Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease

Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom.
PLoS ONE (Impact Factor: 3.23). 10/2011; 6(10):e25446. DOI: 10.1371/journal.pone.0025446
Source: PubMed

ABSTRACT The role of structural brain magnetic resonance imaging (MRI) is becoming more and more emphasized in the early diagnostics of Alzheimer's disease (AD). This study aimed to assess the improvement in classification accuracy that can be achieved by combining features from different structural MRI analysis techniques. Automatically estimated MR features used are hippocampal volume, tensor-based morphometry, cortical thickness and a novel technique based on manifold learning. Baseline MRIs acquired from all 834 subjects (231 healthy controls (HC), 238 stable mild cognitive impairment (S-MCI), 167 MCI to AD progressors (P-MCI), 198 AD) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were used for evaluation. We compared the classification accuracy achieved with linear discriminant analysis (LDA) and support vector machines (SVM). The best results achieved with individual features are 90% sensitivity and 84% specificity (HC/AD classification), 64%/66% (S-MCI/P-MCI) and 82%/76% (HC/P-MCI) with the LDA classifier. The combination of all features improved these results to 93% sensitivity and 85% specificity (HC/AD), 67%/69% (S-MCI/P-MCI) and 86%/82% (HC/P-MCI). Compared with previously published results in the ADNI database using individual MR-based features, the presented results show that a comprehensive analysis of MRI images combining multiple features improves classification accuracy and predictive power in detecting early AD. The most stable and reliable classification was achieved when combining all available features.

1 Follower
20 Reads
  • Source
    • ") were used , which is a common practice in studies of computer - aided diagnosis methods ( Cuingnet et al . , 2011 ; Klöppel et al . , 2008 ; Falahati et al . , 2014 ; Davatzikos et al . , 2008a ; Duchesne et al . , 2008 ; Fan et al . , 2008a , b ; Gray et al . , 2013 ; Koikkalainen et al . , 2012 ; Magnin et al . , 2009 ; Vemuri et al . , 2008 ; Wolz et al . , 2011 ) . Ground truth diagnosis of dementia can only be assessed using autopsy and is therefore only rarely available . Of the previously mentioned pa - pers , only one paper included one group of 20 AD patients with an au - topsy confirmed diagnosis ( Klöppel et al . , 2008 ) . Amyloid imaging ( Klunk et al . , 2004 ) has also proven to be "
    [Show abstract] [Hide abstract]
    ABSTRACT: Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi- center data set. Using clinical practice as starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer’s disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with in total 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer’s Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework:
    NeuroImage 05/2015; DOI:10.1016/j.neuroimage.2015.01.048 · 6.36 Impact Factor
  • Source
    • "Relatively recent literature has shown that pattern analysis methods are powerful diagnostic and predictive tools (Aksu et al., 2011; Costafreda et al., 2011; Davatzikos et al., 2009; Dickerson and Wolk, 2012; Duchesne et al., 2008; Hinrichs et al., 2009; Kloppel et al., 2008; Liu et al., 2004; McEvoy et al., 2009, 2011; Plant et al., 2010; Vemuri et al., 2009b; Wolz et al., 2011). One such index, the SPARE-AD score, calculated using a pattern classification method described in (Davatzikos et al., 2009; Fan et al., 2007), has been previously determined to be a good predictor of MCI to AD conversion (Misra et al., 2009) as well as of conversion from CN to MCI in healthy older adults (Davatzikos et al., 2008b, 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This study evaluates the individual, as well as relative and joint value of indices obtained from magnetic resonance imaging (MRI) patterns of brain atrophy (quantified by the SPARE-AD index), cerebrospinal fluid (CSF) biomarkers, APOE genotype, and cognitive performance (ADAS-Cog) in progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a variable follow-up period up to 6 years, using data from the Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1). SPARE-AD was first established as a highly sensitive and specific MRI-marker of AD vs. cognitively normal (CN) subjects (AUC = 0.98). Baseline predictive values of all aforementioned indices were then compared using survival analysis on 381 MCI subjects. SPARE-AD and ADAS-Cog were found to have similar predictive value, and their combination was significantly better than their individual performance. APOE genotype did not significantly improve prediction, although the combination of SPARE-AD, ADAS-Cog and APOE ε4 provided the highest hazard ratio estimates of 17.8 (last vs. first quartile). In a subset of 192 MCI patients who also had CSF biomarkers, the addition of Aβ1–42, t-tau, and p-tau181p to the previous model did not improve predictive value significantly over SPARE-AD and ADAS-Cog combined. Importantly, in amyloid-negative patients with MCI, SPARE-AD had high predictive power of clinical progression. Our findings suggest that SPARE-AD and ADAS-Cog in combination offer the highest predictive power of conversion from MCI to AD, which is improved, albeit not significantly, by APOE genotype. The finding that SPARE-AD in amyloid-negative MCI patients was predictive of clinical progression is not expected under the amyloid hypothesis and merits further investigation.
    Clinical neuroimaging 12/2014; 4:164–173. DOI:10.1016/j.nicl.2013.11.010 · 2.53 Impact Factor
  • Source
    • "This was studied on a patient group consisting of presenile (disease onset <65 years), early stage dementia patients suspected of AD or FTD and a matched control group (Group I). For comparison of the structural-MRI-based classifications with previous work [e.g., Cuingnet et al., 2011; Davatzikos et al., 2008; Duchesne et al., 2008; Fan et al., 2008a, b; Kl€ oppel et al., 2008; Koikkalainen et al., 2012; Magnin et al., 2009; Vemuri et al., 2008; Wolz et al., 2011], we also included a reference dataset from the AD neuroimaging initiative (ADNI) database (Group II). We evaluated several linear support vector machine (SVM) classification methods. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 − 91%) than all other approaches (AUC = 57 − 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 09/2014; 35(9). DOI:10.1002/hbm.22522 · 5.97 Impact Factor
Show more