The influence of biological and technical factors on quantitative analysis of amyloid PET: Points to consider and recommendations for controlling variability in longitudinal data.

In vivo imaging of amyloid burden with positron emission tomography (PET) provides a means for studying the pathophysiology of Alzheimer's and related diseases. Measurement of subtle changes in amyloid burden requires quantitative analysis of image data. Reliable quantitative analysis of amyloid PET scans acquired at multiple sites and over time requires rigorous standardization of acquisition protocols, subject management, tracer administration, image quality control, and image processing and analysis methods. We review critical points in the acquisition and analysis of amyloid PET, identify ways in which technical factors can contribute to measurement variability, and suggest methods for mitigating these sources of noise. Improved quantitative accuracy could reduce the sample size necessary to detect intervention effects when amyloid PET is used as a treatment end point and allow more reliable interpretation of change in amyloid burden and its relationship to clinical course.

Precision medicine in neurodegeneration: the IHI-PROMINENT project.

Neurodegenerative diseases are one of the most important contributors to morbidity and mortality in the elderly. In Europe, over 14 million people are currently living with dementia, at a cost of over 400 billion EUR annually. Recent advances in diagnostics and approval for new pharmaceutical treatments for Alzheimer's disease (AD), the most common etiology of dementia, heralds the beginning of precision medicine in this field. However, their implementation will challenge an already over-burdened healthcare systems. There is a need for innovative digital solutions that can drive the related clinical pathways and optimize and personalize care delivery. Public-private partnerships are ideal vehicles to tackle these challenges. Here we describe the Innovative Health Initiative (IHI) public-private partnership project PROMINENT that has been initiated by connecting leading dementia researchers, medical professionals, dementia patients and their care partners with the latest innovative health technologies using a precision medicine based digital platform. The project builds upon the knowledge and already implemented digital tools from several collaborative initiatives that address new models for early detection, diagnosis, and monitoring of AD and other neurodegenerative disorders. The project aims to provide support to improvement efforts to each aspect of the care pathway including diagnosis, prognosis, treatment, and data collection for real world evidence and cost effectiveness studies. Ultimately the PROMINENT project is expected to lead to cost-effective care and improved health outcomes.

Software tool for improved prediction of Alzheimer's disease.

BACKGROUND: Diagnostic criteria of Alzheimer's disease (AD) emphasize the integration of clinical data and biomarkers. In practice, collection and analysis of patient data vary greatly across different countries and clinics. OBJECTIVE: The goal was to develop a versatile and objective clinical decision support system that could reduce diagnostic errors and highlight early predictors of AD. METHODS: Novel data analysis methods were developed to derive composite disease indicators from heterogeneous patient data. Visualizations that communicate these findings were designed to help the interpretation. The methods were implemented with a software tool that is aimed for daily clinical practice. RESULTS: With the tool, clinicians can analyze available patients as a whole, study them statistically against previously diagnosed cases, and characterize the patients with respect to having AD. The tool is able to work with virtually any patient measurement data, as long as they are stored in electronic format or manually entered into the system. For a subset of patients from the test cohort, the tool was able to predict conversion to AD at an accuracy of 93.6%. CONCLUSION: The software tool developed in this study provides objective information for early detection and prediction of AD based on interpretable visualizations of patient data.

Combination of biomarkers: PET [18F]flutemetamol imaging and structural MRI in dementia and mild cognitive impairment.

BACKGROUND: The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aß accumulation and neuronal degeneration or injury. OBJECTIVE: It was the aim of this study to compute hippocampus volume from MRI and a neocortical standard uptake value ratio (SUVR) from [(18)F]flutemetamol PET and investigate the performance of these biomarkers when used individually and when combined. METHODS: Fully automated methods for hippocampus segmentation and for computation of neocortical SUVR were applied to MR and scans with the investigational imaging agent [(18)F]flutemetamol in a cohort comprising 27 AD patients, 25 healthy volunteers (HVs) and 20 subjects with amnestic mild cognitive impairment (MCI). Clinical follow-up was performed 2 years after the initial assessment. RESULTS: Hippocampus volumes showed extensive overlap between AD and HV cases while PET SUVRs showed clear group clustering. When both measures were combined, there was a relatively compact cluster of HV scans and a less compact AD cluster. MCI cases had a bimodal distribution of SUVRs. [(18)F]Flutemetamol-positive MCI subjects showed a large variability in hippocampus volumes, indicating that these subjects were in different stages of neurodegeneration. Some [(18)F]flutemetamol-negative MCI scans had hippocampus volumes that were well below the HV range. Clinical follow-up showed that 8 of 9 MCI to AD converters came from the [(18)F]flutemetamol-positive group. CONCLUSION: Combining [(18)F]flutemetamol PET with structural MRI provides additional information for categorizing disease and potentially predicting shorter time to progression from MCI to AD, but this has to be validated in larger longitudinal studies.

Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease.

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and compared to the conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1% for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.

Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer's disease.

Assessment of temporal lobe atrophy from magnetic resonance images is a part of clinical guidelines for the diagnosis of prodromal Alzheimer's disease. As hippocampus is known to be among the first areas affected by the disease, fast and robust definition of hippocampus volume would be of great importance in the clinical decision making. We propose a method for computing automatically the volume of hippocampus using a modified multi-atlas segmentation framework, including an improved initialization of the framework and the correction of partial volume effect. The method produced a high similarity index, 0.87, and correlation coefficient, 0.94, with semi-automatically generated segmentations. When comparing hippocampus volumes extracted from 1.5T and 3T images, the absolute value of the difference was low: 3.2% of the volume. The correct classification rate for Alzheimer's disease and cognitively normal cases was about 80% while the accuracy 65% was obtained for classifying stable and progressive mild cognitive impairment cases. The method was evaluated in three cohorts consisting altogether about 1000 cases, the main emphasis being in the analysis of the ADNI cohort. The computation time of the method is about 2 minutes on a standard laptop computer. The results show a clear potential for applying the method in clinical practice.

18F-flutemetamol amyloid imaging in Alzheimer disease and mild cognitive impairment: a phase 2 trial.

OBJECTIVE: The most widely studied positron emission tomography ligand for in vivo beta-amyloid imaging is (11)C-Pittsburgh compound B ((11)C-PIB). Its availability, however, is limited by the need for an on-site cyclotron. Validation of the (18)F-labeled PIB derivative (18)F-flutemetamol could significantly enhance access to this novel technology. METHODS: Twenty-seven patients with early-stage clinically probable Alzheimer disease (AD), 20 with amnestic mild cognitive impairment (MCI), and 15 cognitively intact healthy volunteers (HVs) above and 10 HVs below 55 years of age participated. The primary endpoint was the efficacy of blinded visual assessments of (18)F-flutemetamol scans in assigning subjects to a raised versus normal uptake category, with clinical diagnosis as the standard of truth (SOT). As secondary objectives, we determined the correlation between the regional standardized uptake value ratios (SUVRs) for (18)F-flutemetamol and its parent molecule (11)C-PIB in 20 of the AD subjects and 20 of the MCI patients. We also determined test-retest variability of (18)F-flutemetamol SUVRs in 5 of the AD subjects. RESULTS: Blinded visual assessments of (18)F-flutemetamol scans assigned 25 of 27 scans from AD subjects and 1 of 15 scans from the elderly HVs to the raised category, corresponding to a sensitivity of 93.1% and a specificity of 93.3% against the SOT. Correlation coefficients between cortical (18)F-flutemetamol SUVRs and (11)C-PIB SUVRs ranged from 0.89 to 0.92. Test-retest variabilities of regional SUVRs were 1 to 4%. INTERPRETATION: (18)F-Flutemetamol performs similarly to the (11)C-PIB parent molecule within the same subjects and provides high test-retest replicability and potentially much wider accessibility for clinical and research use.

The importance of appropriate partial volume correction for PET quantification in Alzheimer's disease.

PURPOSE: Alzheimer's disease (AD) is the most common form of dementia. Clinically, it is characterized by progressive cognitive and functional impairment with structural hallmarks of cortical atrophy and ventricular expansion. Amyloid plaque aggregation is also known to occur in AD subjects. In-vivo imaging of amyloid plaques is now possible with positron emission tomography (PET) radioligands. PET imaging suffers from a degrading phenomenon known as the partial volume effect (PVE). The quantitative accuracy of PET images is reduced by PVEs primarily due to the limited spatial resolution of the scanner. The degree of PVE is influenced by structure size, with smaller structures tending to suffer from more severe PVEs such as atrophied grey matter regions. The aims of this paper were to investigate the effect of partial volume correction (PVC) on the quantification of amyloid PET and to highlight the importance of selecting an appropriate PVC technique. METHODS: An improved PVC technique, region-based voxel-wise (RBV) correction, was compared against existing Van-Cittert (VC) and Müller-Gärtner (MG) methods using amyloid PET imaging data. Digital phantom data were produced using segmented MRI scans from a control subject and an AD subject. Typical tracer distributions were generated for each of the phantom anatomies. Also examined were 70 clinical PET scans acquired using [(18)F]flutemetamol. Volume of interest (VOI) analysis was performed for corrected and uncorrected images. RESULTS: PVC was shown to improve the quantitative accuracy of regional analysis performed on amyloid PET images. Of the corrections applied, VC deconvolution demonstrated the worst recovery of grey matter values. MG PVC was shown to induce biases in some grey matter regions due to grey matter variability. In addition, white matter variability was shown to influence the accuracy of MG PVC in cortical grey matter and also cerebellar grey matter, a typical reference region for amyloid PET normalization in sporadic AD. RBV was shown to be more accurate than MG in terms of grey matter and white matter uptake. An increase in within-group variability after PVC was observed and is believed to be a genuine, more accurate representation of the data rather than a correction-induced error. The standardized uptake value ratio (SUVR) threshold for classifying subjects as either amyloid-positive or amyloid-negative was found to be 1.64 in the uncorrected dataset, rising to 2.25 after PVC. CONCLUSION: Care should be taken when applying PVC to amyloid PET images. Assumptions made in existing PVC strategies can induce biases that could lead to erroneous inferences about uptake in certain regions. The proposed RBV PVC technique accounts for within-compartment variability, with the potential to reduce errors of this kind.

Fast and robust multi-atlas segmentation of brain magnetic resonance images.

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity modelling based on segmentation using expectation maximisation (EM) and optimisation via graph cuts. The segmentation pipeline is evaluated with two data cohorts: IBSR data (N=18, six subcortial structures: thalamus, caudate, putamen, pallidum, hippocampus, amygdala) and ADNI data (N=60, hippocampus). The average similarity index between automatically and manually generated volumes was 0.849 (IBSR, six subcortical structures) and 0.880 (ADNI, hippocampus). The correlation coefficient for hippocampal volumes was 0.95 with the ADNI data. The computation time using a standard multicore PC computer was about 3-4 min. Our results compare favourably with other recently published results.

Phase 1 study of the Pittsburgh compound B derivative 18F-flutemetamol in healthy volunteers and patients with probable Alzheimer disease.

UNLABELLED: (11)C-Pittsburgh compound B (PiB) marks Abeta amyloidosis, a key pathogenetic process in Alzheimer disease (AD). The use of (11)C-PiB is limited to centers with a cyclotron. Development of the (18)F-labeled thioflavin derivative of PiB, (18)F-flutemetamol, could hugely increase the availability of this new technology. The aims of this phase 1 study were to perform brain kinetic modeling of (18)F-flutemetamol, optimize the image acquisition procedure, and compare methods of analysis (step 1) and to compare (18)F-flutemetamol brain retention in AD patients versus healthy controls in a proof-of-concept study (steps 1 and 2). METHODS: In step 1, 3 AD patients (Mini-Mental State Examination, 22-24) and 3 elderly healthy controls were scanned dynamically during windows of 0-90, 150-180, and 220-250 min after injection of approximately 180 MBq of (18)F-flutemetamol, with arterial sampling. We compared different analysis methods (compartmental modeling, Logan graphical analysis, and standardized uptake value ratios) and determined the optimal acquisition window for step 2. In step 2, 5 AD patients (Mini-Mental State Examination, 20-26) and 5 elderly healthy controls were scanned from 80 to 170 min after injection. To determine overall efficacy, steps 1 and 2 were pooled and standardized uptake value ratios were calculated using cerebellar cortex as a reference region. RESULTS: No adverse events were reported. There was a strong correlation between uptake values obtained with the different analysis methods. From 80 min after injection onward, the ratio of neocortical to cerebellar uptake was maximal and only marginally affected by scan start time or duration. AD patients showed significantly increased standardized uptake value ratios in neocortical association zones and striatum, compared with healthy controls, whereas uptake in white matter, cerebellum, and pons did not differ between groups. Two AD patients were (18)F-flutemetamol-negative and 1 healthy control was (18)F-flutemetamol-positive. CONCLUSION: (18)F-flutemetamol uptake can be readily quantified. This phase 1 study warrants further studies to validate this (18)F-labeled derivative of PiB as a biomarker for Abeta amyloidosis.

Visualization and Quantification of 3-Dimensional Stereotactic Surface Projections for 18F-Flutemetamol PET Using Variable Depth.

UNLABELLED: Three-dimensional stereotactic surface projection (3D-SSP) is a widely used method for the analysis of clinical (18)F-FDG brain studies. However, for PET amyloid scans the use of 3D-SSP is challenging because of nonspecific uptake in white matter. Our objective was to implement a method for 3D-SSP quantification and visualization of (18)F-flutemetamol images that avoids extraction of white matter signal. METHODS: Triangulated brain surface models were extracted from a T1-weighted MR template image. Using an (18)F-flutemetamol-negative template, a maximum depth for each vertex on the surface models was calculated to avoid extraction of white matter. The method was evaluated using (18)F-flutemetamol images from 2 cohorts. Cohort 1 consisted of 105 healthy volunteers and was used to create a normal database for each reference region. Cohort 2 consisted of 171 subjects including patients with Alzheimer disease and mild cognitive impairment and healthy volunteers. Images were spatially normalized using an adaptive template registration method, and SUV ratio 3D-SSP values were computed using the pons and cerebellar cortex as reference regions. Images from cohort 2 were then compared with the normal database and classified into negatives and positives, based on a calculated z score threshold. The results were compared with consensus visual interpretation results from 5 trained interpreters blinded to clinical data. RESULTS: With the pons as the reference region, the optimal z score threshold was 1.97, resulting in an overall agreement with visual interpretation results in 170 of 171 images (99.42%). With the cerebellar cortex as the reference region, the optimal z score threshold was 2.41, with an overall agreement with visual interpretation in 168 of 171 images (98.25%). CONCLUSION: Variable-depth 3D-SSP allows computation and visualization of (18)F-flutemetamol 3D-SSP maps, with minimized contribution from white matter signal while retaining sensitivity in detecting gray matter signal.

Blood protein predictors of brain amyloid for enrichment in clinical trials?

BACKGROUND: Measures of neocortical amyloid burden (NAB) identify individuals who are at substantially greater risk of developing Alzheimer's disease (AD). Blood-based biomarkers predicting NAB would have great utility for the enrichment of AD clinical trials, including large-scale prevention trials. METHODS: Nontargeted proteomic discovery was applied to 78 subjects from the Australian Imaging, Biomarkers and Lifestyle Flagship Study of Ageing with a range of NAB values. Technical and independent replications were performed by immunoassay. RESULTS: Seventeen discovery candidates were selected for technical replication. α2-Macroglobulin, fibrinogen γ-chain (FGG), and complement factor H-related protein 1 were confirmed to be associated with NAB. In an independent cohort, FGG plasma levels combined with age predicted NAB had a sensitivity of 59% and specificity of 78%. CONCLUSION: A single blood protein, FGG, combined with age, was shown to relate to NAB and therefore could have potential for enrichment of clinical trial populations.

Training and assessment of laparoscopic skills using a haptic simulator.

Surgical simulation is a promising technique for training of laparoscopic surgery. Computer based simulation provides not only a cost effective alternative to traditional training but also a way to assess the surgeons performance. In this paper, we present a haptic simulator that allows for training and assessment of basic laparoscopic skills. The skills trained are modeled around a cholecystectomy procedure and include bi-manual dissection, clips setting, catheter insertion and cutting. The system uses accurate anatomic models of the organs involved in the procedure. This combined with effective methods for soft tissue deformation and haptic feedback, giving the surgeon a precise feeling of the interaction between organs and surgical instruments, provides a realistic training environment. The system has been designed with procedural training in mind and by putting together the individual tasks it will be possible to train on performing a complete cholecystectomy procedure.

The empirical variance estimator for computer aided diagnosis: lessons for algorithm validation.

Computer aided diagnosis is an established field in medical image analysis; a great deal of effort goes into the development and refinement of pipelines to achieve greater performance. This improvement is dependent on reliable comparison, which is intimately related to variance estimation. For supervised methods, this can be confounded by statistical issues at the comparatively small sample sizes typical of the field. Given the importance of reliable comparison to pipeline development, this issue has received relatively little attention. As a solution, we advocate an empirical variance estimator based on validation within disjoint subsets of the available data. Using Alzheimer's disease classification in the ADNI dataset as an examplar, we investigate the behaviour of different variance estimators in a series of resampling experiments. We show that the proposed estimator is unbiased, and that it exceeds the estimates of naive approaches, which are biased down. Because the estimator avoids independence assumptions, it is able to accommodate arbitrary validation strategies and performance metrics. As it is unbiased, it is able to provide statistically convincing comparison and confidence intervals for algorithm performance. Finally, we show how the estimator can be used to compare different validation strategies, and make some recommendations about which should be used.

Automated quantification of 18F-flutemetamol PET activity for categorizing scans as negative or positive for brain amyloid: concordance with visual image reads.

UNLABELLED: Clinical trials of the PET amyloid imaging agent (18)F-flutemetamol have used visual assessment to classify PET scans as negative or positive for brain amyloid. However, quantification provides additional information about regional and global tracer uptake and may have utility for image assessment over time and across different centers. Using postmortem brain neuritic plaque density data as a truth standard to derive a standardized uptake value ratio (SUVR) threshold, we assessed a fully automated quantification method comparing visual and quantitative scan categorizations. We also compared the histopathology-derived SUVR threshold with one derived from healthy controls. METHODS: Data from 345 consenting subjects enrolled in 8 prior clinical trials of (18)F-flutemetamol injection were used. We grouped subjects into 3 cohorts: an autopsy cohort (n = 68) comprising terminally ill patients with postmortem confirmation of brain amyloid status; a test cohort (n = 172) comprising 33 patients with clinically probable Alzheimer disease, 80 patients with mild cognitive impairment, and 59 healthy volunteers; and a healthy cohort of 105 volunteers, used to define a reference range for SUVR. Visual image categorizations for comparison were from a previous study. A fully automated PET-only quantification method was used to compute regional neocortical SUVRs that were combined into a single composite SUVR. An SUVR threshold for classifying scans as positive or negative was derived by ranking the PET scans from the autopsy cohort based on their composite SUVR and comparing data with the standard of truth based on postmortem brain amyloid status for subjects in the autopsy cohort. The derived threshold was used to categorize the 172 scans in the test cohort as negative or positive, and results were compared with categorization using visual assessment. Different reference and composite region definitions were assessed. Threshold levels were also compared with corresponding thresholds derived from the healthy group. RESULTS: Automated quantification (using pons as the reference region) demonstrated 91% sensitivity and 88% specificity and gave 3 false-positive and 4 false-negative scans. All 3 false-positive cases were either borderline-normal by standard of truth or had moderate to heavy cortical diffuse plaque burden. In the test cohort, the concordance between quantitative and visual read categorization ranged from 97.1% to 99.4% depending on the selection of reference and composite regions. The threshold derived from the healthy group was close to the histopathology-derived threshold. CONCLUSION: Categorization of (18)F-flutemetamol amyloid imaging data using an automated PET-only quantification method showed good agreement with histopathologic classification of neuritic plaque density and a strong concordance with visual read results.

Implementation and validation of an adaptive template registration method for 18F-flutemetamol imaging data.

UNLABELLED: The spatial normalization of PET amyloid imaging data is challenging because different white and gray matter patterns of negative (Aß-) and positive (Aß+) uptake could lead to systematic bias if a standard method is used. In this study, we propose the use of an adaptive template registration method to overcome this problem. METHODS: Data from a phase II study (n = 72) were used to model amyloid deposition with the investigational PET imaging agent (18)F-flutemetamol. Linear regression of voxel intensities on the standardized uptake value ratio (SUVR) in a neocortical composite region for all scans gave an intercept image and a slope image. We devised a method where an adaptive template image spanning the uptake range (the most Aß- to the most Aß+ image) can be generated through a linear combination of these 2 images and where the optimal template is selected as part of the registration process. We applied the method to the (18)F-flutemetamol phase II data using a fixed volume of interest atlas to compute SUVRs. Validation was performed in several steps. The PET-only adaptive template registration method and the MR imaging-based method used in statistical parametric mapping were applied to spatially normalize PET and MR scans, respectively. Resulting transformations were applied to coregistered gray matter probability maps, and the quality of the registrations was assessed visually and quantitatively. For comparison of quantification results with an independent patient-space method, FreeSurfer was used to segment each subject's MR scan and the parcellations were applied to the coregistered PET scans. We then correlated SUVRs for a composite neocortical region obtained with both methods. Furthermore, to investigate whether the (18)F-flutemetamol model could be generalized to (11)C-Pittsburgh compound B ((11)C-PIB), we applied the method to Australian Imaging, Biomarkers and Lifestyle (AIBL) (11)C-PIB scans (n = 285) and compared the PET-only neocortical composite score with the corresponding score obtained with a semimanual method that made use of the subject's MR images for the positioning of regions. RESULTS: Spatial normalization was successful on all scans. Visual and quantitative comparison of the new PET-only method with the MR imaging-based method of statistical parametric mapping indicated that performance was similar in the cortical regions although the new PET-only method showed better registration in the cerebellum and pons reference region area. For the (18)F-flutemetamol quantification, there was a strong correlation between the PET-only and FreeSurfer SUVRs (Pearson r = 0.96). We obtained a similar correlation for the AIBL (11)C-PIB data (Pearson r = 0.94). CONCLUSION: The derived adaptive template registration method allows for robust, accurate, and fully automated quantification of uptake for (18)F-flutemetamol and (11)C-PIB scans without the use of MR imaging data.

The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.

Mechanism of amyloid removal in patients with Alzheimer disease treated with gantenerumab.

BACKGROUND: Gantenerumab is a fully human anti-Aß monoclonal antibody in clinical development for the treatment of Alzheimer disease (AD). OBJECTIVES: To investigate whether treatment with gantenerumab leads to a measurable reduction in the level of Aß amyloid in the brain and to elucidate the mechanism of amyloid reduction. DESIGN: A multicenter, randomized, double-blind, placebo-controlled, ascending-dose positron emission tomographic study. Additionally, ex vivo studies of human brain slices from an independent sample of patients who had AD were performed. SETTING: Three university medical centers. PATIENTS: Patients with mild-to-moderate AD. INTERVENTION: Two consecutive cohorts of patients received 2 to 7 infusions of intravenous gantenerumab (60 or 200 mg) or placebo every 4 weeks. Brain slices from patients who had AD were coincubated with gantenerumab at increasing concentrations and with human microglial cells. MAIN OUTCOME MEASURES: Percent change in the ratio of regional carbon 11-labeled Pittsburgh Compound B retention in vivo and semiquantitative assessment of gantenerumab-induced phagocytosis ex vivo. RESULTS: Sixteen patients with end-of-treatment positron emission tomographic scans were included in the analysis. The mean (95% CI) percent change from baseline difference relative to placebo (n = 4) in cortical brain amyloid level was -15.6% (95% CI, -42.7 to 11.6) for the 60-mg group (n = 6) and -35.7% (95% CI, -63.5 to -7.9) for the 200-mg group (n = 6). Two patients in the 200-mg group showed transient and focal areas of inflammation or vasogenic edema on magnetic resonance imaging scans at sites with the highest level of amyloid reduction. Gantenerumab induced phagocytosis of human amyloid in a dose-dependent manner ex vivo. CONCLUSION: Gantenerumab treatment resulted in a dose-dependent reduction in brain amyloid level, possibly through an effector cell-mediated mechanism of action.

Asymmetry of SPECT perfusion image patterns as a diagnostic feature for Alzheimer's disease.

In this paper we propose a new diagnostic feature for Alzheimer's Disease (AD) which is based on assessment of the degree of inter-hemispheric asymmetry using Single Photon Emission Computed Tomography (SPECT). The asymmetry measure used represents differences in 3D perfusion image patterns in the cerebral hemispheres. We start from the simplest descriptors of brain perfusion such as the mean intensity within pairs of brain lobes, gradually increasing the resolution up to five-dimensional co-occurrence matrices. Evaluation of the method was performed using SPECT scans of 79 subjects including 42 patients with clinical diagnosis of AD and 37 controls. It was found that combination of intensity and gradient features in co-occurrence matrices captures significant differences in asymmetry values between AD and normal controls (p < 0.00003 for all cerebral lobes). Our results suggest that the asymmetry feature is useful for discriminating AD patients from normal controls as detected by SPECT.

A new approach for improving diagnostic accuracy in Alzheimer's disease and frontal lobe dementia utilising the intrinsic properties of the SPET dataset.

Alzheimer's disease (AD) and frontal lobe dementia (FLD) show characteristic patterns of regional cerebral blood flow (rCBF). However, these patterns may overlap with those observed in the aging brain in elderly normal individuals. The aim of this study was to develop a new method for better classification and recognition of AD and FLD cases as compared with normal controls. Forty-six patients with AD, 7 patients with FLD and 34 normal controls (CTR) were included in the study. rCBF was assessed by technetium-99m hexamethylpropylene amine oxime and a three-headed single-photon emission tomography (SPET) camera. A brain atlas was used to define volumes of interest (VOIs) corresponding to the brain lobes. In addition to conventional image processing methods, based on count density/voxel, the new approach also analysed other intrinsic properties of the data by means of gradient computation steps. Hereby, five factors were assessed and tested separately: the mean count density/voxel and its histogram, the mean gradient and its histogram, and the gradient angle co-occurrence matrix. A feature vector concatenating single features was also created and tested. Preliminary feature discrimination was performed using a two-sided t-test and a K-means clustering was then used to classify the image sets into categories. Finally, five-dimensional co-occurrence matrices combining the different intrinsic properties were computed for each VOI, and their ability to recognise the group to which each individual scan belonged was investigated. For correct classification of the AD-CTR groups, the gradient histogram in the parieto-temporal lobes was the most useful single feature (accuracy 91%). FLD and CTR were better classified by the count density/voxel histogram (frontal and occipital lobes) and by the mean gradient (frontal, temporal and parietal lobes, accuracy 98%). For AD and FLD the count density/voxel histogram in the frontal, parietal and occipital lobes classified the groups with the highest accuracy (85%). The concatenated joint feature correctly classified 96% of the AD-CTR, 98% of the FLD-CTR and 94% of the AD-FLD cases. 5D co-occurrence matrices correctly recognised 98% of the AD-CTR cases, 100% of the FLD-CTR cases and 98% of the AD-FLD cases. The proposed approach classified and diagnosed AD and FLD patients with higher accuracy than conventional analytical methods used for rCBF-SPET. This was achieved by extracting from the SPET data the intrinsic information content in each of the selected VOIs.