH, Villena T, Sabbagh MN, et al. Performance of [(18)F]flutemetamol amyloid imaging against the neuritic plaque component of CERAD and the current (2012) NIA-AA recommendations for the neuropathologic diagnosis of Alzheimer's disease. Alzheimers Dement (Amst). 2017;9:25–34.
24. Seibyl J, Catafau AM, Barthel H, Ishii K, Rowe CC, Leverenz JB, et al. Impact of Training Method on the Robustness of the Visual Assessment of 18F-Florbetaben PET Scans: Results from a Phase-3 Study. J Nucl Med. 2016;57:900–6.
25. Camus V, Payoux P, Barre L, Desgranges B, Voisin T, Tauber C, et al. Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur J Nucl Med Mol Imaging. 2012;39:621–31.
26. Gearing M, Levey AI, Mirra SS. Diffuse plaques in the striatum in Alzheimer disease (AD): relationship to the striatal mosaic and selected neuropeptide markers. J Neuropathol Exp Neurol. 1997;56:1363–70.
27. Ikonomovic MD, Fantoni ER, Farrar G, Salloway S. Infrequent false positive [(18)F]flutemetamol PET signal is resolved by combined histological assessment of neuritic and diffuse plaques. Alzheimers Res Ther. 2018;10:60.