Automatic volumetry on MR brain images can support diagnostic decision making.

Background: Diagnostic decisions in clinical imaging currently rely almost exclusively on visual image interpretation. This can lead to uncertainty, for example in dementia disease, where some of the changes resemble those of normal ageing. We hypothesized that extracting volumetric data from patients MR brain images, relating them to reference data and presenting the results as a colour overlay on the grey scale data would aid diagnostic readers in classifying dementia disease versus normal ageing. Methods: A proof-of-concept forced-choice reader study was designed using MR brain images from 36 subjects. Images were segmented into 43 regions using an automatic atlas registration-based label propagation procedure. Seven subjects had clinically probable AD, the remaining 29 of a similar age range were used as controls. Seven of the control subject data sets were selected at random to be presented along with the seven AD datasets to two readers, who were blinded to all clinical and demographic information except age and gender. Readers were asked to review the grey scale MR images and to record their choice of diagnosis (AD or non-AD) along with their confidence in this decision. Afterwards, readers were given the option to switch on a false-colour overlay representing the relative size of the segmented structures. Colorization was based on the size rank of the test subject when compared with a reference group consisting of the 22 control subjects who were not used as review subjects. The readers were then asked to record whether and how the additional information had an impact on their diagnostic confidence. Results: The size rank colour overlays were useful in 18 of 28 diagnoses, as determined by their impact on readers diagnostic confidence. A not useful result was found in 6 of 28 cases. The impact of the additional information on diagnostic confidence was significant (p < 0.02). Conclusion: Volumetric anatomical information extracted from brain images using automatic segmentation and presented as colour overlays can support diagnostic decision making. © 2008 Heckemann et al; licensee BioMed Central Ltd.Published versio

Diagnosis of Alzheimer's Disease Based on Disease-Specific Autoantibody Profiles in Human Sera

After decades of Alzheimer's disease (AD) research, the development of a definitive diagnostic test for this disease has remained elusive. The discovery of blood-borne biomarkers yielding an accurate and relatively non-invasive test has been a primary goal. Using human protein microarrays to characterize the differential expression of serum autoantibodies in AD and non-demented control (NDC) groups, we identified potential diagnostic biomarkers for AD. The differential significance of each biomarker was evaluated, resulting in the selection of only 10 autoantibody biomarkers that can effectively differentiate AD sera from NDC sera with a sensitivity of 96.0% and specificity of 92.5%. AD sera were also distinguishable from sera obtained from patients with Parkinson's disease and breast cancer with accuracies of 86% and 92%, respectively. Results demonstrate that serum autoantibodies can be used effectively as highly-specific and accurate biomarkers to diagnose AD throughout the course of the disease

Diagnosing dementia: interrater reliability assessment and accuracy of the NINCDS/ADRDA criteria versus CERAD histopathological criteria for Alzheimer's disease.

We investigated the interrater reliability and accuracy of two independent medical doctors in using NINCDS/ADRDA criteria to classify 82 elderly subjects enrolled in OPTIMA, a longitudinal study investigating dementia. Kappa statistics revealed moderate agreement (0.5) in overall classification of dementia type, and almost perfect agreement (0.9) on the absence or presence of dementia. Combining NINCDS/ADRDA 'possible' and 'probable' Alzheimer's disease (AD) categories produced substantial agreement (0.7). Comparison with CERAD histopathological criteria for AD showed that combining 'possible' and 'probable' AD resulted in a high sensitivity and accuracy, but a low specificity. To increase specificity, the NINCDS/ADRDA 'probable AD' category should be used alone. An important finding was that the accuracy of diagnoses of AD made from the case notes alone was not different from the diagnoses obtained following active involvement with participants

Abnormal function of potassium channels in platelets of patients with Alzheimer's disease.

BACKGROUND: Reports of abnormalities of potassium-channel function in various cultured cells of Alzheimer's disease patients led us to attempt to characterise the pharmacological characteristics of the abnormal channel. METHODS: We studied platelets from 14 patients with Alzheimer-type dementia and 14 non-demented controls matched for age and sex. The effects of specific inhibitors of K+ channels on the efflux of rubidium-86 ions, a radioactive analogue of K+, from the platelets were measured. FINDINGS: Normal platelets contain three types of K+ channel, sensitive to the inhibitory actions of apamin (small-conductance calcium-dependent potassium channels), charybdotoxin (of less specificity, but probably intermediate-conductance calcium-dependent K+ channels), and alpha-dendrotoxin (voltage-sensitive K+ channels). However, 8Rb+ efflux from the platelets of patients with Alzheimer-type dementia was not inhibited by either apamin or charybdotoxin. By contrast, inhibition by alpha-dendrotoxin did occur. INTERPRETATION: Our results suggest that calcium-dependent K+ channels in platelets are selectively impaired in Alzheimer's disease. A similar abnormality in neurons could contribute to the pathophysiology of the disorder