Computer-assisted system for diagnosis of Alzheimer disease using data base- independent estimation and fluorodeoxyglucose- positron-emission tomography and 3D-stereotactic surface projection

Usefulness of 3-dimensional stereotactic surface projection FDG PET images for the diagnosis of dementia

To compare diagnostic performance and confidence of a standard visual reading and combined 3-dimensional stereotactic surface projection (3D-SSP) results to discriminate between Alzheimer disease (AD)/mild cognitive impairment (MCI), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD).[F]fluorodeoxyglucose (FDG) PET brain images were obtained from 120 patients (64 AD/MCI, 38 DLB, and 18 FTD) who were clinically confirmed over 2 years follow-up. Three nuclear medicine physicians performed the diagnosis and rated diagnostic confidence twice; once by standard visual methods, and once by adding of 3D-SSP. Diagnostic performance and confidence were compared between the 2 methods.3D-SSP showed higher sensitivity, specificity, accuracy, positive, and negative predictive values to discriminate different types of dementia compared with the visual method alone, except for AD/MCI specificity and FTD sensitivity. Correction of misdiagnosis after adding 3D-SSP images was greatest for AD/MCI (56%), followed by DLB (13%) and FTD (11%). Diagnostic confidence also increased in DLB (visual: 3.2; 3D-SSP: 4.1; P < 0.001), followed by AD/MCI (visual: 3.1; 3D-SSP: 3.8; P = 0.002) and FTD (visual: 3.5; 3D-SSP: 4.2; P = 0.022). Overall, 154/360 (43%) cases had a corrected misdiagnosis or improved diagnostic confidence for the correct diagnosis.The addition of 3D-SSP images to visual analysis helped to discriminate different types of dementia in FDG PET scans, by correcting misdiagnoses and enhancing diagnostic confidence in the correct diagnosis. Improvement of diagnostic accuracy and confidence by 3D-SSP images might help to determine the cause of dementia and appropriate treatment.

Cognitive impairment before changes appear on [18F]-fluoro-D-glucose positron emission tomography images in a patient with possible early-stage cerebellar-predominant multiple system atrophy

Multiple system atrophy (MSA) is a sporadic, rapidly progressive neurodegenerative disorder characterized by autonomic dysfunction combined with parkinsonism or cerebellar ataxia. Patients with MSA typically suffer from cognitive disorders and rapid eye movement sleep behaviour disorder. ¹⁸ F-fluorodeoxyglucose-positron emission tomography is used to assess MSA. However, the relationship between the clinical features and findings on ¹⁸ F-fluorodeoxyglucose-positron emission tomography in patients with MSA has not yet been investigated. Here we report a case of possible early-stage cerebellar-type MSA. We concluded that cerebellar-type MSA or other factors, such as rapid eye movement sleep behaviour disorder or obstructive sleep apnoea cognitive impairment, could appear before changes are visible on ¹⁸ F-fluorodeoxyglucose-positron emission tomography images. Additionally, we concluded that the cognitive impairment could derive from cerebellar-type MSA itself, not from other factors such as rapid eye movement sleep behaviour disorder or sleep apnoea syndrome.

Imaging Brain Metabolism and Pathology in Alzheimer's Disease with Positron Emission Tomography

Current Positron Emission Tomography (PET) biomarkers for Alzheimer's disease (AD) assess either neuronal function, or associated pathological features of this common neurodegenerative disease. The most widely accepted clinical PET tool for AD is 18-fluorodeoxyglucose PET (FDG-PET), which measures cerebral metabolic glucose utilization rate (CMRglc). FDG-PET is a marker of synaptic activity, neuronal function, and neuronal metabolic activity. AD is characterized by a distinct pattern of hypometabolism, as seen with the FDG images. This pattern can show variability across different subjects and is present before a patient is demented, specifically in amnestic mild cognitive impairment a clinical diagnosis defined as an intermediate state from normal aging to dementia. In addition to FDG PET, novel PET approaches assess known pathological hallmarks of AD including extracellular amyloid-beta plaques (Aβ) and intracellular neurofibrillary tangles composed of tau fibrils. Already, amyloid PET imaging is a tool that allows in vivo imaging of extracellular beta-amyloid levels. Efforts to bring tau imaging into clinical use continue, but this approach is hampered by the intracellular nature of tau protein deposition, subsequent weak radiotracer binding, and low image contrast. Several new candidate probes for tau-specific PET imaging are currently available but have not found their way into broad clinical applications. This study gives an overview of the most recent PET-based neuroimaging techniques for AD. We place special emphasis on PET data analysis and interpretation techniques, as well as radiochemistry for imaging metabolism and assessing Aβ and tau pathology.

Ontology driven decision support for the diagnosis of mild cognitive impairment

In recent years, mild cognitive impairment (MCI) has attracted significant attention as an indicator of high risk for Alzheimer's disease (AD), and the diagnosis of MCI can alert patient to carry out appropriate strategies to prevent AD. To avoid subjectivity in diagnosis, we propose an ontology driven decision support method which is an automated procedure for diagnosing MCI through magnetic resonance imaging (MRI). In this approach, we encode specialized MRI knowledge into an ontology and construct a rule set using machine learning algorithms. Then we apply these two parts in conjunction with reasoning engine to automatically distinguish MCI patients from normal controls (NC). The rule set is trained by MRI data of 187 MCI patients and 177 normal controls selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) using C4.5 algorithm. By using a 10-fold cross validation, we prove that the performance of C4.5 with 80.2% sensitivity is better than other algorithms, such as support vector machine (SVM), Bayesian network (BN) and back propagation (BP) neural networks, and C4.5 is suitable for the construction of reasoning rules. Meanwhile, the evaluation results suggest that our approach would be useful to assist physicians efficiently in real clinical diagnosis for the disease of MCI.

PET approaches for diagnosis of dementia

There is increasing use of neuroimaging modalities, including PET, for diagnosing dementia. For example, FDG-PET demonstrates hypometabolic regions in the posterior cingulate gyri, precuneus, and parietotemporal association cortices, while amyloid PET indicates amyloid deposition in Alzheimer disease and mild cognitive impairment due to Alzheimer disease. Furthermore, the use of combination PET with structural MR imaging can improve the diagnostic accuracy of dementia. In other neurodegenerative dementias, each disease exhibits a specific metabolic reduction pattern. In dementia with Lewy bodies, occipital glucose metabolism is decreased, while in frontotemporal dementia, frontal and anterior temporal metabolism is predominantly decreased. These FDG-PET findings and positive or negative amyloid deposits are important biomarkers for various neurodegenerative dementias.