FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease.
نویسندگان
چکیده
Distinguishing Alzheimer's disease (AD) and frontotemporal dementia (FTD) currently relies on a clinical history and examination, but positron emission tomography with [(18)F] fluorodeoxyglucose (FDG-PET) shows different patterns of hypometabolism in these disorders that might aid differential diagnosis. Six dementia experts with variable FDG-PET experience made independent, forced choice, diagnostic decisions in 45 patients with pathologically confirmed AD (n = 31) or FTD (n = 14) using five separate methods: (1) review of clinical summaries, (2) a diagnostic checklist alone, (3) summary and checklist, (4) transaxial FDG-PET scans and (5) FDG-PET stereotactic surface projection (SSP) metabolic and statistical maps. In addition, we evaluated the effect of the sequential review of a clinical summary followed by SSP. Visual interpretation of SSP images was superior to clinical assessment and had the best inter-rater reliability (mean kappa = 0.78) and diagnostic accuracy (89.6%). It also had the highest specificity (97.6%) and sensitivity (86%), and positive likelihood ratio for FTD (36.5). The addition of FDG-PET to clinical summaries increased diagnostic accuracy and confidence for both AD and FTD. It was particularly helpful when raters were uncertain in their clinical diagnosis. Visual interpretation of FDG-PET after brief training is more reliable and accurate in distinguishing FTD from AD than clinical methods alone. FDG-PET adds important information that appropriately increases diagnostic confidence, even among experienced dementia specialists.
منابع مشابه
The diagnostic difference between 18F- FDG PET and 99mTc-HMPAO SPECT perfusion imaging in assessment of Alzheimer's disease
Introduction:Brain imaging with F-18 fluorodeoxyglucose (18F-FDG) positron emission tomography or Tc-99m hexamethylpropyleneamine oxime (99mTc-HMPAO) SPECT is widely used for the evaluation of Alzheimer's dementia (AD); we aim to assess superiority of one method over the other. Methods: Twenty four patients with clinical diagnosi...
متن کاملBrain PET in the diagnosis of Alzheimer's disease.
OBJECTIVES The aim of this article was to review the current role of brain PET in the diagnosis of Alzheimer dementia. The characteristic patterns of glucose metabolism on brain FDG-PET can help in differentiating Alzheimer's disease from other causes of dementia such as frontotemporal dementia and dementia of Lewy body. Amyloid brain PET may exclude significant amyloid deposition and thus Alzh...
متن کاملCombined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia
INTRODUCTION Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separatel...
متن کاملImpact of (18)FDG PET and (11)C-PIB PET brain imaging on the diagnosis of Alzheimer's disease and other dementias in a regional memory clinic in Hong Kong.
OBJECTIVE This study investigated the improvement in the accuracy of diagnosis of dementia subtypes among Chinese dementia patients who underwent [18F]-2-fluoro-2-deoxy-D-glucose positron emission tomography ((18)FDG PET) with or without carbon 11-labelled Pittsburgh compound B ((11)C-PIB). METHODS This case series was performed in the Memory Clinic at Queen Mary Hospital, Hong Kong. We revie...
متن کاملAutomatic Classification of Alzheimers Disease vs. Frontotemporal Dementia: A Decision Tree Approach with FDG-PET
We introduce a novel approach for the automatic classification of FDG-PET scans of subjects with Alzheimers Disease (AD) and Frontotemporal dementia (FTD). Unlike previous work in the literature which focuses on principal component analysis and predefined regions of interest, we propose the use of decision tree learning combined with empirically determined regions of interest as attributes. The...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Brain : a journal of neurology
دوره 130 Pt 10 شماره
صفحات -
تاریخ انتشار 2007