Visual Read Research Articles

Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer’s disease pathology in the eye clinic

BackgroundAlzheimer’s disease (AD) is a common, complex and multifactorial disease that may require screening across multiple routes of referral to enable early detection and subsequent future implementation of tailored interventions. Blood- and eye-based biomarkers show promise as low-cost, scalable and patient-friendly tools for early AD detection given their ability to provide information on AD pathophysiological changes and manifestations in the retina, respectively. Eye clinics provide an intriguing real-world proof-of-concept setting to evaluate the performance of these potential AD screening tools given the intricate connections between the eye and brain, presumed enrichment for AD pathology in the aging population with eye disorders, and the potential for an accelerated diagnostic pathway for under-recognized patient groups.MethodsThe BeyeOMARKER study is a prospective, observational, longitudinal cohort study aiming to include individuals visiting an eye-clinic. Inclusion criteria entail being ≥ 50 years old and having no prior dementia diagnosis. Excluded eye-conditions include traumatic insults, superficial inflammation, and conditions in surrounding structures of the eye that are not engaged in vision. The BeyeOMARKER cohort (n = 700) will undergo blood collection to assess plasma p-tau217 levels and a brief cognitive screening at the eye clinic. All participants will subsequently be invited for annual longitudinal follow-up including remotely administered cognitive screening and questionnaires. The BeyeOMARKER + cohort (n = 150), consisting of 100 plasma p-tau217 positive participants and 50 matched negative controls selected from the BeyeOMARKER cohort, will additionally undergo Aβ-PET and tau-PET, MRI, retinal imaging including hyperspectral imaging (primary), widefield imaging, optical coherence tomography (OCT) and OCT-Angiography (secondary), and cognitive and cortical vision assessments.ResultsWe aim to implement the current protocol between April 2024 until March 2027. Primary outcomes include the performance of plasma p-tau217 and hyperspectral retinal imaging to detect AD pathology (using Aβ- and tau-PET visual read as reference standard) and to detect cognitive decline. Initial follow-up is ~ 2 years but may be extended with additional funding.ConclusionsWe envision that the BeyeOMARKER study will demonstrate the feasibility of early AD detection based on blood- and eye-based biomarkers in alternative screening settings, and will improve our understanding of the eye-brain connection.Trial registrationThe BeyeOMARKER study (Eudamed CIV ID: CIV-NL-23–09-044086; registration date: 19th of March 2024) is approved by the ethical review board of the Amsterdam UMC.

Laboratory evaluation of the Chembio DPP Syphilis Screen & Confirm point-of-care test on serum and simulated blood samples

Introduction: Syphilis infections are increasing in Canada, and traditional serological diagnostics pose barriers for vulnerable and marginalized populations at greatest risk. Point-of-care tests (POCTs) offer solutions, yet none were available in Canada until recently. The Chembio Dual Path Platform (DPP) Syphilis Screen & Confirm (SSC) is one of the first POCTs that helps distinguish active infection from non-infectious syphilis. This study evaluates the reliability of the Chembio DPP SSC to diagnose syphilis infection. Methods: One hundred clinical samples with known syphilis serology (chemiluminescent microparticle immunoassay [CMIA] and rapid plasma regain [RPR]) results were used to evaluate the Chembio DPP SSC. CMIA–ve (n = 20), CMIA+ve RPR–ve (n = 40), and CMIA+ve RPR+ve (n = 40) samples represented negative, past, and presumed active infection, respectively. Samples were used in two formats: serum and simulated blood. Two laboratory technicians read the test visually, and with the manufacturer's DPP Micro Reader, in blinded fashion. Results: Overall sensitivity of the Chembio DPP SSC to distinguish presumed active infection from non-infectious syphilis (past infection and no infection) with visual reads were 52.50% (serum) and 55.00% (simulated blood). Sensitivity increased using the DPP Micro Reader to 90.00% (serum) and 97.50% (simulated blood). Specificity with visual reads were 98.33% (serum) and 95.00% (simulated blood) compared to Micro Reader results of 88.30% (serum) and 80.00% (simulated blood). For the non-treponemal portion of the POCT with visual reads, the sensitivity increased with increasing RPR titers. Low RPR titers <1:4 only had a sensitivity of 42.86% (serum) and 14.29% (simulated blood). Discussion: The laboratory evaluation of the Chembio DPP SSC shows promise in detecting active syphilis, particularly in samples with RPR titers >1:4. However, the need for the Micro Reader for more accurate results is a limitation of the POCT, and financial constraints may pose barriers to some users. Further field evaluation is warranted.

Plasma p-tau217 concordance with amyloid PET among ethnically diverse older adults.

Commercially available plasma p-tau217 biomarker tests are not well studied in ethnically diverse samples. We evaluated associations between ALZPath plasma p-tau217 and amyloid-beta positron emission tomography (Aβ-PET) in Hispanic/Latino (88% of Cuban or South American ancestry) and non-Hispanic/Latino older adults. One- and two-cutoff ranges were derived and evaluated to assess agreement with Aβ-PET. A total of 239 participants underwent blood draw and Aβ-PET (age 70.8±7.8, 55.2% female, education 15.6±3.4 years, 48.9% Hispanic/Latino, 94.9% white). Plasma p-tau217 showed excellent discrimination of Aβ-PET positive and negative participants (visual read: AUC=0.91 [0.87-0.95], p<0.001; Centiloids quantification: AUC=0.90 [0.86-0.94]). There was a greater percent agreement between low p-tau217 and negative Aβ-PET (95.8%) than high p-tau217 and positive Aβ-PET (86.3%). Analyses within ethnicity-specific subgroups suggested similar p-tau217 performance. Plasma p-tau217 (ALZPath) relates to brain Aβ in Hispanic/Latino and non-Hispanic/Latino older adults. Independent validation and replication are necessary to establish reference ranges and inform appropriate contexts of use across ethno-racially diverse populations. Plasma p-tau217 (ALZPath) and Aβ-PET were measured in Hispanic/Latino and non-Hispanic/Latino older adults.Plasma p-tau217 accurately discriminated Aβ-PET positive and negative participants.Applying a two-cutoff "intermediate" plasma p-tau217 approach could reduce need for more invasive and costly testing.Plasma p-tau217 associations with Aβ-PET were strong within both Hispanic/Latino and non-Hispanic/Latino groups.

Head-to-Head Comparison of Tau and Amyloid Positron Emission Tomography Visual Reads for Differential Diagnosis of Neurodegenerative Disorders: An International, Multicenter Study.

We compared the accuracy of amyloid and [18F]Flortaucipir (FTP) tau positron emission tomography (PET) visual reads for distinguishing patients with mild cognitive impairment (MCI) or dementia with fluid biomarker support of Alzheimer's disease (AD). Participants with FTP-PET, amyloid-PET, and diagnosis of dementia-AD (n = 102), MCI-AD (n = 41), non-AD diseases (n = 76), and controls (n = 20) were included. AD status was determined independent of PET by cerebrospinal fluid or plasma biomarkers. The mean age was 66.9 years, and 44.8% were women. Three readers interpreted scans blindly and independently. Amyloid-PET was classified as positive/negative using tracer-specific criteria. FTP-PET was classified as positive with medial temporal lobe (MTL) binding as the minimum uptake indicating AD tau (tau-MTL+), positive with posterolateral temporal or extratemporal cortical binding in an AD-like pattern (tau-CTX+), or negative. The majority of scan interpretations were used to calculate diagnostic accuracy of visual reads in detecting MCI/dementia with fluid biomarker support for AD (MCI/dementia-AD). Sensitivity of amyloid-PET for MCI/dementia-AD was 95.8% (95% confidence interval 91.1-98.4%), which was comparable to tau-CTX+ 92.3% (86.7-96.1%, p = 0.67) and tau-MTL+ 97.2% (93.0-99.2%, p = 0.27). Specificity of amyloid-PET for biomarker-negative healthy and disease controls was 84.4% (75.5-91.0%), which was like tau-CTX+ 88.5% (80.4-94.1%, p = 0.34), and trended toward being higher than tau-MTL+ 75.0% (65.1-83.3%, p = 0.08). Tau-CTX+ had higher specificity than tau-MTL+ (p = 0.0002), but sensitivity was lower (p = 0.02), driven by decreased sensitivity for MCI-AD (80.5% [65.1-91.2] vs. 95.1% [83.5-99.4], p = 0.03). Amyloid- and tau-PET visual reads have similar sensitivity/specificity for detecting AD in cognitively impaired patients. Visual tau-PET interpretations requiring cortical binding outside MTL increase specificity, but lower sensitivity for MCI-AD. ANN NEUROL 2024;96:476-487.

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment

An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Tau PET, Aβ PET, and MRI. Positive results on tau PET (temporal meta-region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Computerized decision support is an effective approach to select memory clinic patients for amyloid-PET.

The use of amyloid-PET in dementia workup is upcoming. At the same time, amyloid-PET is costly and limitedly available. While the appropriate use criteria (AUC) aim for optimal use of amyloid-PET, their limited sensitivity hinders the translation to clinical practice. Therefore, there is a need for tools that guide selection of patients for whom amyloid-PET has the most clinical utility. We aimed to develop a computerized decision support approach to select patients for amyloid-PET. We included 286 subjects (135 controls, 108 Alzheimer's disease dementia, 33 frontotemporal lobe dementia, and 10 vascular dementia) from the Amsterdam Dementia Cohort, with available neuropsychology, APOE, MRI and [18F]florbetaben amyloid-PET. In our computerized decision support approach, using supervised machine learning based on the DSI classifier, we first classified the subjects using only neuropsychology, APOE, and quantified MRI. Then, for subjects with uncertain classification (probability of correct class (PCC) < 0.75) we enriched classification by adding (hypothetical) amyloid positive (AD-like) and negative (normal) PET visual read results and assessed whether the diagnosis became more certain in at least one scenario (PPC≥0.75). If this was the case, the actual visual read result was used in the final classification. We compared the proportion of PET scans and patients diagnosed with sufficient certainty in the computerized approach with three scenarios: 1) without amyloid-PET, 2) amyloid-PET according to the AUC, and 3) amyloid-PET for all patients. The computerized approach advised PET in n = 60(21%) patients, leading to a diagnosis with sufficient certainty in n = 188(66%) patients. This approach was more efficient than the other three scenarios: 1) without amyloid-PET, diagnostic classification was obtained in n = 155(54%), 2) applying the AUC resulted in amyloid-PET in n = 113(40%) and diagnostic classification in n = 156(55%), and 3) performing amyloid-PET in all resulted in diagnostic classification in n = 154(54%). Our computerized data-driven approach selected 21% of memory clinic patients for amyloid-PET, without compromising diagnostic performance. Our work contributes to a cost-effective implementation and could support clinicians in making a balanced decision in ordering additional amyloid PET during the dementia workup.

Prognostic role of quantitative [18F]FDG PET/CT parameters in adrenocortical carcinoma

PurposeWe aimed to evaluate the prognostic potential of baseline [18F]FDG PET/CT for overall survival (OS) in patients with adrenocortical carcinoma (ACC).MethodsWe performed a retrospective analysis of 67 treatment-naïve ACC patients with available [18F]FDG PET/CT at time of initial diagnosis. Pretherapeutic PETs of primary tumors were manually segmented and quantitative parameters (maximum/mean/peak standardized uptake value (SUVmax/mean/peak), metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG, defined as TV*SUVmean) were derived. Based on a visual read, absence (M0) or presence of metastatic disease (M1) were evaluated. Kaplan–Meier and Cox regression analyses were used to determine the prognostic value of the above mentioned markers on overall survival adjusted for established prognostic markers.Results24/67 patients (36%) presented with M0 based on PET/CT, while the remaining 43/67 (64%) had M1-status. 32/67 patients died during follow-up and median OS was 48 months. In 12% of patients FDG-PET detected additional metastatic lesion not clearly visible by CT only. In univariable analysis, all quantitatively derived PET parameters failed to reach significance (P ≥ 0.1), and only PET/CT-based M1-status and Ki-67 were associated with increased mortality (M1: HR 13.89, 95% CI 4.15–86.32, P < 0.001; Ki-67 HR 1.29, 95% CI 1.16–1.42; P < 0.0001). Using multivariable Cox regression analyses, M1-status (HR 9.69, 95% CI 2.82–60.99) and Ki-67 index (HR 1.29, 95% CI 1.13–1.04; P < 0.05) remained significant associated with OS.ConclusionIn treatment-naïve ACC patients, the quantitative PET parameter failed to predict OS, but presence of metastases detected by [18F]FDG PET/CT and Ki-67 index were independently associated with shorter OS. Therefore, a simple visual PET-based read-out is of prognostic value at initial diagnosis, while time-consuming PET-based quantification can be omitted.

Eligibility in the INTERCEPT‐AD trial: visual amyloid classification for equivocal SUVrs in early Alzheimer’s disease

AbstractBackgroundPET tracers enable the in‐vivo measurement of amyloid in the brain and have been incorporated in eligibility criteria for clinical trials. Automated quantitative image analysis enables the calculation of a standardised uptake value ratio (SUVr) which can enable a rapid amyloid classification without requiring a visual read. However, quantitative analysis may have an equivocal range where amyloid classification does require a visual read. The range of SUVrs that is equivocal is not well understood.MethodParticipants in the INTERCEPT‐AD trial of ACU193 (Siemers et al., J Prev Alzheimers Dis. 2023;10(1):19‐24) underwent an amyloid [18F] Florbetapir PET scan and T1‐weighted MRI scans. MRIs were segmented using the LEAP algorithm (Wolz et al., NeuroImage. 2010;49(2):1316‐1325), registered to the PET image, and the global cortical average (GCA) SUVr based on brain regions typically with high amyloid load in Alzheimer’s disease (Klunk et al., Alzheimers Dement. 2015;11(1):1‐15) was calculated, using the whole cerebellum as the reference region. SUVrs greater than 1.2 were classified as amyloid positive, SUVrs less than 1.0 were classified as amyloid negative, and SUVrs in the equivocal range were assessed by visual read by trained central radiologists.ResultsOf 194 participants who underwent screening, 188 had an SUVr available from automated analysis. Of these, 64 were within the equivocal range (SUVr 1.0 – 1.2) and had a visual read performed. For PET scans in the equivocal range, 53 (83%) were classified as amyloid negative and 11 (17%) were classified as amyloid positive by visual read. Within the equivocal range, there was no difference in SUVrs between the images classified by visual read as amyloid negative or positive.ConclusionSUVrs may be used as an objective quantitative amyloid classification method at values &gt; 1.2 for trial inclusion; however, in the equivocal range, the GCA SUVr did not correspond with amyloid classification based on a visual read. Further study is required to investigate the equivocal range and the use of combined quantitative measures and visual reads.

Tau‐PET to predict conversion from mild cognitive impairment to dementia; a head‐to‐head comparison against amyloid‐PET and MRI

AbstractBackgroundIn contrast to amyloid‐PET and MRI, tau‐PET imaging has not been integrated into clinical practice even though it shows a close relationship with clinical symptoms. With the present study, we aim to assess the performance of tau‐PET to predict conversion to dementia (all‐cause and AD) in individuals with mild cognitive impairment (MCI), and compare it with amyloid‐PET and MRI.MethodWe selected participants from an ongoing multicenter study (n = 408, Table 1). The following ROIs were used to define quantified thresholds for positivity using an independent cognitively unimpaired reference group: a temporal meta‐region for tau‐PET and an AD‐susceptible temporoparietal region for MRI‐based cortical thickness. For amyloid‐PET, we used the standard Centiloid mask and a published threshold of 27 Centiloids to determine positivity. We additionally performed visual reads for all imaging modalities. We used Cox proportional hazard, logistic regression, and receiver operating curve (ROC) analyses to assess the performance of amyloid‐PET (A), tau‐PET (T), and MRI (N) to predict progression from MCI to dementia over a mean follow‐up period of 2 years.ResultHazard ratios indicated that tau‐PET and amyloid‐PET positivity (both when assessed quantitatively and using visual reads) conveyed a greater risk of progression to dementia than MRI, and that a combination of A+ and T+ (or N+) conveyed a greater risk than A‐T‐N‐ or isolated A+ or T+ (Figure 1). ROC analyses revealed that only tau‐PET performed better than demographics alone when predicting all‐cause dementia while both amyloid‐ and tau‐PET performed better than demographics and MRI when predicting AD dementia. LASSO logistic regression revealed that the optimal model to predict conversion from MCI to all‐cause dementia included a quantitative MRI marker and tau‐PET visual read. For optimal prediction of AD dementia specifically, the quantitative measure of amyloid‐PET was added in addition to the quantitative MRI marker and the tau‐PET visual read (Table 2).ConclusionAmong individuals with MCI, tau‐PET performs best when predicting dementia, and can be optimized when combined with MRI. For AD dementia, tau‐PET and amyloid‐PET are the best‐performing modalities, but an optimal prediction is again achieved when combined with MRI.

Longitudinal tau binding in tau‐PET negative patients with clinical Alzheimer’s disease

AbstractBackgroundDifferent tau‐PET patterns have been observed in Alzheimer’s disease (AD) which may be associated with demographic factors, copathology and resilience. We and others previously found that not all Aβ‐positive AD patients are tau‐PET positive. It is currently not known whether tau pathology develops in these groups over time. Therefore, we aimed to examine longitudinal tau binding in tau‐PET negative individuals with symptomatic AD.MethodWe included 97 Aβ‐positive participants with clinical diagnosis of mild cognitive impairment or dementia due to AD (MCI/AD) of which 12 (13%) were tau‐PET negative. To compare, we included 131 tau‐PET negative cognitively unimpaired individuals (CU) of which 93 (71%) were Aβ‐negative and 38 (29%) Aβ‐positive. Tau‐status was defined by [18F]flortaucipir PET consensus visual read of two nuclear physicians according to US Food and Drug Administration‐approved guidelines. [18F]flortaucipir SUVr at baseline (N = 225) and after ±2 years (N = 81) was quantified in an early tau region reflecting Braak I (entorhinal cortex), the medial temporal lobe (MTL), and a combined visual read region of interest (ROI) (parietal, occipital, and posterior lateral temporal lobe). We used age‐and sex‐adjusted linear mixed models to compare tau accumulation.ResultDemographics are shown in table 1. Compared to MCI/AD A+T+, MCI/AD A+T‐ were older (p&lt;0.001) and more often male (p = 0.015). MCI/AD A+T‐ did not differ from CU A‐T‐ in baseline tau‐PET SUVr, but had a slightly lower SUVr in Braak I compared to CU A+T‐ (p = 0.040) (figure 1). After ±2 years, all MCI/AD A+T‐ participants who underwent follow up remained visual read tau negative. However, MCI/AD A+T‐ showed a steeper increase in Braak I and MTL tau‐PET SUVr compared to CU A‐T‐ (figure 2A,B) and in Braak I compared to CU A+T‐ (β = 0.31, p = 0.036). Interestingly, MCI/AD A+T‐ showed a significantly steeper tau‐PET SUVr increase compared to MCI/AD A+T+ in Braak I (β = 0.29, p = 0.029) (figure 2A).ConclusionAlthough visual read tau‐PET status remains stable over time, our results indicate that tau‐negative MCI/AD patients do accumulate tau over time, most pronounced in early tau regions. This highlights the relevance of quantifying tau regionally over time in tau‐PET negative individuals with clinical AD diagnosis.

Developing Topics.

Both 11C-Pittsburgh compound-B (PiB) and 18F-Flutemetamol (FMT) have off-target white matter (WM) signal that increases with age, but WM uptake is stronger with FMT than PiB. Although their WM patterns are comparably associated with age, the difference between them may impact visual and quantitative assessments of cortical amyloid-β uptake. Our objective was to determine whether PiB PET and FMT PET visual assessments agree with quantitative cortical amyloid-β evaluations. A cohort of cognitively unimpaired (CU) younger adults with median (range) of 39 (30, 48) years (N = 30), CU older adults with age of 67 (61, 83) years (N = 30) and older adults with AD dementia with age of 67 (54-84) years (N = 23) underwent MRI, PiB PET and FMT PET. PiB-PET and FMT PET scans were evaluated visually by two nuclear medicine specialists blinded to participant information. Disagreements were reconciled with a second, joint round of visual reads. Readers applied standard visual assessment criteria using the absence of regional gray matter (GM) and WM contrast as an indication of a positive scan. The global cortical PiB standard uptake value ratio (SUVr) was obtained referencing to cerebellar crus uptake and converted to centiloid values. Participants were classified as PiB positive/negative using a centiloid cut-off of 22. PiB and FMT visual positivity agreed with quantitative positivity on PiB-based centiloid values in 69/83 for PiB and 78/83 for FMT. There were 10 disagreements with PiB, 1 disagreement with FMT and 4 disagreements with both. Few disagreements occurred near the centiloid threshold. Interestingly, all disagreements between visual reads (amyloid-β positive) and centiloid (amyloid-β negative) in the CU younger were with PiB. FMT disagreements were always in the CU older and AD groups. Disagreements between amyloid-β positivity on visual reads and quantitative measurements (centiloid) may depend on age and tracer. PiB WM uptake is lower than FMT WM uptake, which may lead to reduced contrast between GM and WM (part of the standard criteria for visual reads), especially in younger ages. In contrast, WM uptake increases at older ages, and increased WM signal may bleed more into cortical regions, hiding subtle, near-threshold cortical uptake, especially with FMT.

Tau‐PET to predict conversion from mild cognitive impairment to dementia; a head‐to‐head comparison against amyloid‐PET and MRI

AbstractBackgroundIn contrast to amyloid‐PET and MRI, tau‐PET imaging has not been integrated into clinical practice even though it shows a close relationship with clinical symptoms. With the present study, we aim to assess the performance of tau‐PET to predict conversion to dementia (all‐cause and AD) in individuals with mild cognitive impairment (MCI), and compare it with amyloid‐PET and MRI.MethodWe selected participants from an ongoing multicenter study (n = 408, Table 1). The following ROIs were used to define quantified thresholds for positivity using an independent cognitively unimpaired reference group: a temporal meta‐region for tau‐PET and an AD‐susceptible temporoparietal region for MRI‐based cortical thickness. For amyloid‐PET, we used the standard Centiloid mask and a published threshold of 27 Centiloids to determine positivity. We additionally performed visual reads for all imaging modalities. We used Cox proportional hazard, logistic regression, and receiver operating curve (ROC) analyses to assess the performance of amyloid‐PET (A), tau‐PET (T), and MRI (N) to predict progression from MCI to dementia over a mean follow‐up period of 2 years.ResultHazard ratios indicated that tau‐PET and amyloid‐PET positivity (both when assessed quantitatively and using visual reads) conveyed a greater risk of progression to dementia than MRI, and that a combination of A+ and T+ (or N+) conveyed a greater risk than A‐T‐N‐ or isolated A+ or T+ (Figure 1). ROC analyses revealed that only tau‐PET performed better than demographics alone when predicting all‐cause dementia while both amyloid‐ and tau‐PET performed better than demographics and MRI when predicting AD dementia. LASSO logistic regression revealed that the optimal model to predict conversion from MCI to all‐cause dementia included a quantitative MRI marker and tau‐PET visual read. For optimal prediction of AD dementia specifically, the quantitative measure of amyloid‐PET was added in addition to the quantitative MRI marker and the tau‐PET visual read (Table 2).ConclusionAmong individuals with MCI, tau‐PET performs best when predicting dementia, and can be optimized when combined with MRI. For AD dementia, tau‐PET and amyloid‐PET are the best‐performing modalities, but an optimal prediction is again achieved when combined with MRI.

Concordance between visual interpretation and quantitative analysis of [18F]flortaucipir pet images

AbstractBackgroundPositron emission tomography (PET) imaging with [18F]flortaucipir allows for in‐vivo visualization of aggregated tau in Alzheimer’s disease (AD). The FDA‐approved label for [18F]flortaucipir PET provides a standardized, clinically applicable definition of tau‐PET positivity by visual interpretation. Here, we studied the concordance between this visual interpretation and quantitative approaches employed in research.MethodWe included 2692 participants (cognitively unimpaired [CU] and impaired [CI, MCI or AD dementia]) from four cohorts (Mayo Clinic Study of Aging and ADRC, ADNI, A4, and A05) with available [18F]flortaucipir PET (mean age: 70.2 y, 50.0% females). Three trained readers assessed each [18F]flortaucipir PET scan according to the FDA‐approved method. Visual reads were compared to an established approach for defining tau‐PET positivity based on SUVR values in a temporal meta‐ROI (Jack, et al. Alzheimers Dement. 2017). Previously defined “lenient” (SUVR = 1.22) and “conservative” (SUVR = 1.30) cut‐points were explored. In addition, visual reads were compared to tau‐PET positivity as defined by a scheme based on the expected spatial progression of tau pathology from the medial temporal lobe (MTL) to the temporal neocortex (NEO) (Ossenkoppele, et al. Nat Med. 2022; cut‐points: SUVRMTL = 1.30, SUVRNEO = 1.37).ResultConcordance between visual and quantitative approaches was limited (Figure 1): the “lenient” and MTL‐NEO methods yielded high rates of quantitative‐positive, visual‐negative scans (73‐52% and 26‐18% for CU and CI, respectively), while the “conservative” cut‐point failed to detect 45% and 13% of the CU and CI visual‐positive scans, respectively. The prevalence of tau‐PET positivity in Aß‐negative individuals was highest with the “lenient” cut‐point (∼24%) and lowest with visual interpretation (∼3%) (Figure 2). Visual‐positive, quantitative‐negative individuals were more frequently Aß‐positive than visual‐negative, quantitative‐positive individuals (“lenient”: 86% vs 50%, p&lt;0.001; “conservative”: 87% vs 58%, p&lt;0.001; MTL‐NEO: 85% vs 70%, p = 0.005). Among visually‐positive participants, 164 (28%) showed a tau deposition pattern that deviated from the expected MTL to NEO progression (Figure 3).ConclusionVisual and quantitative methods for [18F]flortaucipir PET are non‐exchangeable. Tau‐PET positivity based on visual interpretation aligns better with Aß‐pathology. A significant number of visually‐positive participants showed a pattern of tau accumulation that deviated from the expected stereotypical spatial progression and was not detected by standard quantitative methods.

Discordance in amyloid positivity defined by Visual Reads VR and Centiloids CL

AbstractBackgroundAmyloid PET VR is the most common method to determine Aβ pathology in clinical practice. Amyloid positivity by VR or CL cut‐off generally shows a good concordance, however, it is possible to have discordant cases whereby the VR is positive, but CL is below the cut‐off of amyloid positivity, or vice versa. The objective of this analysis was to assess the rate and cause of discordance in defining amyloid pathology using VR and CL in Eisai’s Elenbecestat MissionAD Phase 3 program.MethodFlorbetaben, Florbetapir, and Flutemetamol amyloid PET scans from 3412 participants with MCI due to AD or mild AD dementia were visually read at screening by trained neuroradiologists and quantitated using CLs. VR classification of the amyloid status is based on the higher or equal grey matter (GM) uptake relative to uptake in the white matter (WM) in at least one area, as per manufacturer’s guidance. Quantitatively, amyloid negativity was defined as CL&lt;30. The number of positive cortical regions (SUVR&gt;1.17) was also calculated.ResultThe positivity concordance between VR and CL was 92%, while the negativity concordance was 94.6%. VR+/CL‐ discordance was 8% (n = 1814 VR+, n = 145 CL‐) whereas VR‐/CL+ discordance was 5.4% (n = 1598 VR‐, n = 86 CL+). VR+/CL‐ discordant cases had significantly fewer positive cortical regions than both VR+/CL+ and VR‐/CL+ cases (Figure 1). VR‐/CL+ had a significantly higher WM uptake than both VR‐/CL‐ and VR+/CL‐ cases (Figure 2).ConclusionVR+/CL‐ discordant cases show fewer amyloid positive cortical regions, computing CLs results in below cut‐off values due to a dilution effect. These cases are considered VR+ as per manufacturer’s guidelines as they show at least one area of amyloid accumulation. On the other hand, VR‐/CL+ discordant cases result from an increased WM uptake, reducing GM/WM contrast required to determine visual read positivity. VR represents a robust and validated method to determine the presence of amyloid deposition and enables enrolling patients with amyloid beta pathology, as visible on amyloid PET scans, while CLs are optimal for assessing longitudinal changes over time and are a more sensitive measure to assess disease progression.

Quantification supports visual assessment of challenging amyloid‐PET images

AbstractBackgroundIn clinical practice, amyloid‐PET images are visually assessed to determine the presence or absence of amyloid pathology. The expected arrival of disease‐modifying treatments warrants the need for objective quantification of amyloid burden to further support treatment decisions. Here, we investigated whether quantification increases reader confidence and influences visual read (VR) classification in patients with challenging or ambiguous amyloid information sampled across the clinical continuum.MethodsFrom the AMYPAD‐DPMS trial (N = 840), we selected a subsample of 85 amyloid‐PET scans ([18F]flutemetamol, N = 48/[18F]florbetaben, N = 53), for which 1) visual assessment was discordant with amyloid status based on Centiloid (CL) (cut‐off = 21) and/or 2) were assessed with a low confidence (i.e. ≤3 on a 5‐point scale) by the local reader. In addition, concordant negative and positive (N = 8/8) scans across tracers were included to balance the dataset (N = 101, Table‐1). PET images were processed using GEHCs AMYPYPE PET‐only pipeline, providing global CL and regional z‐scores for 7 cortical ROIs. VR classification and corresponding confidence was assessed for 3 independent certified assessors before and after disclosure of the quantification results. In addition, readers stated whether CL quantification and/or the regional z‐scores supported their assessment. Reader agreement was assessed using Fleiss Kappa. Changes in reader confidence after disclosure was assessed using paired t‐test.ResultsOverall, readers reported global CL and regional z‐score quantification to be VR‐supportive in 76.2% and 49.7% of assessments, respectively. After disclosure of quantitative results, concordance between readers (Kbaseline= 0.60; Kpost‐disclosure= 0.79), concordance between majority‐VR and CL‐status (agreementbaseline = 75.2%; agreementpost‐disclosure = 81.2%), and reader confidence (Mbaseline= 4.0 vs. Mpost‐disclosure= 4.37, t = ‐5.8, p&lt;0.001) significantly improved. Classification changed for 26 (8.6%) assessments, with 8 (30.8%) changing to VR‐ and 18 (69.2%) to VR+. In these cases, CL was considered supportive in 16 (61.5%) cases, regional z‐scores were considered supportive for 8/10 remaining cases, and most often for the posterior cingulate/precuneus region. The 2 remaining cases had a low confidence at both time‐points. Example cases are shown in Figure 1.ConclusionIn a dataset enriched with challenging amyloid‐PET scans for visual assessment, quantification resulted in a significantly increased agreement and confidence across readers. We further aim to determine in which cases quantification was considered most supportive.

A prospective study evaluating the clinical utility of tau‐PET in the diagnostic work‐up of patients with cognitive symptoms

AbstractBackgroundDetermining the added value of tau‐PET in the diagnostic work‐up of patients with cognitive symptoms is of great importance before implementation of the method in clinical practice. We therefore aimed to prospectively study the added clinical value of including tau‐PET in the clinical work‐up.Method878 patients referred for cognitive complaints to secondary Memory clinics in southern Sweden were consecutively recruited to the Swedish BioFINDER‐2 cohort study (May 2017 – Sept 2021). Participants underwent a baseline diagnostic work‐up including clinical examination, medical history, cognitive testing, blood and CSF sampling, and MRI of the brain. A tau‐PET ([18F]RO948) scan was added onto this clinical diagnostic work‐up. The tau‐PET scans were visually read as: normal, early AD pattern, late AD pattern or inconclusive scan. The primary end points were change in diagnosis and change in Alzheimer disease drug therapy or other drug treatment between the pre‐ and post‐PET visits. Secondary end point was the change in diagnostic certainty between the pre‐ and post‐PET visits.ResultThe tau‐PET visual read result led to a change in diagnoses in 66 participants (7.5%, p&lt;0.001) and a change in medication in 48 participants (5.5%, p&lt;0.001) (Figure 1). There was an overall increase in the diagnostic certainty after tau‐PET in the whole dataset [6.9±2.3 vs 7.4±2.4; p&lt;0.0001]. The certainty was higher in participants with a pre‐PET diagnosis of AD [7.6±1.7 vs 8.2±2.0; p&lt;0.0001] (Figure 2) and increased, as expected, even further in participants with a tau‐PET positive result supporting an AD diagnosis [8.0±1.4 vs 9.0±0.9; p&lt;0.0001]. The impact of tau‐PET results was strongest in participants with pathological Ab‐status, whereas no significant change in diagnoses was seen in participants with normal Ab‐status.ConclusionWe report a significant change in diagnoses and medication of patients when tau‐PET is added to an already extensive diagnostic work‐up that included CSF AD biomarkers. Tau‐PET led to a significant increase in certainty of underlying etiology. The additive effect on certainty of etiology and to diagnosis was predominantly seen in Ab‐positives and we suggest that clinical use of tau‐PET is limited to populations with biomarkers indicating Ab‐positivity.

The role of cerebrovascular pathology in the association between amyloid‐β and tau in cognitively unimpaired and impaired individuals

AbstractBackgroundAlthough cerebrovascular pathology often co‐exists with Alzheimer’s disease pathology, it remains incompletely understood to what extent it can contribute to the progression of Alzheimer’s disease pathology. We investigated whether cerebrovascular pathology (white matter lesions [WML] and microbleeds) modified the association between amyloid‐ß (Aß) and both cross‐sectional and longitudinal tau in cognitively unimpaired (CU) and cognitively impaired (CI) individuals.MethodWe included 1240 participants from the Swedish BioFINDER‐2 cohort including 402 Aß‐negative CU, 147 Aß‐positive CU, 257 Aß‐negative CI (MCI and non‐AD dementia) and 434 Aß‐positive CI participants (MCI and AD dementia) (Table 1). All underwent baseline [18F]RO948 (tau)‐PET, and a subset (n = 678) underwent longitudinal tau‐PET (2.6±1.0‐years). We extracted tau‐PET SUVr in a temporal meta‐region‐of‐interest. For Aß, we used global [18F]flutemetamol‐PET SUVr for CU and Aß‐status (+/‐) for CI participants. Global WML volume was calculated using the SPM lesion segmentation tool, corrected for intra‐cranial volume and log‐transformed. Presence of microbleeds was dichotomized into absent or present ( = 1) by visual read. Primary analyses included age‐, sex‐ and APOE e4‐corrected linear mixed models between tau‐PET (outcome) and interactions between time, Aß and WML volume/microbleeds (predictors).ResultUpon correcting for age, sex and e4‐carriership, Aß‐positive CU and CI participants showed significantly more microbleeds compared to Aß‐negative participants of the same cognitive stage (both p&lt;0.05), but no differences in WML volume was observed (both p&gt;0.05). In CU, we observed a significant interaction effect between = 1 microbleed and higher Aß‐load with both greater cross‐sectional tau (ß = 0.67 [CI: 0.45‐0.89], p&lt;0.001) and longitudinal tau (ß = 0.11 [CI: 0.07‐0.16], p&lt;0.001) (Fig‐1, Table‐2). In CU, we also observed a significant interaction effect between larger WML volumes and higher Aß‐load with greater cross‐sectional tau (ß = 0.14 [CI: 0.06‐0.22], p = 0.001). When including both microbleeds and WML volume in the same model, only effects of microbleeds remained significant (Table‐2). No significant effects were observed in CI participants.ConclusionIn CU (but not CI) individuals, cerebrovascular pathology may modify the association between Aß and tau by accelerating tau accumulation in the presence of Aß. These results might suggest an early effect of cerebrovascular pathology on the interplay between Aß and tau.

Estimating the time between amyloid and tau‐pet positivity: implications for Alzheimer’s disease prevention trials

AbstractBackgroundAlzheimer’s disease (AD) is characterized by a preclinical stage in which abnormal accumulation of amyloid‐ß (Aß) leads to phosphorylated tau aggregation. This sequence of events represents the transition between “AD pathologic change” (A+T‐) to the much riskier full AD neuropathology (A+T+, Jack et al. Alzheimers Dement. 2018). Thus, to understand the clinical relevance of the A+T‐ stage in the long term, it is important to understand the temporal course of Aß and tau. In this study, we used longitudinal Aß‐ and tau‐positron emission tomography (PET) to estimate the expected time gap between Aß‐ and tau‐PET positivity, and explored factors that influenced this gap.MethodWe estimated the natural history of amyloid accumulation in AD using longitudinal Aß‐PET data from 1690 AD‐continuum subjects from the ADNI. Specifically, we fitted a non‐linear mixed effects model (3‐knot spline) and modelled the subject‐specific “age of Aß positivity” as random effects (Figure 1). An interval‐censored time‐to‐event analysis using “time to amyloid positivity” as time variable was performed on 318 Aß+ ADNI participants who had available tau‐PET ([18F]flortaucipir) visual reads. Cox proportional hazards regression was used to assess whether age of onset of amyloid positivity, sex, APOE status, and cognitive impairment at follow‐up influenced the onset of tau‐PET positivity relative to Aß‐positivity.ResultAfter ∼11 years from Aß positivity, 50% of the CU individuals progressed to tau‐PET positivity, while only ∼17% remained tau‐PET negative after 20 years (Figure 2). Among the studied factors, only the presence of cognitive impairment accelerated the onset of visual tau‐PET positivity (HR = 2.52, p&lt;0.001, median tau‐free survival difference ∼6 years) (Figure 3). Both female sex (HR = 1.44, p = 0.16, median tau‐free survival difference∼2.5 years) and presence of the APOE‐e4 allele (HR = 1.44, p = 0.15, median tau‐free survival difference∼2.5 years) were trend‐level associated to faster onset of tau‐PET positivity.ConclusionThe time gap between Aß‐ and tau‐PET positivity typically spans a decade and is highly variable across individuals. This long gap implies that a significant fraction of A+T‐ CU older individuals will never develop tau‐PET positivity and subsequent symptoms in their lifespan, which suggests that these subjects may not benefit from current disease‐modifying therapies for AD.

Quantitative Amyloid‐PET in Real‐World Practice: Lessons from the Imaging Dementia—Evidence for Amyloid Scanning (IDEAS) study

AbstractBackgroundAmyloid‐PET quantification was developed in research settings using highly selected samples, harmonized acquisition protocols, and MRI‐based preprocessing. We aimed to analyze scans from IDEAS, a large‐scale, real‐world study of amyloid‐PET. Major challenges lie in scans being acquired on various PET/PET‐CT/PET‐MR scanners without standardization of acquisition or reconstruction protocols, and without MRI to preprocess data.Method18,295 Medicare beneficiaries with MCI or dementia underwent PET with [18F]florbetapir, [18F]florbetaben, or [18F]flutemetamol at 343 facilities. Sites shared 10,700 raw scans with their visual interpretation (positive or negative) by local radiologists/nuclear medicine physicians. Scans were processed using a PET‐only pipeline, which derives Centiloid based on a cortex‐to‐cerebellum tissue ratio in template space. Processed scans underwent standard visual quality control by 2 raters (Fig 1a‐b), and flagged scans were reviewed in a consensus meeting to discuss troubleshooting options. Injected dose and time between tracer injection and PET acquisition start were obtained from DICOM file header.Result9,963 scans (93%) were successfully pre‐processed with default parameters (Fig 1a) and 403 additional scans (4%) were included after manual intervention (Fig 1c). 339 scans (3%) were excluded because of technical artifacts or major bilateral lesions (Fig 1c). The final sample (n = 10,361) is described in Table 1, with example scans in Fig 1d. Centiloids showed the expected bimodal distribution (Fig 2a) and differentiated visually positive from negative scans (median = ‐2 versus 74 CL; area under the curve = 0.913, Fig 2b). PET acquisition generally followed tracer‐specific FDA recommendations for injected dose and acquisition time, but major variability was observed (Fig 2c). Centiloid values were independent from injected dose but were higher in patients scanned at later time windows for [18F]flutemetamol, resulting from known slow tracer kinetics (Fig 2c).ConclusionMRI‐free, amyloid‐PET quantification in a real‐world setting was feasible, with results conforming to expected distribution of Centiloid values and concordant with visual reads. However, greater standardization of PET acquisition than is currently required by FDA labels will be needed to reliably quantify amyloid PET in clinical practice (e.g., to monitor response to amyloid lowering therapy). The IDEAS dataset will be made available via the Global Alzheimer’s Association Interactive Network in 2023.