A Cross-Validation of FDG- and Amyloid-PET Biomarkers in Mild Cognitive Impairment for the Risk Prediction to Dementia due to Alzheimer’s Disease in a Clinical Setting

Iaccarino, Leonardo; Chiotis, Konstantinos; Alongi, Pierpaolo; Almkvist, Ove; Wall, Anders; Cerami, Chiara; Bettinardi, Valentino; Gianolli, Luigi; Nordberg, Agneta; Perani, Daniela

doi:10.3233/jad-170158

Cited by 52 publications

(52 citation statements)

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“…For example, decreased metabolism detected using [ 18 F]FDG-PET has long been observed as an early change in AD (Minoshima et al, 1997;Reiman et al, 2004), and several studies have described its utility for predicting conversion to dementia (Choo et al, 2013;Iaccarino et al, 2017;Prestia et al, 2013). Generally, studies using ASL have been consistent with the [ 18 F]FDG-PET literature.…”

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confidence: 72%

A longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure

Staffaroni

Cobigo

Elahi

et al. 2019

Human Brain Mapping

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Cerebral perfusion declines across the lifespan and is altered in the early stages of several age‐related neuropathologies. Little is known, however, about the longitudinal evolution of perfusion in healthy older adults, particularly when perfusion is quantified using magnetic resonance imaging with arterial spin labeling (ASL). The objective was to characterize longitudinal perfusion in typically aging adults and elucidate associations with cognition and brain structure. Adults who were functionally intact at baseline (n = 161, ages 47–89) underwent ASL imaging to quantify whole‐brain gray matter perfusion; a subset (n = 136) had repeated imaging (average follow‐up: 2.3 years). Neuropsychological testing at each visit was summarized into executive function, memory, and processing speed composites. Global gray matter volume, white matter microstructure (mean diffusivity), and white matter hyperintensities were also quantified. We assessed baseline associations among perfusion, cognition, and brain structure using linear regression, and longitudinal relationships using linear mixed effects models. Greater baseline perfusion, particularly in the left dorsolateral prefrontal cortex and right thalamus, was associated with better executive functions. Greater whole‐brain perfusion loss was associated with worsening brain structure and declining processing speed. This study helps validate noninvasive MRI‐based perfusion imaging and underscores the importance of cerebral blood flow in cognitive aging.

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confidence: 72%

A longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure

Staffaroni

Cobigo

Elahi

et al. 2019

Human Brain Mapping

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“…Amyloid deposits are one of the main pathological findings in AD causing alterations in neurotransmission, activation of inflammatory mechanisms, neuronal death, and cerebral atrophy . While AD can currently be definitively confirmed by postmortem histopathologic examination, in vivo imaging as amyloid PET may improve the clinician's ability to identify AD and guide clinicians in the management of patients . Many PET imaging probes for AD‐specific pathological modifications have been developed and proved effective in detecting amyloid deposits in vivo .…”

Section: Discussionmentioning

confidence: 99%

“…1 While AD can currently be definitively confirmed by postmortem histopathologic examination, in vivo imaging as amyloid PET may improve the clinician's ability to identify AD and guide clinicians in the management of patients. 25 Many PET imaging probes for AD-specific pathological modifications have been developed and proved effective in detecting amyloid deposits in vivo. 26 Our study supports the use of amyloid PET with 18F-Florbetaben in the clinical setting by confirming its importance in the differential diagnosis of dementia.…”

Section: Discussionmentioning

confidence: 99%

18F‐Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification

Alongi

Sardina

Coppola

et al. 2019

Journal of Neuroimaging

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BACKGROUND AND PURPOSE While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD. METHODS Amyloid PET results using a new processing imaging algorithm (MRI‐Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave‐one‐out cross‐validation was carried out to validate the results. RESULTS Forty‐four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F‐FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p‐tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches. CONCLUSIONS Amyloid PET using SPM‐normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

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“…In [56], both FDG-PET and amyloid PET were assessed in a clinical setting to predict AD from MCI using a database of the Karolinska Institutet Stockholm, Sweden. Data were classified according to the diagnosis criteria.…”

Section: B Pet Modalitiesmentioning

confidence: 99%

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Ahmed

Zhang

Feng

et al. 2019

IEEE Rev. Biomed. Eng.

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Dementia, a chronic and progressive cognitive declination of brain function due to disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving the accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. Much of the success of these approaches can be attributed to the application of machine learning and deep learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the previous five years (2013-2018). Based on the rigorous review of the existing works, we have found that while most of the studies focused on Alzheimer's disease have demonstrated reasonable performance, the identification of other types of dementia remains a major challenge. Hybrid imaging analysis through deep learning approaches may hold promises for early diagnosis of these other types of dementia, and warrant further investigation in the future. The main contributions of this review paper are as follows: 1. Based on the detailed analysis of the existing literature, this paper discusses the most recent neuroimaging procedures for dementia diagnosis, and 2. It systematically explains the machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

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A Cross-Validation of FDG- and Amyloid-PET Biomarkers in Mild Cognitive Impairment for the Risk Prediction to Dementia due to Alzheimer’s Disease in a Clinical Setting

Cited by 52 publications

References 50 publications

A longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure

A longitudinal characterization of perfusion in the aging brain and associations with cognition and neural structure

18F‐Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

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