Automatic temporal lobe atrophy assessment in prodromal AD: Data from the DESCRIPA study

Chincarini, A.; Bosco, Paolo; Gemme, G.; Espósito, Mario; Rei, L.; Squarcia, S.; Bellotti, R.; Minthon, Lennart; Frisoni, Giovanni B.; Scheltens, Philip; Frölich, Lutz; Visser, Pieter Jelle; Nobili, Flavio

doi:10.1016/j.jalz.2013.05.1774

Cited by 19 publications

(14 citation statements)

References 41 publications

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“…Thus, the present series of MCI converter patients is the largest ever assessed with the PALZ score. A similar phenomenon has been verified for an MRI automatic analysis method measuring temporal atrophy that was less accurate in distinguishing between MCI converters and non-converters when the tool was trained in AD-dementia patients versus controls rather than in MCI converters versus non-converters [36].…”

Section: Discussionsupporting

confidence: 52%

Visual Versus Semi-Quantitative Analysis of 18F-FDG-PET in Amnestic MCI: An European Alzheimer's Disease Consortium (EADC) Project

Morbelli

Brugnolo

Bossert

et al. 2015

JAD

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We aimed to investigate the accuracy of FDG-PET to detect the Alzheimer's disease (AD) brain glucose hypometabolic pattern in 142 patients with amnestic mild cognitive impairment (aMCI) and 109 healthy controls. aMCI patients were followed for at least two years or until conversion to dementia. Images were evaluated by means of visual read by either moderately-skilled or expert readers, and by means of a summary metric of AD-like hypometabolism (PALZ score). Seventy-seven patients converted to AD-dementia after 28.6 ± 19.3 months of follow-up. Expert reading was the most accurate tool to detect these MCI converters from healthy controls (sensitivity 89.6%, specificity 89.0%, accuracy 89.2%) while two moderately-skilled readers were less (p < 0.05) specific (sensitivity 85.7%, specificity 79.8%, accuracy 82.3%) and PALZ score was less (p < 0.001) sensitive (sensitivity 62.3%, specificity 91.7%, accuracy 79.6%). Among the remaining 67 aMCI patients, 50 were confirmed as aMCI after an average of 42.3 months, 12 developed other dementia, and 3 reverted to normalcy. In 30/50 persistent MCI patients, the expert recognized the AD hypometabolic pattern. In 13/50 aMCI, both the expert and PALZ score were negative while in 7/50, only the PALZ score was positive due to sparse hypometabolic clusters mainly in frontal lobes. Visual FDG-PET reads by an expert is the most accurate method but an automated, validated system may be particularly helpful to moderately-skilled readers because of high specificity, and should be mandatory when even a moderately-skilled reader is unavailable.

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Section: Discussionsupporting

confidence: 52%

Visual Versus Semi-Quantitative Analysis of 18F-FDG-PET in Amnestic MCI: An European Alzheimer's Disease Consortium (EADC) Project

Morbelli

Brugnolo

Bossert

et al. 2015

JAD

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“…Furthermore, using an automated temporal lobe atrophy assessment, Chincarini et al. [14] were able to achieve an AUC of 0.81 when classifying MCI-NC from MCI-C. We show in our study that using HOC alone achieved an AUC of 0.655. The lower AUC and accuracy may be related to the heterogeneity of our MCI group, which contained participants with MCI-C showing conversion at any time point at 12, 24, and 36 months.…”

Section: Discussionsupporting

confidence: 52%

“…For instance, MRI measurements of volume reduction in multiple brain regions and in cortical thickness have been predictive of those who will convert from MCI to AD [8] , [9] , [10] , [11] , [12] . More specifically, the hippocampus has been extensively studied and hippocampal volume and shape have predictive value in conversion [12] , [13] , [14] . Similar predictions for conversion have also been seen in other imaging modalities including diffusion tensor imaging [15] , magnetic resonance spectroscopy [16] , fluorodeoxyglucose positron emission tomography [12] , [17] , amyloid imaging [18] , [19] , and positron emission tomography acetylcholinesterase activity [20] .…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's disease: 3‐Dimensional MRI texture for prediction of conversion from mild cognitive impairment

Luk

Ishaque

Khan

et al. 2018

Alz & Dem Diag Ass & Dis Mo

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Introduction Currently, there are no tools that can accurately predict which patients with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD). Texture analysis uses image processing and statistical methods to identify patterns in voxel intensities that cannot be appreciated by visual inspection. Our main objective was to determine whether MRI texture could be used to predict conversion of MCI to AD. Methods A method of 3-dimensional, whole-brain texture analysis was used to compute texture features from T1-weighted MR images. To assess predictive value, texture changes were compared between MCI converters and nonconverters over a 3-year observation period. A predictive model using texture and clinical factors was used to predict conversion of patients with MCI to AD. This model was then tested on ten randomly selected test groups from the data set. Results Texture features were found to be significantly different between normal controls (n = 225), patients with MCI (n = 382), and patients with AD (n = 183). A subset of the patients with MCI were used to compare between MCI converters (n = 98) and nonconverters (n = 106). A composite model including texture features, APOE -ε4 genotype, Mini-Mental Status Examination score, sex, and hippocampal occupancy resulted in an area under curve of 0.905. Application of the composite model to ten randomly selected test groups (nonconverters = 26, converters = 24) predicted MCI conversion with a mean accuracy of 76.2%. Discussion Early texture changes are detected in patients with MCI who eventually progress to AD dementia. Therefore, whole-brain 3D texture analysis has the potential to predict progression of patients with MCI to AD.

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“…Over the last decade, classification methods based on imaging have been increasingly integrated to identify the imaging signature of AD [ 201 – 203 ], offering promising tools for individualized diagnoses and prognostic predictions. However, until recently, neuroimaging-based studies for classifying SCD have been scarce [ 111 , 126 , 135 , 204 – 206 ]. The early identification of SCD and the prediction of disease progression at the individual level is important for timely interventions.…”

Section: Shortcomings and Emerging Trendsmentioning

confidence: 99%

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

Wang

Huang

et al. 2020

Mol Neurodegeneration

145

135

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Subjective cognitive decline (SCD) is regarded as the first clinical manifestation in the Alzheimer’s disease (AD) continuum. Investigating populations with SCD is important for understanding the early pathological mechanisms of AD and identifying SCD-related biomarkers, which are critical for the early detection of AD. With the advent of advanced neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), accumulating evidence has revealed structural and functional brain alterations related to the symptoms of SCD. In this review, we summarize the main imaging features and key findings regarding SCD related to AD, from local and regional data to connectivity-based imaging measures, with the aim of delineating a multimodal imaging signature of SCD due to AD. Additionally, the interaction of SCD with other risk factors for dementia due to AD, such as age and the Apolipoprotein E (ApoE) ɛ4 status, has also been described. Finally, the possible explanations for the inconsistent and heterogeneous neuroimaging findings observed in individuals with SCD are discussed, along with future directions. Overall, the literature reveals a preferential vulnerability of AD signature regions in SCD in the context of AD, supporting the notion that individuals with SCD share a similar pattern of brain alterations with patients with mild cognitive impairment (MCI) and dementia due to AD. We conclude that these neuroimaging techniques, particularly multimodal neuroimaging techniques, have great potential for identifying the underlying pathological alterations associated with SCD. More longitudinal studies with larger sample sizes combined with more advanced imaging modeling approaches such as artificial intelligence are still warranted to establish their clinical utility.

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Automatic temporal lobe atrophy assessment in prodromal AD: Data from the DESCRIPA study

Abstract: Automatic magnetic resonance imaging analysis may assist clinical classification of subjects in a memory clinic setting even when images are not specifically acquired for automatic analysis.

Cited by 19 publications

References 41 publications

Visual Versus Semi-Quantitative Analysis of 18F-FDG-PET in Amnestic MCI: An European Alzheimer's Disease Consortium (EADC) Project

Visual Versus Semi-Quantitative Analysis of 18F-FDG-PET in Amnestic MCI: An European Alzheimer's Disease Consortium (EADC) Project

Alzheimer's disease: 3‐Dimensional MRI texture for prediction of conversion from mild cognitive impairment

Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer’s disease

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