Article

Temporoparietal hypometabolism in frontotemporal lobar degeneration and associated imaging diagnostic errors.

Department of Neurology, University of Texas Southwestern Medical Center at Dallas, 5323 Harry Hines Blvd, Dallas, TX 75390-9129, USA.
Archives of neurology (Impact Factor: 7.58). 11/2010; 68(3):329-37. DOI: 10.1001/archneurol.2010.295
Source: PubMed

ABSTRACT To evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomographic scans with fludeoxyglucose F 18 (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and patients with Alzheimer disease (AD).
Twelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere-frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex, and posterior cingulate cortex. Results were compared with neuropathological diagnoses.
Academic medical centers.
Forty-five patients with pathologically confirmed FTLD (n=14) or AD (n=31).
Raters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27 of 31 patients [87%]) than in patients with FTLD (7 of 14 patients [50%]) (P=.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 scans lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD.
Temporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelihood of FTLD, even when temporoparietal hypometabolism is present. Ultimately, the accurate interpretation of FDG-PET scans in patients with dementia cannot rest on the presence or absence of a single region of hypometabolism but rather must take into account the relative hypometabolism of all brain regions.

0 Bookmarks
 · 
220 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 − 91%) than all other approaches (AUC = 57 − 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 04/2014; 35(9). · 6.92 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In the search for therapeutic modifiers, frontotemporal dementia (FTD) has traditionally been overshadowed by other conditions such as Alzheimer's disease (AD). A clinically and pathologically diverse condition, FTD has been galvanized by a number of recent discoveries such as novel genetic variants in familial and sporadic forms of disease and the identification of TAR DNA binding protein of 43 kDa (TDP-43) as the defining constituent of inclusions in more than half of cases. In combination with an ever-expanding knowledge of the function and dysfunction of tau—a protein which is pathologically aggregated in the majority of the remaining cases—there exists a greater understanding of FTD than ever before. These advances may indicate potential approaches for the development of hypothetical therapeutics, but FTD remains highly complex and the roles of tau and TDP-43 in neurodegeneration are still wholly unclear. Here the challenges facing potential therapeutic strategies are discussed, which include sufficiently accurate disease diagnosis and sophisticated technology to deliver effective therapies.
    Frontiers in Aging Neuroscience 08/2014; · 2.84 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Biomarkers derived from brain magnetic resonance (MR) imaging have promise in being able to assist in the clinical diagnosis of brain pathologies. These have been used in many studies in which the goal has been to distinguish between pathologies such as Alzheimer's disease and healthy aging. However, other dementias, in particular, frontotemporal dementia, also present overlapping pathological brain morphometry patterns. Hence, a classifier that can discriminate morphometric features from a brain MRI from the three classes of normal aging, Alzheimer's disease (AD), and frontotemporal dementia (FTD) would offer considerable utility in aiding in correct group identification. Compared to the conventional use of multiple pair-wise binary classifiers that learn to discriminate between two classes at each stage, we propose a single three-way classification system that can discriminate between three classes at the same time. We present a novel classifier that is able to perform a three-class discrimination test for discriminating among AD, FTD, and normal controls (NC) using volumes, shape invariants, and local displacements (three features) of hippocampi and lateral ventricles (two structures times two hemispheres individually) obtained from brain MR images. In order to quantify its utility in correct discrimination, we optimize the three-class classifier on a training set and evaluate its performance using a separate test set. This is a novel, first-of-its-kind comparative study of multiple individual biomarkers in a three-class setting. Our results demonstrate that local atrophy features in lateral ventricles offer the potential to be a biomarker in discriminating among AD, FTD, and NC in a three-class setting for individual patient classification.
    Frontiers in Neurology 05/2014; 5:71.

Full-text (2 Sources)

Download
96 Downloads
Available from
Jul 24, 2014