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

The usefulness of combined analysis using CIScore and VSRAD parameters for differentiating between dementia with Lewy body and Alzheimer's disease

PURPOSE

The Cingulate Island score (CIScore) is useful index for differentiating between dementia with Lewy body (DLB) and Alzheimer's disease (AD) using regional cerebral blood flow (rCBF) SPECT. The Z score standing for medial temporal lobe (MTL) atrophy and the ratio of Z score between dorsal brain stem (DBS) to MTL are useful indices for differentiating between DLB and AD using MRI with VSRAD. The current study investigated the diagnostic ability by the combined use of rCBF SPECT and MRI in the differentiation between AD and DLB.

MATERIALS AND METHODS

In cases with 42 AD and 28 DLB undertaken Tc-99m-ECD SPECT and MRI, we analyzed differential diagnostic ability between AD and DLB among following conditions by single or combined settings. Namely, they were (1) the CIScore as a parameter of rCBF SPECT (DLB ≦ 0.25), (2) Z score value of MTL atrophy (DLB ≦ 2.05), (3) the ratio of Z score of DBS to medial temporal gray matter as a parameter of brain atrophy using VSRAD (DLB ≧ 0.38). Also, we analyzed them both including and omitting the elderly (over 75 years old).

RESULTS

The accuracy of differential diagnosis in this condition was 74% for (1), 69% for (2), and 67% for (3). The accuracy by combination condition was 84% for (1) and (2), 81% for (1) and (3), and 67% for (2) and (3), respectively. The combination method by CIScore and the Z score of MTL showed the best accuracy. When we confined condition to ages younger than 75 years, the accuracy improved to 94% in the combination method.

CONCLUSION

The combined use of CIScore and Z score of MTL was suggested to be useful in the differential diagnosis between DLB and AD particularly in younger than 75 years old.

A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease

Alzheimer's disease (AD) is the main cause of dementia, with its diagnosis and management remaining challenging. Amyloid positron emission tomography (PET) has become increasingly important in medical practice for patients with AD. To integrate and update previous guidelines in the field, a task group of experts of several disciplines from multiple countries was assembled, and they revised and approved the content related to the application of amyloid PET in the medical settings of cognitively impaired individuals, focusing on clinical scenarios, patient preparation, administered activities, as well as image acquisition, processing, interpretation and reporting. In addition, expert opinions, practices, and protocols of prominent research institutions performing research on amyloid PET of dementia are integrated. With the increasing availability of amyloid PET imaging, a complete and standard pipeline for the entire examination process is essential for clinical practice. This international consensus and practice guideline will help to promote proper clinical use of amyloid PET imaging in patients with AD.

Early Brain Amyloid Accumulation at PET in Military Instructors Exposed to Subconcussive Blast Injuries

Background Traumatic brain injury (TBI) is the leading cause of disability in young adults. Recurrent TBI is associated with a range of neurologic sequelae, but the contributing factors behind the development of such chronic encephalopathy are poorly understood. Purpose To quantify early amyloid β deposition in the brain of otherwise healthy adult men exposed to repeated subconcussive blast injury using amyloid PET. Materials and Methods In this prospective study from January 2020 to December 2021, military instructors who were routinely exposed to repeated blast events were evaluated at two different points: baseline (before blast exposure from breacher or grenade) and approximately 5 months after baseline (after blast exposure). Age-matched healthy control participants not exposed to blasts and without a history of brain injury were evaluated at similar two points. Neurocognitive evaluation was performed with standard neuropsychologic testing in both groups. Analysis of PET data consisted of standardized uptake value measurements in six relevant brain regions and a whole-brain voxel-based statistical approach. Results Participants were men (nine control participants [median age, 33 years; IQR, 32-36 years] and nine blast-exposed participants [median age, 33 years; IQR, 30-34 years]; P = .82). In the blast-exposed participants, four brain regions showed significantly increased amyloid deposition after blast exposure: inferomedial frontal lobe (P = .004), precuneus (P = .02), anterior cingulum (P = .002), and superior parietal lobule (P = .003). No amyloid deposition was observed in the control participants. Discriminant analysis on the basis of regional changes of amyloid accumulation correctly classified the nine healthy control participants as healthy control participants (100%), and seven of the nine blast-exposed participants (78%) were correctly classified as blast exposed. Based on the voxel-based analysis, whole-brain parametric maps of early abnormal early amyloid uptake were obtained. Conclusion Early brain amyloid accumulation was identified and quantified at PET in otherwise healthy adult men exposed to repetitive subconcussive traumatic events. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Haller in this issue.

Software compatibility analysis for quantitative measures of [18F]flutemetamol amyloid PET burden in mild cognitive impairment

RATIONALE

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease pathogenesis. In clinical practice, trained readers will visually categorise positron emission tomography (PET) scans as either Aβ positive or negative. However, adjunct quantitative analysis is becoming more widely available, where regulatory approved software can currently generate metrics such as standardised uptake value ratios (SUVr) and individual Z-scores. Therefore, it is of direct value to the imaging community to assess the compatibility of commercially available software packages. In this collaborative project, the compatibility of amyloid PET quantification was investigated across four regulatory approved software packages. In doing so, the intention is to increase visibility and understanding of clinically relevant quantitative methods.

METHODS

Composite SUVr using the pons as the reference region was generated from [¹⁸F]flutemetamol (GE Healthcare) PET in a retrospective cohort of 80 amnestic mild cognitive impairment (aMCI) patients (40 each male/female; mean age = 73 years, SD = 8.52). Based on previous autopsy validation work, an Aβ positivity threshold of ≥ 0.6 SUVr_pons was applied. Quantitative results from MIM Software's MIMneuro, Syntermed's NeuroQ, Hermes Medical Solutions' BRASS and GE Healthcare's CortexID were analysed using intraclass correlation coefficient (ICC), percentage agreement around the Aβ positivity threshold and kappa scores.

RESULTS

Using an Aβ positivity threshold of ≥ 0.6 SUVr_pons, 95% agreement was achieved across the four software packages. Two patients were narrowly classed as Aβ negative by one software package but positive by the others, and two patients vice versa. All kappa scores around the same Aβ positivity threshold, both combined (Fleiss') and individual software pairings (Cohen's), were ≥ 0.9 signifying "almost perfect" inter-rater reliability. Excellent reliability was found between composite SUVr measurements for all four software packages, with an average measure ICC of 0.97 and 95% confidence interval of 0.957-0.979. Correlation coefficient analysis between the two software packages reporting composite z-scores was strong (r² = 0.98).

CONCLUSION

Using an optimised cortical mask, regulatory approved software packages provided highly correlated and reliable quantification of [¹⁸F]flutemetamol amyloid PET with a ≥ 0.6 SUVr_pons positivity threshold. In particular, this work could be of interest to physicians performing routine clinical imaging rather than researchers performing more bespoke image analysis. Similar analysis is encouraged using other reference regions as well as the Centiloid scale, when it has been implemented by more software packages.

Semiquantitative Approach to Amyloid Positron Emission Tomography Interpretation in Clinical Practice

Objective  Amyloid positron emission tomography (PET) plays a vital role in the in vivo detection of β-amyloid accumulation in Alzheimer's disease. Increasingly, trainees and infrequent readers are relying on semiquantitative analyses to support clinical diagnostic efforts. Our objective was to determine if the visual assessment of amyloid PET may be facilitated by relying on semiquantitative analysis. Methods  We conducted a retrospective review of [ ¹⁸ F]-florbetaben PET/computed tomographies (CTs) from 2016 to 2018. Visual interpretation to determine Aβ+ status was conducted by two readers blinded to each other's interpretation. Scans were then post-processed utilizing the MIMneuro software, which generated regional-based semiquantitative Z-scores indicating cortical Aβ-burden. Results  Of 167 [ ¹⁸ F]-florbetaben PET/CTs, 92/167 (reader-1) and 101/167 (reader-2) were positive for amyloid deposition (agreement = 92.2%, κ = 0.84). Additional nine scans were identified as possible Aβ-positive based solely on semiquantitative analyses. Largest semiquantitative differences were identified in the left frontal lobe (Z = 7.74 in Aβ + ; 0.50 in Aβ - ). All unilateral regions showed large statistically significant differences in Aβ-burden ( P ≤ 2.08E-28). Semiquantitative scores were highly sensitive to Aβ+ status and accurate in their ability to identify amyloid positivity, defined as a positive scan by both readers (AUC ≥ 0.90 [0.79-1.00]). Spread analyses suggested that amyloid deposition was most severe in the left posterior cingulate gyrus. The largest differences between Aβ +/Aβ- were in the left frontal lobe. Analyses using region-specific cutoffs indicated that the presence of amyloid in the temporal and anterior cingulate cortex, while exhibiting relatively low Z-scores, was most common. Conclusion  Visual assessment and semiquantitative analysis provide highly congruent results, thereby enhancing reader confidence and improving scan interpretation. This is particularly relevant, given recent advances in amyloid-targeting disease-modifying therapeutics.

Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging

Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.

Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity

BACKGROUND

The detection of amyloid-β (Aβ) deposition in the brain provides crucial evidence in the clinical diagnosis of Alzheimer's disease (AD). However, the current positron emission tomography (PET)-based brain Aβ examination suffers from the problems of coarse visual inspection (in many cases, with 2-class stratification) and high scanning cost.

OBJECTIVE

1) To characterize the non-binary Aβ deposition levels in the AD continuum based on clustering of PET data, and 2) to explore the feasibility of predicting individual Aβ deposition grades with non-invasive functional magnetic resonance imaging (fMRI).

METHODS

1) Individual whole-brain Aβ-PET images from the OASIS-3 dataset (N = 258) were grouped into three clusters (grades) with t-SNE and k-means. The demographical data as well as global and regional standard uptake value ratios (SUVRs) were compared among the three clusters with Chi-square tests or ANOVA tests. 2) From resting-state fMRI, both conventional functional connectivity (FC) and high-order FC networks were constructed and the topological architectures of the two networks were jointly learned with graph convolutional networks (GCNs) to predict the Aβ-PET grades for each individual.

RESULTS

We found three clearly separated clusters, indicating three Aβ-PET grades. There were significant differences in gender, age, cognitive ability, APOE type, as well as global and regional SUVRs among the three grades we found. The prediction of Aβ-PET grades with GCNs on FC for the 258 participants in the AD continuum reached a satisfactory averaged accuracy (78.8%) in the two-class classification tasks.

CONCLUSION

The results demonstrated the feasibility of using deep learning on a non-invasive brain functional imaging technique to approximate PET-based Aβ deposition grading.