Amyloid PET Screening for Enrichment of Early-Stage Alzheimer Disease Clinical Trials: Experience in a Phase 1b Clinical Trial.

Amyloid positron emission tomography (PET) imaging is being investigated as a screening tool to identify amyloid-positive patients as an enrichment strategy for Alzheimer disease (AD) clinical trial enrollment. In a multicenter, phase 1b trial, patients meeting clinical criteria for prodromal or mild AD underwent florbetapir PET scanning at screening. PET, magnetic resonance imaging, and coregistered PET/magnetic resonance imaging scans were reviewed by 2 independent readers and binary visual readings tabulated. Semiquantitative values of cortical to whole cerebellar standard uptake value ratios were computed (threshold 1.10). Of 278 patients with an evaluable PET scan, 170 (61%) and 185 (67%) were amyloid-positive by visual reading and quantitative analysis, respectively; 39% were excluded from the study due to an amyloid-negative scan based on visual readings. More ApoE ε4 carriers than noncarriers were amyloid-positive (80% vs. 43%). Comparison of visual readings with quantitative results identified 21 discordant cases (92% agreement). Interreader and intrareader agreements from visual readings were 98% and 100%, respectively. Amyloid PET imaging is an effective and feasible screening tool for enrollment of amyloid-positive patients with early stages of AD into clinical trials.

In vivo evidence of glutamate toxicity in multiple sclerosis.

OBJECTIVE: There is increasing evidence that altered glutamate (Glu) homeostasis is involved in the pathophysiology of multiple sclerosis (MS). The aim of this study was to evaluate the in vivo effects of excess brain Glu on neuroaxonal integrity measured by N-acetylaspartate (NAA), brain volume, and clinical outcomes in a large, prospectively followed cohort of MS subjects. METHODS: We used multivoxel spectroscopy at 3T to longitudinally estimate Glu and NAA concentrations from large areas of normal-appearing white and gray matter (NAWM and GM) in MS patients (n = 343) with a mean follow-up time of 5 years. Using linear mixed-effects models, Glu was examined as a predictor of NAA decline, annualized percentage brain volume change, and evolution of clinical outcomes (Multiple Sclerosis Functional Composite [MSFC], Paced Auditory Serial Addition Test-3 [PASAT], and Expanded Disability Status Scale). Glu/NAA ratio was tested as a predictor of brain volume loss and clinical outcomes. RESULTS: Baseline Glu[NAWM] was predictive of accelerated longitudinal decline in NAA[GM] (-0.06mM change in NAA[GM] /yr for each unit increase in Glu; p = 0.004). The sustained elevation of Glu[NAWM] was predictive of a loss of 0.28mM/yr in NAA[NAWM] (p < 0.001) and 0.15mM/yr in NAA[GM] (p = 0.056). Each 10% increase in Glu/NAA[NAWM] was associated with a loss of 0.33% brain volume/yr (p = 0.001), 0.009 standard deviations/yr in MSFC z-score (p < 0.001), and 0.17 points/yr on the PASAT (p < 0.001). INTERPRETATION: These results indicate that higher Glu concentrations increase the rate of NAA decline, and higher Glu/NAA[NAWM] ratio increases the rate of decline of brain volume, MSFC, and PASAT. This provides evidence of a relationship between brain Glu and markers of disease progression in MS.

Evaluation of 18F-flutemetamol amyloid PET image analysis parameters on the effect of verubecestat on brain amlyoid load in Alzheimer's disease.

PURPOSE: The BACE inhibitor verubecestat was previously found to reduce amyloid load as assessed by 18F-flutemetamol positron emission tomography (PET) composite cortical standard uptake value ratio (SUVr) in patients with mild-to-moderate Alzheimer's disease (AD) in a substudy of the EPOCH trial. Here, we report on additional analyses relevant to the EPOCH PET data, to help inform on the use of PET for assessing amlyloid load in AD clinical trials. PROCEDURES: The analyses addressed (1) identification of an optimal 18F-flutemetamol reference region, (2) determination of the threshold to characterize the magnitude of the longitudinal change, and (3) the impact of partial volume correction (PVC). Pons and subcortical white matter were evaluated as reference regions. The SUVr cutoffs and final reference region choice were determined using 162 18F-flutemetamol PET scans from the AIBL dataset. 18F-flutemetamol SUVrs were computed at baseline and at Week 78 in EPOCH participants who received verubecestat 12 mg (n = 14), 40 mg (n = 20), or placebo (n = 20). Drug effects on amyloid load were computed using either Meltzer (MZ), or symmetric geometric transfer matrix (SGTM) PVC and compared to uncorrected data. RESULTS: The optimal subcortical white matter and pons SUVr cutoffs were determined to be 0.69 and 0.62, respectively. The effect size to detect longitudinal change was higher for subcortical white matter (1.20) than pons (0.45). Hence, subcortical white matter was used as the reference region for the EPOCH PET substudy. In EPOCH, uncorrected baseline SUVr values correlated strongly with MZ PVC (r2 = 0.94) and SGTM PVC (r2 = 0.92) baseline SUVr values, and PVC did not provide improvement for evaluating treatment effects on amyloid load at Week 78. No change from baseline was observed in the placebo group at Week 78, whereas a 0.02 and a 0.04 decrease in SUVr were observed in the 12 mg and 40 mg arms, with the latter representing a 22% reduction in the amyloid load above the detection threshold. CONCLUSIONS: Treatment-related 18F-flutemetamol longitudinal changes in AD clinical trials can be quantified using a subcortical white matter reference region without PVC. CLINICAL TRIAL REGISTRATION: clinicaltrials.gov NCT01739348.

Regional areas and widths of the midsagittal corpus callosum among HIV-infected patients on stable antiretroviral therapies.

Recent reports suggest that a growing number of human immunodeficiency virus (HIV)-infected persons show signs of persistent cognitive impairment even in the context of combination antiretroviral therapies (cART). The basis for this finding remains poorly understood as there are only a limited number of studies examining the relationship between CNS injury, measures of disease severity, and cognitive function in the setting of stable disease. This study examined the effects of HIV infection on cerebral white matter using quantitative morphometry of the midsagittal corpus callosum (CC) in 216 chronically infected participants from the multisite HIV Neuroimaging Consortium study currently receiving cART and 139 controls. All participants underwent MRI assessment, and HIV-infected subjects also underwent measures of cognitive function and disease severity. The midsagittal slice of the CC was quantified using two semi-automated procedures. Group comparisons were accomplished using ANOVA, and the relationship between CC morphometry and clinical covariates (current CD4, nadir CD4, plasma and CSF HIV RNA, duration of HIV infection, age, and ADC stage) was assessed using linear regression models. HIV-infected patients showed significant reductions in both the area and linear widths for several regions of the CC. Significant relationships were found with ADC stage and nadir CD4 cell count, but no other clinical variables. Despite effective treatment, significant and possibly irreversible structural loss of the white matter persists in the setting of chronic HIV disease. A history of advanced immune suppression is a strong predictor of this complication and suggests that antiretroviral intervention at earlier stages of infection may be warranted.

Disease modeling in multiple sclerosis: assessment and quantification of sources of variability in brain parenchymal fraction measurements.

The measurement of brain atrophy from magnetic resonance imaging (MRI) has become an established method of estimating disease severity and progression in multiple sclerosis (MS). Most commonly reported in the form of brain parenchymal fraction (BPF), it is more sensitive to the degenerative component of the disease and shows progression more reliably than lesion burden. Typically, the reliability of BPF and other morphometric measurements is assessed by evaluating scan-rescan experiments. While these experiments provide good estimates of real-life error related to imperfect patient repositioning in the MRI scanner, measurement variance due to physiological and reversible pathological fluctuations in brain volume are not taken into account. In this work, we propose a new model for estimating variability in serial morphometry, particularly the BPF measurement. Specifically, we attempt to detect and explicitly model the remaining sources of error to more accurately describe the overall variability in BPF measurements. Our results show that sources of variability beyond subject repositioning error are important and cannot be ignored. We demonstrate that scan-rescan experiments only provide a lower bound on the true error in repeated measurements of patients' BPF. We have estimated the variance due to patient repositioning during scan-rescan (sigma(sr)(2) = 3.0e-06), variance assigned to physiological fluctuations (sigma(p)(2) = 5.74e-06) and the variance associated with lesion activity (sigma(les)(2) = 1.09e-05). These variance components can be used to determine the relative impact of their sources on sample size estimates for studies investigating change over time in MS patients. Our results demonstrate that sample size calculations based exclusively on scan-rescan variability (sigma(sr)) are likely to underestimate the number of patients required. If the physiological variability (sigma(p)) is incorporated in sample size calculations, the required sample size would increase by a factor of 5.69 based on standard t-test sample size calculation.

Unbiased treatment effect estimates by modeling the disease process of multiple sclerosis.

Gadolinium-enhancing lesions in the brain are commonly used as a primary outcome measure of disease activity in phase I/II clinical trials in multiple sclerosis (MS). The advent of effective therapy and the cost of clinical trials have led some researchers to adopt a one-arm study design with selection towards patients showing MRI activity. Regression to the mean is recognized as an important consideration in these trials, but the additional confounding effect of alternating active and inactive phases of disease has not been considered. Simulated data were generated from Poisson and normal distributions to mimic outcomes from phase I/II clinical trials of patients with relapsing-remitting MS under a constant or changing disease process model. In all cases, conventional comparison of pretreatment to on-treatment measurements overestimated the treatment effect. Although correction for regression to the mean provided unbiased estimates of the treatment effect under a constant disease process model, this correction also overestimated the treatment effect when disease activity changed over time. Conversely, unbiased estimates of the treatment effect under an alternating (active/inactive) disease process were obtained by correctly accounting for regression to the mean and the disease process. The implications of these results are discussed in terms of efficacy and safety.

A model-based framework for the detection of spiculated masses on mammography.

The detection of lesions on mammography is a repetitive and fatiguing task. Thus, computer-aided detection systems have been developed to aid radiologists. The detection accuracy of current systems is much higher for clusters of microcalcifications than for spiculated masses. In this article, the authors present a new model-based framework for the detection of spiculated masses. The authors have invented a new class of linear filters, spiculated lesion filters, for the detection of converging lines or spiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures of spiculated masses. As a part of this algorithm, the authors have also invented a novel technique to enhance spicules on mammograms. This entails filtering in the radon domain. They have also developed models to reduce the false positives due to normal linear structures. A key contribution of this work is that the parameters of the detection algorithm are based on measurements of physical properties of spiculated masses. The results of the detection algorithm are presented in the form of free-response receiver operating characteristic curves on images from the Mammographic Image Analysis Society and Digital Database for Screening Mammography databases.

Comparison of algorithms to enhance spicules of spiculated masses on mammography.

We have developed an algorithm for enhancement of spicules of spiculated masses, which uses the discrete radon transform. Previously, we employed a commonly used method to compute the discrete radon transform, which we refer to as the DRT. Recently, a new, more exact method to compute the discrete radon transform was developed by Averbuch et al, which is called the fast slant stack (FSS) method. Our hypothesis was that this new formulation would help to improve our enhancement algorithm. To test this idea, we conducted multiple two-alternative-forced-choice observer studies and found that most observers preferred the enhanced images generated with the FSS method.

Early CNS neurodegeneration in radiologically isolated syndrome.

OBJECTIVE: Increasing evidence indicates that the thalamus may be a location of early neurodegeneration in multiple sclerosis (MS). Our objective was to identify the presence of gray matter volume loss and thinning in patients with radiologically isolated syndrome (RIS). METHODS: Sixty-three participants were included in this case-control study. Twenty-one patients with RIS were age- and sex-matched to 42 healthy controls in a 1:2 ratio. All participants underwent brain MRIs on a single 3T scanner. After lesion segmentation and inpainting, 1 mm(3)-isometric T1-weighted images were submitted to FreeSurfer (v5.2). Normalized cortical and deep gray matter volumes were compared between patients with RIS and controls using t tests, and thalamic volumes were correlated with white matter lesion volumes using Pearson correlation. Exploratory cortical thickness maps were created. RESULTS: Although traditional normalized total gray and white matter volumes were not statistically different between patients with RIS and controls, normalized left (0.0046 ± 0.0005 vs 0.0049 ± 0.0004, p = 0.006), right (0.0045 ± 0.0005 vs 0.0048 ± 0.0004, p = 0.008), and mean (0.0045 ± 0.0005 vs 0.0049 ± 0.0004, p = 0.004) thalamic volumes were significantly lower in patients with RIS (n = 21, mean age 41.9 ± 12.7 years) than in controls (n = 42, mean age 41.4 ± 11.2 years). Thalamic volumes correlated modestly with white matter lesion volumes (range: r = -0.35 to -0.47). CONCLUSION: Our data provide novel evidence of thalamic atrophy in RIS and are consistent with previous reports in early MS stages. Thalamic volume loss is present early in CNS demyelinating disease and should be further investigated as a metric associated with neurodegeneration.

Magnetic resonance spectroscopy markers of disease progression in multiple sclerosis.

IMPORTANCE: Predicting disease evolution is becoming essential for optimizing treatment decision making in multiple sclerosis (MS). Multiple sclerosis pathologic damage typically includes demyelination, neuro-axonal loss, and astrogliosis. OBJECTIVE: To evaluate the potential of magnetic resonance markers of central nervous system injury to predict brain-volume loss and clinical disability in multiple sclerosis. DESIGN, SETTING, AND PARTICIPANTS: Participants were selected from the Multiple Sclerosis Center at the University of California-San Francisco. The preliminary data set included 59 patients with MS and 43 healthy control individuals. The confirmatory data set included 220 patients from an independent, large genotype-phenotype research project. MAIN OUTCOMES AND MEASURES: Baseline N-acetylaspartate (NAA) level, myo-inositol (mI) in normal-appearing white and gray matter, myelin water fraction in normal-appearing white matter, markers of axonal damage, astrogliosis, and demyelination were evaluated as predictors in a preliminary data set. Potential predictors were subsequently tested for replication in a confirmatory data set. Clinical scores and percentage of brain-volume change were obtained annually over 4 years as outcomes. Predictors of outcomes were assessed using linear models, linear mixed-effects models, and logistic regression. RESULTS: N-acetylaspartate and mI both had statistically significant effects on brain volume, prompting the use of the mI:NAA ratio in normal-appearing white matter as a predictor. The ratio was a predictor of brain-volume change in both cohorts (annual slope in the percentage of brain-volume change/unit of increase in the ratio: -1.68; 95% CI, -3.05 to -0.30; P = .02 in the preliminary study cohort and -1.08; 95% CI, -1.95 to -0.20; P = .02 in the confirmatory study cohort). Furthermore, the mI:NAA ratio predicted clinical disability (Multiple Sclerosis Functional Composite evolution: -0.52 points annually, P < .001; Multiple Sclerosis Functional Composite sustained progression: odds ratio, 2.76/SD increase in the ratio; 95% CI, 1.32 to 6.47; P = .01) in the preliminary data set and predicted Multiple Sclerosis Functional Composite evolution (-0.23 points annually; P = .01), Expanded Disability Status Scale evolution (0.57 points annually; P = .04), and Expanded Disability Status Scale sustained progression (odds ratio, 1.46; 95% CI, 1.10 to 1.94; P = .009) in the confirmatory data set. Myelin water fraction did not show predictive value. CONCLUSIONS AND RELEVANCE: The mI:NAA ratio in normal-appearing white matter has consistent predictive power on brain atrophy and neurological disability evolution. The combined presence of astrogliosis and axonal damage in white matter has cardinal importance in disease severity.

Computer-aided diagnosis in breast magnetic resonance imaging.

In this paper, we review the role played by breast magnetic resonance imaging in the detection and diagnosis of breast cancer. This is followed by a discussion of clinical decision support systems in medicine and their contributions in breast magnetic resonance imaging interpretation. We conclude by discussing the future of computer-aided diagnosis in breast magnetic resonance imaging.

Wavelet-based adaptive denoising and baseline correction for MALDI TOF MS.

Proteomic profiling by MALDI TOF mass spectrometry (MS) is an effective method for identifying biomarkers from human serum/plasma, but the process is complicated by the presence of noise in the spectra. In MALDI TOF MS, the major noise source is chemical noise, which is defined as the interference from matrix material and its clusters. Because chemical noise is nonstationary and nonwhite, wavelet-based denoising is more effective than conventional noise reduction schemes based on Fourier analysis. However, current wavelet-based denoising methods for mass spectrometry do not fully consider the characteristics of chemical noise. In this article, we propose new wavelet-based high-frequency noise reduction and baseline correction methods that were designed based on the discrete stationary wavelet transform. The high-frequency noise reduction algorithm adaptively estimates the time-varying threshold for each frequency subband from multiple realizations of chemical noise and removes noise from mass spectra of samples using the estimated thresholds. The baseline correction algorithm computes the monotonically decreasing baseline in the highest approximation of the wavelet domain. The experimental results demonstrate that our algorithms effectively remove artifacts in mass spectra that are due to chemical noise while preserving informative features as compared to commonly used denoising methods.

Snakules: a model-based active contour algorithm for the annotation of spicules on mammography.

We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated mass location that has been identified by either a radiologist or a computer-aided detection (CADe) algorithm, we deploy snakules that are converging open-ended active contours also known as snakes. The set of convergent snakules have the ability to deform, grow and adapt to the true spicules in the image, by an attractive process of curve evolution and motion that optimizes the local matching energy. Starting from a natural set of automatically detected candidate points, snakules are deployed in the region around a suspect spiculated mass location. Statistics of prior physical measurements of spiculated masses on mammography are used in the process of detecting the set of candidate points. Observer studies with experienced radiologists to evaluate the performance of snakules demonstrate the potential of the algorithm as an image analysis technique to improve the specificity of CADe algorithms and as a CADe prompting tool.

Indexes for three-class classification performance assessment--an empirical comparison.

Assessment of classifier performance is critical for fair comparison of methods, including considering alternative models or parameters during system design. The assessment must not only provide meaningful data on the classifier efficacy, but it must do so in a concise and clear manner. For two-class classification problems, receiver operating characteristic analysis provides a clear and concise assessment methodology for reporting performance and comparing competing systems. However, many other important biomedical questions cannot be posed as "two-class" classification tasks and more than two classes are often necessary. While several methods have been proposed for assessing the performance of classifiers for such multiclass problems, none has been widely accepted. The purpose of this paper is to critically review methods that have been proposed for assessing multiclass classifiers. A number of these methods provide a classifier performance index called the volume under surface (VUS). Empirical comparisons are carried out using 4 three-class case studies, in which three popular classification techniques are evaluated with these methods. Since the same classifier was assessed using multiple performance indexes, it is possible to gain insight into the relative strengths and weakness of the measures. We conclude that: 1) the method proposed by Scurfield provides the most detailed description of classifier performance and insight about the sources of error in a given classification task and 2) the methods proposed by He and Nakas also have great practical utility as they provide both the VUS and an estimate of the variance of the VUS. These estimates can be used to statistically compare two classification algorithms.

Complex wavelet structural similarity: a new image similarity index.

We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.

Statistical characterization of chemical noise in MALDI TOF MS by wavelet analysis of multiple noise realizations.

Noise reduction is a critical step in proteomic profiling by mass spectrometry for identification of disease biomarkers. We propose a new method for characterizing chemical noise in MALDI TOF mass spectrometry using wavelet analysis of multiple noise realizations. The use of multiple measurements of the noise process enables a more reliable characterization than can be obtained from a single measurement. We analyzed the distributions and summary statistics of the wavelet coefficients.