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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.
Congress: ECR 2015
Poster No.: B-0077
Type: Scientific Paper
Keywords: Neuroradiology brain, Computer applications, MR, Computer Applications-Detection, diagnosis, Dementia, Outcomes
Authors: L. Sørensen1, M. Lillholm1, A. Pai1, I. Balas1, C. Anker2, C. Igel1, M. Nielsen1; 1Copenhagen/DK, 2Kgs. Lyngby/DK
DOI:10.1594/ecr2015/B-0077

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 [2] (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 [3] (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 [4] 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 [1]. The evaluation python scripts supplied by the CADDementia organizers were used for this purpose.

 

 

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