Use of Standardized Uptake Value Ratios Decreases Interreader Variability of [18F] Florbetapir PET Brain Scan Interpretation

Considerations in the clinical use of amyloid PET and CSF biomarkers for Alzheimer's disease

Amyloid-β (Aβ) positron emission tomography (PET) imaging and cerebrospinal fluid (CSF) biomarkers are now established tools in the diagnostic workup of patients with Alzheimer's disease (AD), and their use is anticipated to increase with the introduction of new disease-modifying therapies. Although these biomarkers are comparable alternatives in research settings to determine Aβ status, biomarker testing in clinical practice requires careful consideration of the strengths and limitations of each modality, as well as the specific clinical context, to identify which test is best suited for each patient. This article provides a comprehensive review of the pathologic processes reflected by Aβ-PET and CSF biomarkers, their performance, and their current and future applications and contexts of use. The primary aim is to assist clinicians in making better-informed decisions about the suitability of each biomarker in different clinical situations, thereby reducing the risk of misdiagnosis or incorrect interpretation of biomarker results. HIGHLIGHTS: Recent advances have positioned Aβ PET and CSF biomarkers as pivotal in AD diagnosis. It is crucial to understand the differences in the clinical use of these biomarkers. A team of experts reviewed the state of Aβ PET and CSF markers in clinical settings. Differential features in the clinical application of these biomarkers were reviewed. We discussed the role of Aβ PET and CSF in the context of novel plasma biomarkers.

Interrater agreement and variability in visual reading of [18F] flutemetamol PET images

OBJECTIVE

The purpose of this study was to validate the concordance of visual ratings of [18F] flutemetamol amyloid positron emission tomography (PET) images and to investigate the correlation between the agreement of each rater and the Centiloid (CL) scale.

METHODS

A total of 192 participants, clinically classified as cognitively normal (CN) (n = 59), mild cognitive impairment (MCI) (n = 65), Alzheimer's disease (AD) (n = 55), or non-AD dementia (n = 13), participated in this study. Three experts conducted visual ratings of the amyloid PET images for all 192 patients, assigning a confidence level to each rating on a three-point scale (certain, probable, or neither). The positive or negative determination of amyloid PET results was made by majority vote. The CL value was calculated using the CapAIBL pipeline.

RESULTS

Overall, 101 images were determined to be positive, and 91 images were negative. Of the 101 positive images, the three raters were in complete agreement for 92 images and in disagreement for 9 images. Of the 91 negative images, the three raters were in complete agreement for 75 images and in disagreement for 16 images. Interrater reliability among the three experts was particularly high, with both Fleiss' kappa and Conger's kappa measuring 0.83 (0.76-0.89). The CL values of the unanimous positive group were significantly greater than those of the other groups, whereas the CL values of the unanimous negative group were significantly lower than those of the other groups. Images with rater disagreement had intermediate CLs. In cases with a high confidence level, the positive or negative visual ratings were in almost complete agreement. However, as confidence levels decreased, experts' visual ratings became more variable. The lower the confidence level was, the greater the number of cases with disagreement in the visual ratings.

CONCLUSION

Three experts independently rated 192 amyloid PET images, achieving a high level of interrater agreement. However, in patients with intermediate amyloid accumulation, visual ratings varied. Therefore, determining positive and negative decisions in these patients should be performed with caution.

Impact of shortening time on diagnosis of 18F-florbetaben PET

BACKGROUND

18F-Florbetaben amyloid positron emission tomography (PET) scan is crucial for diagnosing Alzheimer's disease, typically involving a 20 min acquisition. However, maintaining such prolonged scans can be challenging in some cases. This study explores the diagnostic impact and feasibility of reducing scan durations by comparing quantitative measures between shortened and standard scans. Additionally, we identified the optimal Centiloid threshold to distinguish between positive and negative amyloid results.

RESULTS

We analyzed 307 PET scans from our memory clinic, each followed up for a minimum of two years. The scans, conducted 90 to 110 min after approximately 300 MBq of 18F-Florbetaben injection, were categorized into four sets of 5 min durations: 5, 10, 15, and 20 min. Nuclear medicine physicians validated and rated each scan as either amyloid-positive or negative. For quantitative assessments, we employed the standardized uptake value ratio (SUVR) and Centiloid scales, comparing total SUVR and Centiloid values across five subregions (global, frontal, posterior cingulate-precuneus, lateral temporal, and parietal) using Bland-Altman analysis. Receiver operator characteristic (ROC) curves were utilized to develop optimal Centiloid thresholds. Comparing the images at 5, 10, 15, and 20 min images, SUVR and Centiloid values gradually increased with prolonged scan times. The mean SUVR difference between 5 and 20 min was 0.03 for the amyloid-positive and 0.01 for the amyloid-negative groups; Centiloid differences were 4.60 and 2.38, respectively. Additionally, no significant variation was observed in total SUVR and Centiloid values among the durations across all subregions in positive and negative groups (all p > 0.1). ROC analysis indicated that a Centiloid threshold of 21.86 at 5 min provided optimal agreement with visual assessments (AUC = 0.985, sensitivity = 0.950, specificity = 0.972), especially using the global area.

CONCLUSIONS

This study demonstrated that 5 min image scans with an optimal threshold of CL = 21.86 exhibited minimal bias in SUVR and Centiloid values compared to longer scans (10, 15, and 20 min). Our findings suggest that shorter scan times are a viable and effective option for brain amyloid PET imaging in clinical settings.

Novel β-amyloid PET Imaging Study of [18F]92 in Patients with Cognitive Decline

[18F]-4-((E)-(((E)-4-(2-(2-(2-Fluoroethoxy)ethoxy)ethoxy)benzylidene)-hydrazono)methyl)-N-methylaniline ([18F]92) is a novel positron emission tomography (PET) tracer previously reported to exhibit high binding affinity to aggregated β-amyloid (Aβ). This study aims to report a fully automated radiosynthesis procedure for [18F]92, explore its radioactive distribution in the brains of healthy subjects, and investigate its potential application value in the early diagnosis of Alzheimer's disease (AD). The fully automated radiosynthesis of [18F]92 was performed on the AllinOne module. Thirty one participants were recruited for this study. Dynamic [18F]92 PET imaging was conducted over 0-90 min period to assess time-activity curves (TAC) and standardized uptake value ratio (SUVR) curves in cognitively normal (CN) subjects. All participants were visually classified as either positive (+) or negative (-). Semiquantitative analyses of [18F]92 were performed by calculating SUVRs in different regions of interest. Furthermore, the study analyzed the relationships between global SUVR and plasma AD biomarkers, including Aβ42, Aβ40, P-tau181, and T-tau. The automated radiosynthesis of [18F]92 was completed within 50 min, yielding a radiochemical purity of greater than 95% and a radiochemical yield of 36 ± 3% (nondecay-corrected). Among the participants, 15 were estimated as Aβ (-) and 16 as Aβ (+). TACs indicated that [18F]92 rapidly crossed the blood-brain barrier within 10 min, followed by a rapid decrease, which then slowed down in the last 50-90 min. SUVR curves revealed that SUVR values stabilized around 60-70 min after injection and reached an equilibrium between 70 and 90 min, primarily in the cerebral cortex. SUVRs of Aβ (+) participants were significantly higher than those of Aβ (-) individuals within the cerebral cortex. In addition, Aβ42 and the Aβ42/Aβ40 ratio exhibited negative correlations with global SUVR, while plasma P-tau181 and the P-tau181/T-tau ratio displayed positive correlations with global SUVR. [18F]92 exhibits excellent pharmacokinetic properties in the human brain and can be synthesized automatically on a large scale. [18F]92 is a promising and reliable radiotracer for estimating Aβ pathology accumulation, providing valuable assistance in AD diagnosis and guiding clinical trials of therapeutic drugs.

Improving diagnostic precision in amyloid brain PET imaging through data-driven motion correction

BACKGROUND

Head motion during brain positron emission tomography (PET)/computed tomography (CT) imaging degrades image quality, resulting in reduced reading accuracy. We evaluated the performance of a head motion correction algorithm using 18F-flutemetamol (FMM) brain PET/CT images.

METHODS

FMM brain PET/CT images were retrospectively included, and PET images were reconstructed using a motion correction algorithm: (1) motion estimation through 3D time-domain signal analysis, signal smoothing, and calculation of motion-free intervals using a Merging Adjacent Clustering method; (2) estimation of 3D motion transformations using the Summing Tree Structural algorithm; and (3) calculation of the final motion-corrected images using the 3D motion transformations during the iterative reconstruction process. All conventional and motion-corrected PET images were visually reviewed by two readers. Image quality was evaluated using a 3-point scale, and the presence of amyloid deposition was interpreted as negative, positive, or equivocal. For quantitative analysis, we calculated the uptake ratio (UR) of 5 specific brain regions, with the cerebellar cortex as a reference region. The results of the conventional and motion-corrected PET images were statistically compared.

RESULTS

In total, 108 sets of FMM brain PET images from 108 patients (34 men and 74 women; median age, 78 years) were included. After motion correction, image quality significantly improved (p < 0.001), and there were no images of poor quality. In the visual analysis of amyloid deposition, higher interobserver agreements were observed in motion-corrected PET images for all specific regions. In the quantitative analysis, the UR difference between the conventional and motion-corrected PET images was significantly higher in the group with head motion than in the group without head motion (p = 0.016).

CONCLUSIONS

The motion correction algorithm provided better image quality and higher interobserver agreement. Therefore, we suggest that this algorithm be adopted as a routine post-processing protocol in amyloid brain PET/CT imaging and applied to brain PET scans with other radiotracers.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method

Purpose

This study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images.

Methods

150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted.

Results

Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups.

Conclusion

BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

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.

Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods

OBJECTIVE

The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant.

METHODS

This study included 192 participants (mean age: 71.5 years, range: 50-87 years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively.

RESULTS

Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods.

CONCLUSIONS

The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.

PET molecular imaging for pathophysiological visualization in Alzheimer's disease

Alzheimer's disease (AD) is the most common dementia worldwide. The exact etiology of AD is unclear as yet, and no effective treatments are currently available, making AD a tremendous burden posed on the whole society. As AD is a multifaceted and heterogeneous disease, and most biomarkers are dynamic in the course of AD, a range of biomarkers should be established to evaluate the severity and prognosis. Positron emission tomography (PET) offers a great opportunity to visualize AD from diverse perspectives by using radiolabeled agents involved in various pathophysiological processes; PET imaging technique helps to explore the pathomechanisms of AD comprehensively and find out the most appropriate biomarker in each AD phase, leading to a better evaluation of the disease. In this review, we discuss the application of PET in the course of AD and summarized radiolabeled compounds with favorable imaging characteristics.

Clinical impact of amyloid PET using 18F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease: a multicenter study

OBJECTIVE

Amyloid positron emission tomography (PET) can reliably detect senile plaques and fluorinated ligands are approved for clinical use. However, the clinical impact of amyloid PET imaging is still under investigation. The aim of this study was to evaluate the diagnostic impact and clinical utility in patient management of amyloid PET using ¹⁸F-florbetapir in patients with cognitive impairment and suspected Alzheimer's disease (AD). We also aimed to determine the cutoffs for amyloid positivity for quantitative measures by investigating the agreement between quantitative and visual assessments.

METHODS

Ninety-nine patients suspected of having AD underwent ¹⁸F-florbetapir PET at five institutions. Site-specialized physicians provided a diagnosis of AD or non-AD with a percentage estimate of their confidence and their plan for patient management in terms of medication, prescription dosage, additional diagnostic tests, and care planning both before and after receiving the amyloid imaging results. A PET image for each patient was visually assessed and dichotomously rated as either amyloid-positive or amyloid-negative by four board-certified nuclear medicine physicians. The PET images were also quantitatively analyzed using the standardized uptake value ratio (SUVR) and Centiloid (CL) scale.

RESULTS

Visual interpretation obtained 48 positive and 51 negative PET scans. The amyloid PET results changed the AD and non-AD diagnosis in 39 of 99 patients (39.3%). The change rates of 26 of the 54 patients (48.1%) with a pre-scan AD diagnosis were significantly higher than those of 13 of the 45 patients with a pre-scan non-AD diagnosis (χ² = 5.334, p = 0.0209). Amyloid PET results also resulted in at least one change to the patient management plan in 42 patients (42%), mainly medication (20 patients, 20%) and care planning (25 patients, 25%). Receiver-operating characteristic analysis determined the best agreement of the quantitative assessments and visual interpretation of PET scans to have an area under the curve of 0.993 at an SUVR of 1.19 and CL of 25.9.

CONCLUSION

Amyloid PET using ¹⁸F-florbetapir PET had a substantial clinical impact on AD and non-AD diagnosis and on patient management by enhancing diagnostic confidence. In addition, the quantitative measures may improve the visual interpretation of amyloid positivity.

A comparison of advanced semi-quantitative amyloid PET analysis methods

PURPOSE

To date, there is no consensus on how to semi-quantitatively assess brain amyloid PET. Some approaches use late acquisition alone (e.g., ELBA, based on radiomic features), others integrate the early scan (e.g., TDr, which targets the area of maximum perfusion) and structural imaging (e.g., WMR, that compares kinetic behaviour of white and grey matter, or SI based on the kinetic characteristics of the grey matter alone). In this study SUVr, ELBA, TDr, WMR, and SI were compared. The latter - the most complete one - provided the reference measure for amyloid burden allowing to assess the efficacy and feasibility in clinical setting of the other approaches.

METHODS

We used data from 85 patients (aged 44-87) who underwent dual time-point PET/MRI acquisitions. The correlations with SI were computed and the methods compared with the visual assessment. Assuming SUVr, ELBA, TDr, and WMR to be independent measures, we linearly combined them to obtain more robust indices. Finally, we investigated possible associations between each quantifier and age in amyloid-negative patients.

RESULTS

Each quantifier exhibited excellent agreement with visual assessment and strong correlation with SI (average AUC = 0.99, ρ = 0.91). Exceptions to this were observed for subcortical regions with ELBA and WMR (ρ_ELBA = 0.44, ρ_WMR = 0.70). The linear combinations showed better performances than the individual methods. Significant associations were observed between TDr, WMR, SI, and age in amyloid-negative patients (p < 0.05).

CONCLUSION

Among the other methods, TDr came closest to the reference with less implementation complexity. Moreover, this study suggests that combining independent approaches gives better results than the individual procedure, so efforts should focus on multi-classifier systems for amyloid PET. Finally, the ability of techniques integrating blood perfusion to depict age-related variations in amyloid load in amyloid-negative subjects demonstrates the goodness of the estimate.

Decreased imaging time of amyloid PET using [18F]florbetapir can maintain quantitative accuracy

Shortening the amount of time required to acquire amyloid positron emission tomography (PET) brain images while maintaining the accuracy of quantitative evaluation would help to overcome motion artifacts associated with Alzheimer's disease patients. The present study aimed to validate the quantitative accuracy of [¹⁸F]florbetapir ([¹⁸F]FBP) imaging over a shorter acquisition duration. Forty participants were injected with [¹⁸F]FBP, and PET images were acquired for 50-55, 50-60, and 50-70 min after injection. Three physicians visually assessed the reprocessed [¹⁸F]FBP images using a binary scale to classify them as amyloid β (Aβ) negative or positive. A mean composite standard uptake value ratio (cSUVR) > 1.075 was defined as Aβ-positive based on receiver operating characteristic curves. Inter-reader and inter-acquisition duration agreements with visual assessment were evaluated using Cohen's kappa (κ). Binary visual discrimination of 102 for the 120 [¹⁸F]FBP images, was consistent among the three readers. Sixteen, sixteen, and fourteen of the 40 [¹⁸F]FBP images acquired for 50-55, 50-60, and 50-70 min after injection, respectively, were deemed Aβ-positive by visual assessment. The inter-rater agreement was high, and the inter-acquisition duration agreement was almost perfect. The cSUVR did not change significantly among the acquisition durations, and the acquisition duration did not affect the outcome of discrimination based on the cSUVR cutoff. A shorter acquisition duration changed the visual assessment outcomes. Stable quantitative values were derived from [¹⁸F]FBP images acquired within 5 min. cSUVR helped to improve the performance and confidence in the outcomes of visual assessment.

A 4-Year Follow-Up of Subjects with Visually Equivocal Amyloid Positron Emission Tomography Findings from the Alzheimer's Disease Neuroimaging Initiative Cohort

Background

To date, the clinical significance of visually equivocal amyloid positron emission tomography (PET) has not been well established.

Objective

We studied the clinical significance of equivocal amyloid PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Methods

Subjects with F-18 florbetapir PET scans at baseline who were followed up for 4 years were selected. Clinical characteristics, imaging biomarkers, cognitive function, and rate of conversion to AD were compared in subjects with visually equivocal findings.

Results

Of 249 subjects who completed the follow-up, 153 (61.4%), 20 (8.0%), and 129 (30.5%) were F-18 florbetapir-negative, -equivocal, and -positive, respectively. The mean standardized uptake value ratios (SUVR) of F-18 florbetapir PET were 0.75 ± 0.04, 0.85 ± 0.10, and 1.00 ± 0.09 for each group (p <0.001 between groups), and 15.0%, 70.0%, and 98.7% of patients were quantitatively above the positive threshold. The change in the SUVR of F-18 florbetapir PET was higher in the equivocal (6.09 ± 3.61%, p <0.001) and positive (3.13 ± 4.38%, p <0.001) groups than the negative group (0.88 ± 4.28%). Among the subjects with normal or subjective memory impairment and mild cognitive impairment, 5.3% with negative amyloid PET and 37.5% with positive amyloid PET converted to AD over the 4-year period. None of the equivocal amyloid PET subjects converted to AD during this period.

Conclusion

Approximately 8% of subjects from the ADNI cohort showed visually equivocal amyloid PET scans with intermediate load and rapid accumulation of amyloid, but did not convert to AD during the 4-year follow-up.

Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load

OBJECTIVE

To evaluate a novel β-amyloid (Aβ)-PET-based quantitative measure (Aβ accumulation index [Aβ index]), including the assessment of its ability to discriminate between participants based on Aβ status using visual read, CSF Aβ₄₂/Aβ₄₀, and post-mortem neuritic plaque burden as standards of truth.

METHODS

One thousand one hundred twenty-one participants (with and without cognitive impairment) were scanned with Aβ-PET: Swedish BioFINDER, n = 392, [¹⁸F]flutemetamol; Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 692, [¹⁸F]florbetapir; and a phase 3 end-of-life study, n = 100, [¹⁸F]flutemetamol. The relationships between Aβ index and standardized uptake values ratios (SUVR) from Aβ-PET were assessed. The diagnostic performances of Aβ index and SUVR were compared with visual reads, CSF Aβ₄₂/Aβ₄₀, and Aβ histopathology used as reference standards.

RESULTS

Strong associations were observed between Aβ index and SUVR (R ²: BioFINDER 0.951, ADNI 0.943, end-of-life, 0.916). Both measures performed equally well in differentiating Aβ-positive from Aβ-negative participants, with areas under the curve (AUCs) of 0.979 to 0.991 to detect abnormal visual reads, AUCs of 0.961 to 0.966 to detect abnormal CSF Aβ₄₂/Aβ₄₀, and AUCs of 0.820 to 0.823 to detect abnormal Aβ histopathology. Both measures also showed a similar distribution across postmortem-based Aβ phases (based on anti-Aβ 4G8 antibodies). Compared to models using visual read alone, the addition of the Aβ index resulted in a significant increase in AUC and a decrease in Akaike information criterion to detect abnormal Aβ histopathology.

CONCLUSION

The proposed Aβ index showed a tight association to SUVR and carries an advantage over the latter in that it does not require the definition of regions of interest or the use of MRI. Aβ index may thus prove simpler to implement in clinical settings and may also facilitate the comparison of findings using different Aβ-PET tracers.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that the Aβ accumulation index accurately differentiates Aβ-positive from Aβ-negative participants compared to Aβ-PET visual reads, CSF Aβ₄₂/Aβ₄₀, and Aβ histopathology.

Validation of a spatial normalization method using a principal component derived adaptive template for [18F]florbetaben PET

Quantification may help in the context of amyloid-β positron emission tomography (PET). Quantification typically requires that PET images be spatially normalized, a process that can be subject to bias. We herein aimed to test whether a principal component approach (PCA) previously applied to [¹⁸F]flutemetamol PET extends to [¹⁸F]florbetaben. PCA was applied to [¹⁸F]florbetaben PET data for 132 subjects (70 Alzheimer dementia, 62 controls) and used to generate an adaptive synthetic template. Spatial normalization of [¹⁸F]florbetaben data using this approach was compared to that achieved using SPM12's magnetic resonance (MR) imaging driven algorithm. The two registration methods showed high agreement and minimal difference in standardized uptake value ratios (SUVR) (R² = 0.997 using cerebellum as reference region and 0.996 using the pons). Our method allows for robust and accurate registration of [¹⁸F]florbetaben images to template space, without the need for an MR image, and may prove of value in clinical and research settings.

Impact of Tracer Retention Levels on Visual Analysis of Cerebral [18F]- Florbetaben Pet Images

BACKGROUND

To compare visual and semi-quantitative analysis of brain [¹⁸F]Florbetaben PET images in Mild Cognitive Impairment (MCI) patients and relate this finding to the degree of ß-amyloid burden.

METHODS

A sample of 71 amnestic MCI patients (age 74 ± 7.3 years, Mini Mental State Examination 24.2 ± 5.3) underwent cerebral [¹⁸F]Florbetaben PET/CT. Images were visually scored as positive or negative independently by three certified readers blinded to clinical and neuropsychological assessment. Amyloid positivity was also assessed by semiquantitative approach by means of a previously published threshold (SUVr ≥ 1.3). Fleiss kappa coefficient was used to compare visual analysis (after consensus among readers) and semi-quantitative analysis. Statistical significance was taken at P<0.05.

RESULTS

After the consensus reading, 43/71 (60.6%) patients were considered positive. Cases that were interpreted as visually positive had higher SUVr than visually negative patients (1.48 ± 0.19 vs 1.11 ± 0.09) (P<0.05). Agreement between visual analysis and semi-quantitative analysis was excellent (k=0.86, P<0.05). Disagreement occurred in 7/71 patients (9.9%) (6 false positives and 1 false negative). Agreement between the two analyses was 90.0% (18/20) for SUVr < 1.1, 83% (24/29) for SUVr between 1.1 and 1.5, and 100% (22/22) for SUVr > 1.5 indicating lowest agreement for the group with intermediate amyloid burden.

CONCLUSION

Inter-rater agreement of visual analysis of amyloid PET images is high. Agreement between visual analysis and SUVr semi-quantitative analysis decreases in the range of 1.1<SUVr <=1.5, where the clinical scenario is more challenging.

Spill-in counts in the quantification of 18F-florbetapir on Aβ-negative subjects: the effect of including white matter in the reference region

Background

We aim to provide a systematic study of the impact of white matter (WM) spill-in on the calculation of standardized uptake value ratios (SUVRs) on Aβ-negative subjects, and we study the effect of including WM in the reference region as a compensation. In addition, different partial volume correction (PVC) methods are applied and evaluated.

Methods

We evaluated magnetic resonance imaging and ¹⁸F-AV-45 positron emission tomography data from 122 cognitively normal (CN) patients recruited at the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cortex SUVRs were obtained by using the cerebellar grey matter (CGM) (SUVR_CGM) and the whole cerebellum (SUVR_WC) as reference regions. The correlations between the different SUVRs and the WM uptake (WM-SUVR_CGM) were studied in patients, and in a well-controlled framework based on Monte Carlo (MC) simulation. Activity maps for the MC simulation were derived from ADNI patients by using a voxel-wise iterative process (BrainViset). Ten WM uptakes covering the spectrum of WM values obtained from patient data were simulated for different patients. Three different PVC methods were tested (a) the regional voxel-based (RBV), (b) the iterative Yang (iY), and (c) a simplified analytical correction derived from our MC simulation.

Results

WM-SUVR_CGM followed a normal distribution with an average of 1.79 and a standard deviation of 0.243 (13.6%). SUVR_CGM was linearly correlated to WM-SUVR_CGM (r = 0.82, linear fit slope = 0.28). SUVR_WC was linearly correlated to WM-SUVR_CGM (r = 0.64, linear fit slope = 0.13). Our MC results showed that these correlations are compatible with those produced by isolated spill-in effect (slopes of 0.23 and 0.11). The impact of the spill-in was mitigated by using PVC for SUVR_CGM (slopes of 0.06 and 0.07 for iY and RBV), while SUVR_WC showed a negative correlation with SUVR_CGM after PVC. The proposed analytical correction also reduced the observed correlations when applied to patient data (r = 0.27 for SUVR_CGM, r = 0.18 for SUVR_WC).

Conclusions

There is a high correlation between WM uptake and the measured SUVR due to spill-in effect, and that this effect is reduced when including WM in the reference region. We also evaluated the performance of PVC, and we proposed an analytical correction that can be applied to preprocessed data.

Voxel-based statistical analysis and quantification of amyloid PET in the Japanese Alzheimer's disease neuroimaging initiative (J-ADNI) multi-center study

Background

Amyloid PET plays a vital role in detecting the accumulation of in vivo amyloid-β (Aβ). The quantification of Aβ accumulation has been widely performed using the region of interest (ROI)-based mean cortical standardized uptake value ratio (mcSUVR). However, voxel-based statistical analysis has not been well studied. The purpose of this study was to examine the feasibility of analyzing amyloid PET scans by voxel-based statistical analysis. The results were then compared to those with the ROI-based mcSUVR. In total, 166 subjects who underwent ¹¹C-PiB PET in the J-ADNI multi-center study were analyzed. Additionally, 18 Aβ-negative images were collected from other studies to form a normal database. The PET images were spatially normalized to the standard space using an adaptive template method without MRI. The mcSUVR was measured using a pre-defined ROI. Voxel-wise Z-scores within the ROI were calculated using the normal database, after which Z-score maps were generated. A receiver operating characteristic (ROC) analysis was performed to evaluate whether Z-sum (sum of the Z-score) and mcSUVR could be used to classify the scans into positive and negative using the central visual read as the reference standard. PET scans that were equivocal were regarded as positive.

Results

Sensitivity and specificity were respectively 90.8% and 100% by Z-sum and 91.8% and 98.5% by mcSUVR. Most of the equivocal scans were subsequently classified by both Z-sum and mcSUVR as false negatives. Z-score maps correctly delineated abnormal Aβ accumulation over the same regions as the visual read.

Conclusions

We examined the usefulness of voxel-based statistical analysis for amyloid PET. This method provides objective Z-score maps and Z-sum values, which were observed to be helpful as an adjunct to visual interpretation especially for cases with mild or limited Aβ accumulation. This approach could improve the Aβ detection sensitivity, reduce inter-reader variability, and allow for detailed monitoring of Aβ deposition.

Trial registration

The number of the J-ADNI study is UMIN000001374

Comparison of visual criteria for amyloid-PET reading: could criteria merging reduce inter-rater variability?

BACKGROUND

Three different amyloid tracers labeled with 18-flourine have been introduced into clinical use. The leaflets of tracers indicate different visual criteria for PET reporting. In clinical practice, it is not yet ascertained whether these criteria are equivalent in terms of diagnostic accuracy or if anyone is better than another. We aimed to evaluate the inter and intra-rater variability of visual assessment of ¹⁸F-Florbetapir PET/CT images among six independent readers with different clinical experience.

METHODS

We analyzed 252 PET/CT scans, visually assessed by each reader three times, applying independently the three different reading criteria proposed. Each reader evaluated the regional uptake specifying for each cortical region a numeric value of grading of positivity in order to assign a final score. At the end of each reading a level of confidence was determined by assigning a score from 0 (negative) to 4 (positive). After first reading, those cases in which the evaluations by two experienced readers did not match (discordant cases) were independently reevaluated merging all the three different visual interpretation criteria.

RESULTS

Good agreement was observed for visual interpretation among the six readers' confidence-level using independently the three visual reading criteria: ICC=0.83 (0.80-0.86) for ¹⁸F-florbetapir, ICC=0.84 (0.81-0.87) for ¹⁸F-florbetaben, and ICC=0.86 (0.83-0.88) for ¹⁸F-flutemetamol reading. A good inter-rater agreement was observed for final-score too: ICC=0.74 (0.70-0.78) for ¹⁸F-florbetapir; ICC=0.82 (0.79-0.85) for ¹⁸F-florbetaben; ICC=0.84 (0.81-0.87) for ¹⁸F-flutemetamol. Intra-rater agreement was good for final-score (from 0.76 to 0.90; P<0.001) and confidence-level (Spearman's rho from 0.89 to 1.00; P<0.001). Disagreement between the two experienced readers was observed in 22 of 252 cases (9%). The agreement converged over a second round of independent reading in 12 of 22 cases (54%), by merging all the criteria.

CONCLUSIONS

All the criteria proposed are useful to determine the grading of positivity or negativity of amyloid deposition and their merging improves the diagnostic confidence and provides a better agreement.

Optimisation and usefulness of quantitative analysis of 18F-florbetapir PET

OBJECTIVES

This study investigates the usefulness of quantitative SUVR thresholds on sub types of typical (type A) and atypical (non-type A) positive (Aβ+) and negative (Aβ-) ¹⁸F-florbetapir scans and aims to optimise the thresholds.

METHODS

Clinical ¹⁸F-florbetapir scans (n = 100) were categorised by sub type and visual reads were performed independently by three trained readers. Inter-reader agreement and reader-to-reference agreement were measured. Optimal SUVR thresholds were derived by ROC analysis and were compared with thresholds derived from a healthy control group and values from published literature.

RESULTS

Sub type division of ¹⁸F-florbetapir PET scans improves accuracy and agreement of visual reads for type A: accuracy 90%, 96% and 70% and agreement κ > 0.7, κ ≥ 0.85 and -0.1 < κ < 0.9 for all data, type A and non-type A respectively. Sub type division also improves quantitative classification accuracy of type A: optimum mcSUVR thresholds were found to be 1.32, 1.18 and 1.48 with accuracy 86%, 92% and 76% for all data, type A and non-type A respectively.

CONCLUSIONS

Aβ+/Aβ- mcSUVR threshold of 1.18 is suitable for classification of type A studies (sensitivity = 97%, specificity = 88%). Region-wise SUVR thresholds may improve classification accuracy in non-type A studies. Amyloid PET scans should be divided by sub type before quantification.

ADVANCES IN KNOWLEDGE

We have derived and validated mcSUVR thresholds for Aβ+/Aβ- ¹⁸F-florbetapir studies. This work demonstrates that division into sub types improves reader accuracy and agreement and quantification accuracy in scans with typical presentation and highlights the atypical presentations not suited to global SUVR quantification.

Semi-quantification and grading of amyloid PET: A project of the European Alzheimer's Disease Consortium (EADC)

Background

amyloid-PET reading has been classically implemented as a binary assessment, although the clinical experience has shown that the number of borderline cases is non negligible not only in epidemiological studies of asymptomatic subjects but also in naturalistic groups of symptomatic patients attending memory clinics. In this work we develop a model to compare and integrate visual reading with two independent semi-quantification methods in order to obtain a tracer-independent multi-parametric evaluation.

Methods

We retrospectively enrolled three cohorts of cognitively impaired patients submitted to ¹⁸F-florbetaben (53 subjects), ¹⁸F-flutemetamol (62 subjects), ¹⁸F-florbetapir (60 subjects) PET/CT respectively, in 6 European centres belonging to the EADC. The 175 scans were visually classified as positive/negative following approved criteria and further classified with a 5-step grading as negative, mild negative, borderline, mild positive, positive by 5 independent readers, blind to clinical data. Scan quality was also visually assessed and recorded. Semi-quantification was based on two quantifiers: the standardized uptake value (SUVr) and the ELBA method. We used a sigmoid model to relate the grading with the quantifiers.

We measured the readers accord and inconsistencies in the visual assessment as well as the relationship between discrepancies on the grading and semi-quantifications.

Conclusion

It is possible to construct a map between different tracers and different quantification methods without resorting to ad-hoc acquired cases. We used a 5-level visual scale which, together with a mathematical model, delivered cut-offs and transition regions on tracers that are (largely) independent from the population. All fluorinated tracers appeared to have the same contrast and discrimination ability with respect to the negative-to-positive grading. We validated the integration of both visual reading and different quantifiers in a more robust framework thus bridging the gap between a binary and a user-independent continuous scale.

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Scans acquired with all commercial amyloid-PET fluorinated tracers are compared.

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2 independent semi-quantification methods provided whole-brain amyloid load values.

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5 readers independently evaluated all scans using a graded scale.

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A mathematical model is used to link visual grading to semi-quantification.

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Mapping between tracers and reader evaluation are given.

Clinical significance of visually equivocal amyloid PET findings from the Alzheimer's Disease Neuroimaging Initiative cohort

To evaluate the clinical and imaging characteristics of patients with visually equivocal amyloid PET images, patients from the Alzheimer's Disease Neuroimaging Initiative cohort who had fluorine-18-florbetapir PET scans both at baseline and 24 months were selected. Five nuclear medicine physicians visually assessed the PET images and classified them as either positive or negative. Images not reaching a majority agreement were classified as equivocal. Among a total of 379 patients, the number of patients in each fluorine-18-florbetapir PET negative/equivocal/positive categories was 218 (57.5%), 32 (8.4%), and 129 (34.0%). Eight to 9% of patients with normal cognition (N=12/141), mild cognitive impairment (N=20/214), and no Alzheimer's disease (N=0/24) showed equivocal PET finding for each. In negative/equivocal/positive groups, positive cerebrospinal fluid Aβ1-42 was observed in 25.7, 81.5, and 98.3%, respectively. Baseline standardized uptake value ratios of fluorine-18-florbetapir PET were 0.75±0.05, 0.86±0.09, and 1.01±0.09, respectively [F(2, 376)=603.547; P<0.001]. After 24 months of follow-up, the standardized uptake value ratios increased by 0.81±2.62, 2.81±2.90, and 2.17±3.66%, respectively [F(2, 376)=7.905, P<0.05 vs. the negative group]. Among mild cognitive impairment patients, the equivocal group showed a more rapid decline in glucose metabolism than the negative group [5.52±5.36 vs. 0.67±4.45; F(2, 122)=9.028, P<0.01]. 8.4% of the patients in this study showed a visually equivocal result of amyloid PET. These patients showed a moderate amount of amyloid accumulation and a rapid rate of accumulation.

The Precuneus – A Witness for Excessive Aβ Gathering in Alzheimer’s Disease Pathology

Evidence of cortical beta-amyloid (Aβ) load, assessed by Aβ positron emission tomography (Aβ-PET), is an established in vivo biomarker of Alzheimer’s disease (AD)-related pathophysiology. Qualitative assessment of Aβ-PET provides binary information; meanwhile semiquantitative approaches require a parcellation of PET image either manually or by placement of atlas-based volumes of interest. We supposed that a whole-brain approach with voxel-by-voxel standardized uptake value ratio (SUVr) parametric images may better elucidate the spatial trajectories of Aβ burden along the continuum of AD. Methods: We recruited 32 subjects with a diagnosis of probable AD dementia (ADD, n = 20) and mild cognitive impairment due to AD (MCI-AD, n = 12) according to the NIA-AA 2011 criteria. We also enrolled a control group of 6 cognitively healthy individuals (HCs) with preserved cognitive functions and negative Aβ-PET scan. The PET images were spatially normalized using the AV45 PET template in the MNI brain space. Subsequently, parametric SUVr images were calculated using the whole cerebellum as a reference region. A voxel-wise analysis of covariance was used to compare (between groups) the Αβ distribution pattern considering age as a nuisance covariate. Results: Both ADD and MCI-AD subjects showed a widespread increase in radiotracer uptake when compared with HC participants (p < 0.001, uncorrected). After applying a multiple comparison correction (p < 0.05, corrected), a relative large cluster of increased [18F]-flor­betapir uptake was observed in the precuneus in the ADD and MCI-AD groups compared to HCs. Voxel-wise regression analysis showed a significant positive linear association between the voxel-wise SUVr values and the disease duration. Conclusions: The voxel-wise semiquantitative analysis shows that the precuneus is a region with higher vulnerability to Aβ depositions when compared to other cortical regions in both MCI-AD and ADD subjects. We think that the precuneus is a promising PET-based outcome measure for clinical trials of drugs targeting brain Aβ. We found a positive association between the overall Aβ-PET SUVr and the disease duration suggesting that the region-specific slow saturation of Aβ deposition continuously takes place as the disease progresses.

Spatial Normalization of 18F-Flutemetamol PET Images Using an Adaptive Principal-Component Template

Though currently approved for visual assessment only, there is evidence to suggest that quantification of amyloid-β (Aβ) PET images may reduce interreader variability and aid in the monitoring of treatment effects in clinical trials. Quantification typically involves a regional atlas in standard space, requiring PET images to be spatially normalized. Different uptake patterns in Aβ-positive and Aβ-negative subjects, however, make spatial normalization challenging. In this study, we proposed a method to spatially normalize ¹⁸F-flutemetamol images using a synthetic template based on principal-component images to overcome these challenges. Methods:¹⁸F-flutemetamol PET and corresponding MR images from a phase II trial (n = 70), including subjects ranging from Aβ-negative to Aβ-positive, were spatially normalized to standard space using an MR-driven registration method (SPM12). ¹⁸F-flutemetamol images were then intensity-normalized using the pons as a reference region. Principal-component images were calculated from the intensity-normalized images. A linear combination of the first 2 principal-component images was then used to model a synthetic template spanning the whole range from Aβ-negative to Aβ-positive. The synthetic template was then incorporated into our registration method, by which the optimal template was calculated as part of the registration process, providing a PET-only–driven registration method. Evaluation of the method was done in 2 steps. First, coregistered gray matter masks generated using SPM12 were spatially normalized using the PET- and MR-driven methods, respectively. The spatially normalized gray matter masks were then visually inspected and quantified. Second, to quantitatively compare the 2 registration methods, additional data from an ongoing study were spatially normalized using both methods, with correlation analysis done on the resulting cortical SUV ratios. Results: All scans were successfully spatially normalized using the proposed method with no manual adjustments performed. Both visual and quantitative comparison between the PET- and MR-driven methods showed high agreement in cortical regions. ¹⁸F-flutemetamol quantification showed strong agreement between the SUV ratios for the PET- and MR-driven methods (R² = 0.996; pons reference region). Conclusion: The principal-component template registration method allows for robust and accurate registration of ¹⁸F-flutemetamol images to a standardized template space, without the need for an MR image.

Emerging topics and practical aspects for an appropriate use of amyloid PET in the current Italian context

In May 2017 some representatives of the Italian nuclear medicine and neurological communities spontaneously met to discuss the issues emerged during the first two years of routine application of amyloid PET with fluorinated radiopharmaceuticals in the real world. The limitations of a binary classification of scans, the possibility to obtain early images as a surrogate marker of regional cerebral bloos flow, the need for (semi-)quantification and, thus, the opportunity of ranking brain amyloidosis, the correlation with Aβ42 levels in the cerebrospinal fluid, the occurrence and biological meaning of uncertain/boderline scans, the issue of incidental amyloidosis, the technical pittfalls leading to false negative/positive results, the position of the tool in the diagnostic flow-chart in the national reality, are the main topics that have been discussed. Also, a card to justify the examination to be filled by the dementia specialist and a card for the nuclear medicine physician to report the exam in detail have been approved and are available in the web, which should facilitate the creation of a national register, as previewed by the 2015 intersocietal recommendation on the use of amyloid PET in Italy. The content of this discussion could stimulate both public institutions and companies to support further research on these topics.

Augmenting Amyloid PET Interpretations With Quantitative Information Improves Consistency of Early Amyloid Detection

PURPOSE

Establishing reliable methods for interpreting elevated cerebral amyloid-β plaque on PET scans is increasingly important for radiologists, as availability of PET imaging in clinical practice increases. We examined a 3-step method to detect plaque in cognitively normal older adults, focusing on the additive value of quantitative information during the PET scan interpretation process.

METHODS

Fifty-five F-florbetapir PET scans were evaluated by 3 experienced raters. Scans were first visually interpreted as having "elevated" or "nonelevated" plaque burden ("Visual Read"). Images were then processed using a standardized quantitative analysis software (MIMneuro) to generate whole brain and region of interest SUV ratios. This "Quantitative Read" was considered elevated if at least 2 of 6 regions of interest had an SUV ratio of more than 1.1. The final interpretation combined both visual and quantitative data together ("VisQ Read"). Cohen kappa values were assessed as a measure of interpretation agreement.

RESULTS

Plaque was elevated in 25.5% to 29.1% of the 165 total Visual Reads. Interrater agreement was strong (kappa = 0.73-0.82) and consistent with reported values. Quantitative Reads were elevated in 45.5% of participants. Final VisQ Reads changed from initial Visual Reads in 16 interpretations (9.7%), with most changing from "nonelevated" Visual Reads to "elevated." These changed interpretations demonstrated lower plaque quantification than those initially read as "elevated" that remained unchanged. Interrater variability improved for VisQ Reads with the addition of quantitative information (kappa = 0.88-0.96).

CONCLUSIONS

Inclusion of quantitative information increases consistency of PET scan interpretations for early detection of cerebral amyloid-β plaque accumulation.

Diagnostic Performance of the Visual Reading of 123I-Ioflupane SPECT Images With or Without Quantification in Patients With Movement Disorders or Dementia

Visual interpretation of ¹²³I-ioflupane SPECT images has high diagnostic accuracy for differentiating parkinsonian syndromes (PS), from essential tremor and probable dementia with Lewy bodies (DLB) from Alzheimer disease. In this study, we investigated the impact on accuracy and reader confidence offered by the addition of image quantification in comparison with visual interpretation alone. Methods: We collected 304 ¹²³I-ioflupane images from 3 trials that included subjects with a clinical diagnosis of PS, non-PS (mainly essential tremor), probable DLB, and non-DLB (mainly Alzheimer disease). Images were reconstructed with standardized parameters before striatal binding ratios were quantified against a normal database. Images were assessed by 5 nuclear medicine physicians who had limited prior experience with ¹²³I-ioflupane interpretation. In 2 readings at least 1 mo apart, readers performed either a visual interpretation alone or a combined reading (i.e., visual plus quantitative data were available). Readers were asked to rate their confidence of image interpretation and judge scans as easy or difficult to read. Diagnostic accuracy was assessed by comparing image results with the standard of truth (i.e., diagnosis at follow-up) by measuring the positive percentage of agreement (equivalent to sensitivity) and the negative percentage of agreement (equivalent to specificity). The hypothesis that the results of the combined reading were not inferior to the results of the visual reading analysis was tested. Results: A comparison of the combined reading and the visual reading revealed a small, insignificant increase in the mean negative percentage of agreement (89.9% vs. 87.9%) and equivalent positive percentages of agreement (80.2% vs. 80.1%). Readers who initially performed a combined analysis had significantly greater accuracy (85.8% vs. 79.2%; P = 0.018), and their accuracy was close to that of the expert readers in the original studies (range, 83.3%-87.2%). Mean reader confidence in the interpretation of images showed a significant improvement when combined analysis was used (P < 0.0001). Conclusion: The addition of quantification allowed readers with limited experience in the interpretation of ¹²³I-ioflupane SPECT scans to have diagnostic accuracy equivalent to that of the experienced readers in the initial studies. Also, the results of the combined reading were not inferior to the results of the visual reading analysis and offered an increase in reader confidence.

Amyloid Imaging: Poised for Integration into Medical Practice

Amyloid imaging represents a significant advance as an adjunct in the diagnosis of Alzheimer's disease (AD) because it is the first imaging modality that identifies in vivo changes known to be associated with the pathogenesis. Initially, 11C-PIB was developed, which was the prototype for many 18F compounds, including florbetapir, florbetaben, and flutemetamol, among others. Despite the high sensitivity and specificity of amyloid imaging, it is not commonly used in clinical practice, mainly because it is not reimbursed under current Center for Medicare and Medicaid Services guidelines in the USA. To guide the field in who would be most appropriate for the utility of amyloid positron emission tomography, current studies are underway [Imaging Dementia Evidence for Amyloid Scanning (IDEAS) Study] that will inform the field on the utilization of amyloid positron emission tomography in clinical practice. With the advent of monoclonal antibodies that specifically target amyloid antibody, there is an interest, possibly a mandate, to screen potential treatment recipients to ensure that they are suitable for treatment. In this review, we summarize progress in the field to date.

Inter-rater variability of visual interpretation and comparison with quantitative evaluation of 11C-PiB PET amyloid images of the Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI) multicenter study

PURPOSE

The aim of this study was to assess the inter-rater variability of the visual interpretation of ¹¹C-PiB PET images regarding the positivity/negativity of amyloid deposition that were obtained in a multicenter clinical research project, Japanese Alzheimer's Disease Neuroimaging Initiative (J-ADNI). The results of visual interpretation were also compared with a semi-automatic quantitative analysis using mean cortical standardized uptake value ratio to the cerebellar cortex (mcSUVR).

METHODS

A total of 162 ¹¹C-PiB PET scans, including 45 mild Alzheimer's disease, 60 mild cognitive impairment, and 57 normal cognitive control cases that had been acquired as J-ADNI baseline scans were analyzed. Based on visual interpretation by three independent raters followed by consensus read, each case was classified into positive, equivocal, and negative deposition (ternary criteria) and further dichotomized by merging the former two (binary criteria).

RESULTS

Complete agreement of visual interpretation by the three raters was observed for 91.3% of the cases (Cohen κ = 0.88 on average) in ternary criteria and for 92.3% (κ = 0.89) in binary criteria. Cases that were interpreted as visually positive in the consensus read showed significantly higher mcSUVR than those visually negative (2.21 ± 0.37 vs. 1.27 ± 0.09, p < 0.001), and positive or negative decision by visual interpretation was dichotomized by a cut-off value of mcSUVR = 1.5. Significant positive/negative associations were observed between mcSUVR and the number of raters who evaluated as positive (ρ = 0.87, p < 0.0001) and negative (ρ = -0.85, p < 0.0001) interpretation. Cases of disagreement among raters showed generally low mcSUVR.

CONCLUSIONS

Inter-rater agreement was almost perfect in ¹¹C-PiB PET scans. Positive or negative decision by visual interpretation was dichotomized by a cut-off value of mcSUVR = 1.5. As some cases of disagreement among raters tended to show low mcSUVR, referring to quantitative method may facilitate correct diagnosis when evaluating images of low amyloid deposition.

Classification of amyloid status using machine learning with histograms of oriented 3D gradients

Brain amyloid burden may be quantitatively assessed from positron emission tomography imaging using standardised uptake value ratios. Using these ratios as an adjunct to visual image assessment has been shown to improve inter-reader reliability, however, the amyloid positivity threshold is dependent on the tracer and specific image regions used to calculate the uptake ratio. To address this problem, we propose a machine learning approach to amyloid status classification, which is independent of tracer and does not require a specific set of regions of interest. Our method extracts feature vectors from amyloid images, which are based on histograms of oriented three-dimensional gradients. We optimised our method on 133 ¹⁸F-florbetapir brain volumes, and applied it to a separate test set of 131 volumes. Using the same parameter settings, we then applied our method to 209 ¹¹C-PiB images and 128 ¹⁸F-florbetaben images. We compared our method to classification results achieved using two other methods: standardised uptake value ratios and a machine learning method based on voxel intensities. Our method resulted in the largest mean distances between the subjects and the classification boundary, suggesting that it is less likely to make low-confidence classification decisions. Moreover, our method obtained the highest classification accuracy for all three tracers, and consistently achieved above 96% accuracy.

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A machine learning approach to brain amyloid status classification is proposed.

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The method is independent of PET tracer and requires little recalibration.

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Classification accuracy was higher than SUVR for three amyloid tracers.