Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease.

BACKGROUND: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes. AIMS: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD). METHODS: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example. RESULTS: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well. CONCLUSIONS: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

Comparing predictors of conversion to Alzheimer's disease using the disease state index.

BACKGROUND: The Disease State Index (DSI) is a method which interprets data originating from multiple different sources, assisting the clinician in the diagnosis and follow-up of dementia diseases. OBJECTIVE: We compared the differences in accuracy in differentiating stable mild cognitive impairment (S-MCI) and progressive MCI (P-MCI) obtained from different data combinations using the DSI. METHODS: We investigated 212 cases with S-MCI and 165 cases with P-MCI from the Alzheimer's Disease Neuroimaging Initiative cohort. Data from neuropsychological tests, cerebrospinal fluid, apolipoprotein E (APOE) genotype, magnetic resonance imaging (MRI) and positron emission tomography (PET) were included. RESULTS: The combination of all parameters gave the highest accuracy (accuracy 0.70, sensitivity 0.71, specificity 0.68). In the different categories, neuropsychological tests (0.65, 0.65, 0.65) and hippocampal volumetry (0.66, 0.66, 0.66) achieved the highest accuracy. CONCLUSION: In addition to neuropsychological testing, MRI is recommended to be included for differentiating S-MCI from P-MCI. APOE genotype, CSF and PET may provide some additional information.

Disease state fingerprint in frontotemporal degeneration with reference to Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Disease state index and disease state fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer's disease (AD). OBJECTIVE: We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects. METHODS: Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The disease state index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype. RESULTS: We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data. CONCLUSION: The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.

Structural MRI in frontotemporal dementia: comparisons between hippocampal volumetry, tensor-based morphometry and voxel-based morphometry.

BACKGROUND: MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods. OBJECTIVE: To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy. METHODS: Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups. RESULTS: We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55). CONCLUSION: Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.