Verification of predicted robustness and accuracy of multivariate analysis
School of Cancer and Enabling Sciences, University of Manchester/MAHSC, Wolfson Molecular Imaging Centre, Manchester, England, UK. NeuroImage
(Impact Factor: 6.36).
02/2011; 56(3):1382-5. DOI: 10.1016/j.neuroimage.2011.02.036
The assessment of accuracy and robustness of multivariate analysis of FDG-PET brain images as presented in [Markiewicz, P.J., Matthews, J.C., Declerck, J., Herholz, K., 2009. Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease. Neuroimage 46, 472-485.] using a homogeneous sample (from one centre) of small size is here verified using a heterogeneous sample (from multiple centres) of much larger size. Originally the analysis, which included principal component analysis (PCA) and Fisher discriminant analysis (FDA), was established using a sample of 42 subjects (19 Normal Controls (NCs) and 23 Alzheimer's disease (AD) patients) and here the analysis is verified using an independent sample of 166 subjects (86 NCs and 80 ADs) obtained from the ADNI database. It is shown that bootstrap resampling combined with the metric of the largest principal angle between PCA subspaces as well as the deliberate clinical misdiagnosis simulation can predict robustness of the multivariate analysis when used with new datasets. Cross-validation (CV) and the .632 bootstrap overestimated the predictive accuracy encouraging less robust solutions. Also, it is shown that the type of PET scanner and image reconstruction method has an impact on such analysis and affects the accuracy of the verification sample.
Available from: pubs.rsna.org
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ABSTRACT: The diagnosis of dementia syndromes can be challenging for clinicians, particularly in the early stages of disease. Patients with higher education levels may experience a marked decline in cognitive function before their dementia is detectable with routine testing methods. In addition, comorbid conditions (eg, depression) and the use of certain medications can confound the clinical assessment. Clinicians require a high degree of certainty before making a diagnosis of Alzheimer disease or some other neurodegenerative disorder, since the impact on patients and their families can be devastating. Moreover, accurate diagnosis is important because emerging therapeutic regimens vary depending on the cause of the dementia. Clinically based testing is useful; however, the results usually do not enable the clinician to make a definitive diagnosis. For this reason, imaging biomarkers are playing an increasingly important role in the workup of patients with suspected dementia. Positron emission tomography with 2-[fluorine-18]fluoro-2-deoxy-D-glucose allows detection of neurodegenerative disorders earlier than is otherwise possible. Accurate interpretation of these studies requires recognition of typical metabolic patterns caused by dementias and of artifacts introduced by image processing. Although visual interpretation is a vital component of image analysis, computer-assisted diagnostic software has been shown to increase diagnostic accuracy. ©RSNA, 2014.
Radiographics 05/2014; 34(3):684-701. DOI:10.1148/rg.343135065 · 2.60 Impact Factor
Available from: Holly D Soares
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ABSTRACT: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
Published by Elsevier Inc.
Alzheimer's & dementia: the journal of the Alzheimer's Association 10/2011; 8(1 Suppl):S1-68. DOI:10.1016/j.jalz.2011.09.172 · 12.41 Impact Factor
Principal Component Analysis - Multidisciplinary Applications, 02/2012; , ISBN: 978-953-51-0129-1
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