Regional cerebral blood flow single photon emission computed tomography for detection of Frontotemporal dementia in people with suspected dementia.

BACKGROUND: In the UK, dementia affects 5% of the population aged over 65 years and 25% of those over 85 years. Frontotemporal dementia (FTD) represents one subtype and is thought to account for up to 16% of all degenerative dementias. Although the core of the diagnostic process in dementia rests firmly on clinical and cognitive assessments, a wide range of investigations are available to aid diagnosis.Regional cerebral blood flow (rCBF) single-photon emission computed tomography (SPECT) is an established clinical tool that uses an intravenously injected radiolabelled tracer to map blood flow in the brain. In FTD the characteristic pattern seen is hypoperfusion of the frontal and anterior temporal lobes. This pattern of blood flow is different to patterns seen in other subtypes of dementia and so can be used to differentiate FTD.It has been proposed that a diagnosis of FTD, (particularly early stage), should be made not only on the basis of clinical criteria but using a combination of other diagnostic findings, including rCBF SPECT. However, more extensive testing comes at a financial cost, and with a potential risk to patient safety and comfort. OBJECTIVES: To determine the diagnostic accuracy of rCBF SPECT for diagnosing FTD in populations with suspected dementia in secondary/tertiary healthcare settings and in the differential diagnosis of FTD from other dementia subtypes. SEARCH METHODS: Our search strategy used two concepts: (a) the index test and (b) the condition of interest. We searched citation databases, including MEDLINE (Ovid SP), EMBASE (Ovid SP), BIOSIS (Ovid SP), Web of Science Core Collection (ISI Web of Science), PsycINFO (Ovid SP), CINAHL (EBSCOhost) and LILACS (Bireme), using structured search strategies appropriate for each database. In addition we searched specialised sources of diagnostic test accuracy studies and reviews including: MEDION (Universities of Maastricht and Leuven), DARE (Database of Abstracts of Reviews of Effects) and HTA (Health Technology Assessment) database.We requested a search of the Cochrane Register of Diagnostic Test Accuracy Studies and used the related articles feature in PubMed to search for additional studies. We tracked key studies in citation databases such as Science Citation Index and Scopus to ascertain any further relevant studies. We identified 'grey' literature, mainly in the form of conference abstracts, through the Web of Science Core Collection, including Conference Proceedings Citation Index and Embase. The most recent search for this review was run on the 1 June 2013.Following title and abstract screening of the search results, full-text papers were obtained for each potentially eligible study. These papers were then independently evaluated for inclusion or exclusion. SELECTION CRITERIA: We included both case-control and cohort (delayed verification of diagnosis) studies. Where studies used a case-control design we included all participants who had a clinical diagnosis of FTD or other dementia subtype using standard clinical diagnostic criteria. For cohort studies, we included studies where all participants with suspected dementia were administered rCBF SPECT at baseline. We excluded studies of participants from selected populations (e.g. post-stroke) and studies of participants with a secondary cause of cognitive impairment. DATA COLLECTION AND ANALYSIS: Two review authors extracted information on study characteristics and data for the assessment of methodological quality and the investigation of heterogeneity. We assessed the methodological quality of each study using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. We produced a narrative summary describing numbers of studies that were found to have high/low/unclear risk of bias as well as concerns regarding applicability. To produce 2 x 2 tables, we dichotomised the rCBF SPECT results (scan positive or negative for FTD) and cross-tabulated them against the results for the reference standard. These tables were then used to calculate the sensitivity and specificity of the index test. Meta-analysis was not performed due to the considerable between-study variation in clinical and methodological characteristics. MAIN RESULTS: Eleven studies (1117 participants) met our inclusion criteria. These consisted of six case-control studies, two retrospective cohort studies and three prospective cohort studies. Three studies used single-headed camera SPECT while the remaining eight used multiple-headed camera SPECT. Study design and methods varied widely. Overall, participant selection was not well described and the studies were judged as having either high or unclear risk of bias. Often the threshold used to define a positive SPECT result was not predefined and the results were reported with knowledge of the reference standard. Concerns regarding applicability of the studies to the review question were generally low across all three domains (participant selection, index test and reference standard).Sensitivities and specificities for differentiating FTD from non-FTD ranged from 0.73 to 1.00 and from 0.80 to 1.00, respectively, for the three multiple-headed camera studies. Sensitivities were lower for the two single-headed camera studies; one reported a sensitivity and specificity of 0.40 (95% confidence interval (CI) 0.05 to 0.85) and 0.95 (95% CI 0.90 to 0.98), respectively, and the other a sensitivity and specificity of 0.36 (95% CI 0.24 to 0.50) and 0.92 (95% CI 0.88 to 0.95), respectively.Eight of the 11 studies which used SPECT to differentiate FTD from Alzheimer's disease used multiple-headed camera SPECT. Of these studies, five used a case-control design and reported sensitivities of between 0.52 and 1.00, and specificities of between 0.41 and 0.86. The remaining three studies used a cohort design and reported sensitivities of between 0.73 and 1.00, and specificities of between 0.94 and 1.00. The three studies that used single-headed camera SPECT reported sensitivities of between 0.40 and 0.80, and specificities of between 0.61 and 0.97. AUTHORS' CONCLUSIONS: At present, we would not recommend the routine use of rCBF SPECT in clinical practice because there is insufficient evidence from the available literature to support this.Further research into the use of rCBF SPECT for differentiating FTD from other dementias is required. In particular, protocols should be standardised, study populations should be well described, the threshold for 'abnormal' scans predefined and clear details given on how scans are analysed. More prospective cohort studies that verify the presence or absence of FTD during a period of follow up should be undertaken

EANM-EAN recommendations for the use of brain 18 F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) in neurodegenerative cognitive impairment and dementia: Delphi consensus

BACKGROUND: Recommendations for using FDG-PET to support the diagnosis of dementing neurodegenerative disorders are sparse and poorly structured. METHODS: We defined 21 questions on diagnostic issues and on semi-automated analysis to assist visual reading. Literature was reviewed to assess study design, risk of bias, inconsistency, imprecision, indirectness and effect size. Critical outcomes were sensitivity, specificity, accuracy, positive/negative predictive value, area under the receiving operating characteristic curve, and positive/negative likelihood ratio of FDG-PET in detecting the target conditions. Using the Delphi method, an expert panel voted for/against the use of FDG-PET based on published evidence and expert opinion. RESULTS: Of the 1435 papers, 58 provided proper quantitative assessment of test performance. The panel agreed on recommending FDG-PET for 14 questions: diagnosing mild cognitive impairment due to Alzheimer's disease (AD), frontotemporal lobar degeneration (FTLD) or dementia with Lewy bodies (DLB); diagnosing atypical AD and pseudodementia; differentiating between AD and DLB, FTLD, or vascular dementia, between DLB and FTLD, and between Parkinson's disease (PD) and progressive supranuclear palsy; suggesting underlying pathophysiology in corticobasal degeneration and progressive primary aphasia, and cortical dysfunction in PD; using semi-automated assessment to assist visual reading. Panelists did not support FDG-PET use for preclinical stages of neurodegenerative disorders, for amyotrophic lateral sclerosis (ALS) and Huntington disease (HD) diagnoses, and ALS or HD-related cognitive decline. CONCLUSIONS: Despite limited formal evidence, panelists deemed FDG-PET useful in the early and differential diagnosis of the main neurodegenerative disorders, and semiautomated assessment helpful to assist visual reading. These decisions are proposed as interim recommendations. This article is protected by copyright. All rights reserved

MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia

INTRODUCTION: Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. METHODS: A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. RESULTS: Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. CONCLUSION: Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database

Contribution of FDG-PET and MRI to improve Understanding, Detection and Differentiation of Dementia

Progression and pattern of changes in different biomarkers of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) like [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and magnetic resonance imaging (MRI) have been carefully investigated over the past decades. However, there have been substantially less studies investigating the potential of combining these imaging modalities to make use of multimodal information to further improve understanding, detection and differentiation of various dementia syndromes. Further the role of preprocessing has been rarely addressed in previous research although different preprocessing algorithms have been shown to substantially affect diagnostic accuracy of dementia. In the present work common preprocessing procedures used to scale FDG-PET data were compared to each other. Further, FDG-PET and MRI information were jointly analyzed using univariate and multivariate techniques. The results suggest a highly differential effect of different scaling procedures of FDG-PET data onto detection and differentiation of various dementia syndromes. Additionally, it has been shown that combining multimodal information does further improve automatic detection and differentiation of AD and FTLD

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Neuroimaging of dementia in 2013: what radiologists need to know

The structural and functional neuroimaging of dementia have substantially evolved over the last few years. The most common forms of dementia, Alzheimer disease (AD), Lewy body dementia (LBD) and fronto-temporal lobar degeneration (FTLD), have distinct patterns of cortical atrophy and hypometabolism that evolve over time, as reviewed in the first part of this article. The second part discusses unspecific white matter alterations on T2-weighted and fluid-attenuated inversion recovery (FLAIR) images as well as cerebral microbleeds, which often occur during normal aging and may affect cognition. The third part summarises molecular neuroimaging biomarkers recently developed to visualise amyloid deposits, tau protein deposits and neurotransmitter systems. The fourth section reviews the utility of advanced image analysis techniques as predictive biomarkers of cognitive decline in individuals with early symptoms compatible with mild cognitive impairment (MCI). As only about half of MCI cases will progress to clinically overt dementia, whereas the other half remain stable or might even improve, the discrimination of stable versus progressive MCI is of paramount importance for both individual patient treatment and patient selection for clinical trials. The fifth and final part discusses the inter-individual variation in the neurocognitive reserve, which is a potential constraint for all proposed methods. Key Points • Many forms of dementia have spatial atrophy patterns detectable on neuroimaging. • Early treatment of dementia is beneficial, indicating the need for early diagnosis. • Advanced image analysis techniques detect subtle anomalies invisible on radiological evaluation. • Inter-individual variation explains variable cognitive impairment despite the same degree of atroph

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making