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Several studies have demonstrated strong agreement between routine clinical visual assessment and quantification, suggesting that quantification approaches could support assessment by less experienced readers or in challenging cases. However, all studies to date have implemented a retrospective case collection, and challenging cases were generally underrepresented. Methods: We included all participants (n = 741) from the AMYPAD diagnostic and patient management study with available baseline amyloid PET quantification. Quantification was done with the PET-only AmyPype pipeline, providing global Centiloid and regional z scores. Visual assessment was performed by local readers for the entire cohort. From the total cohort, we selected a subsample of 85 cases for which the amyloid status based on the local reader's visual assessment and the Centiloid classification (cutoff = 21) was discordant or that were assessed with low confidence (i.e., ≤3 on a 5-point scale) by the local reader. In addition, concordant negative (n = 8) and positive (n = 8) scans across tracers were selected. In this sample (n = 101 cases; [18F]flutemetamol, n = 48; [18F]florbetaben, n = 53), the visual assessments and corresponding confidence by 5 certified independent central readers were captured before and after disclosure of the quantification results. Results: For the whole AMYPAD diagnostic and patient management study cohort, overall assessment by local readers highly agreed with Centiloid status (κ = 0.85, 92.3% agreement). This was consistently observed within disease stages (subjective cognitive decline-plus, κ = 0.82, 92.3% agreement; mild cognitive impairment, κ = 0.80, 89.8% agreement; dementia, κ = 0.87, 94.6% agreement). Across all central reader assessments in the challenging subsample, quantification of global Centiloid and regional z scores was considered supportive of visual reads in 70.3% and 49.3% of assessments, respectively. After disclosure of the quantitative results, we observed improvement in concordance across the 5 readers (baseline κ = 0.65, 65.3% agreement; κ after disclosure = 0.74, 73.3% agreement) and a significant increase in reader confidence (baseline mean (M) = 4.0 vs. M after disclosure = 4.34, Wilcoxon statistic (W) = 101,056, P Conclusion: In this clinical study enriched for challenging amyloid PET cases, we demonstrate the value of quantification to support visual assessment. After disclosure, both interreader agreement and confidence showed significant improvement. These results are important considering the arrival of antiamyloid therapies, which used the Centiloid metric for trial inclusion and target engagement. Moreover, quantification could support determination of amyloid-β status with high certainty, an important factor for treatment initiation.

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IntroductionElevated tau (T+) is temporally proximal to dementia onset but less is known about factors influencing T+ onset age and time to dementia after T+ in Alzheimer's disease (AD). We used sampled iterative local approximation (SILA) estimated T+ onset age (ETOA) to investigate factors associated with T+ age and time from T+ to dementia onset in the Alzheimer's Disease Neuroimaging Initiative.MethodsUsing SILA-estimated amyloid positivity and T+ onset ages derived from 18F-Flortaucipir, 18F-Florbetapir, and 18F-Florbetaben positron emission tomography and Cox proportional hazards and accelerated failure time models, we analyzed apolipoprotein E (APOE), sex, amyloid burden, age, educational attainment, and literacy associations with ETOA and time from T+ to dementia.ResultsHigher amyloid, APOE-ε4, lower education, and lower literacy associated with younger ETOA. Older ETOA and higher amyloid associated with shorter time from T+ to dementia.DiscussionThis work highlights the prognostic value of ETOA and the need to better characterize factors contributing to ETOA and dementia onset in AD.HighlightsWe applied sampled iterative local approximation (SILA) to Alzheimer's Disease Neuroimaging Initiative 18F-Flortaucipir data, to estimate individuals' age of tau pathology onset (T+) and time from T+ onset to dementia. Higher amyloid, apolipoprotein E ε4, lower education, and lower literacy associated with younger estimated T+ onset age. Older T+ onset age and higher amyloid associated with shorter time from T+ to dementia. Only one individual was observed to remain dementia free 14 years after T+ onset. This work highlights the prognostic value of T+ onset age and the need to better characterize factors contributing to T+ onset age and dementia onset in Alzheimer's disease.

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The increasing recognition of cardiac amyloidosis (CA) as a cause of heart failure, coupled with advancements in therapeutic options, has underscored the need for early detection. Positron emission tomography (PET) imaging emerged as a promising non-invasive tool for diagnosing and managing CA. This review provides a comprehensive analysis of current PET imaging techniques, focusing on radiotracers, including [11C]Pittsburgh Compound B, [18F]Flutemetamol, [18F]Florbetapir, [18F]Florbetaben, [18F]-sodium fluoride, and [124I]Evuzamitide. PET imaging's ability to differentiating CA subtypes and quantify amyloid burden contributes defining prognosis and aids in monitoring treatment response. However, standardizing imaging protocols and establishing definitive diagnostic thresholds remain challenging. As PET imaging continues to evolve, it promises to improve patient outcomes by facilitating earlier diagnosis, more accurate subtype differentiation, and better treatment monitoring in CA.

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Background: Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. Methods: This retrospective study included 332 amyloid PET scans (165 [18F]Florbetaben; 167 [18F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson's correlation coefficient, the intraclass correlation coefficient (ICC), a Passing-Bablok regression, and Bland-Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. Results: BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860-0.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026-1.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999-1.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0-50 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves > 0.996 for all). Conclusions: Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes.

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BackgroundStandard methods for deriving Centiloid scales from amyloid PET images are time-consuming and require considerable expert knowledge. We aimed to develop a deep learning method of automating Centiloid scale calculations from amyloid PET images with 11C-Pittsburgh Compound-B (PiB) tracer and assess its applicability to 18F-labeled tracers without retraining.MethodsWe trained models on 231 11C-PiB amyloid PET images using a 50-layer 3D ResNet architecture. The models predicted the Centiloid scale, and accuracy was assessed using mean absolute error (MAE), linear regression analysis, and Bland-Altman plots.ResultsThe MAEs for Alzheimer's disease (AD) and young controls (YC) were 8.54 and 2.61, respectively, using 11C-PiB, and 8.66 and 3.56, respectively, using 18F-NAV4694. The MAEs for AD and YC were higher with 18F-florbetaben (39.8 and 7.13, respectively) and 18F-florbetapir (40.5 and 12.4, respectively), and the error rate was moderate for 18F-flutemetamol (21.3 and 4.03, respectively). Linear regression yielded a slope of 1.00, intercept of 1.26, and R2 of 0.956, with a mean bias of -1.31 in the Centiloid scale prediction.ConclusionsWe propose a deep learning means of directly predicting the Centiloid scale from amyloid PET images in a native space. Transferring the model trained on 11C-PiB directly to 18F-NAV4694 without retraining was feasible.

Abstract licence: CC BY