|ECR 2015 / B-0077|
|Improved Alzheimer’s disease diagnostic performance using structural MRI: validation of the MRI combination biomarker that won the CADDementia challenge|
|This poster is published under an open license. Please read the disclaimer for further details.|
Methods and materials
Two T1-weighted structural MRI reference datasets were considered. ADNI: baseline scans from the "complete annual year 2 visit" 1.5T standardized Alzheimer’s Disease Neuroimaging Initiative dataset  (169 normal controls (CTRL), 234 subjects with mild cognitive impairment (MCI), 101 AD patients). AIBL: baseline scans from the imaging arm of the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging  (88 CTRL, 29 MCI, and 28 AD).
The challenge-winning combination MRI biomarker was applied to each scan. First, the following individual MRI biomarkers were computed:
- cortical thickness using cross-sectional FreeSurfer,
- hippocampal shape using an in-house method,
- hippocampal texture using an in-house method,
- standard volumetry using cross-sectional FreeSurfer as well as an in-house method specifically constructed to segment the hippocampus.
The individual MRI biomarkers were subsequently age-normalized and combined using a regularized linear discriminant analysis classifier (LDA). We refer to  for further details.
The method was applied using 10-fold cross-validation stratified on diagnostic group and cohort, and performance on ADNI and AIBL was subsequently investigated separately for the two datasets. Both per-class and three-class receiver operating characteristic (ROC) curves and the associated area under the ROC curve (AUC) were computed . The evaluation python scripts supplied by the CADDementia organizers were used for this purpose.
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