• Tong Tong, Qinquan Gao, Ricardo Guerrero, Christian Ledig, Liang Chen, Daniel Rueckert, and InitiativeAlzheimer's Disease NeuroimagingADNBiomedical Image Analysis Group, Department of Computing, Imperial College London..
• Biomedical Image Analysis Group, Department of Computing, Imperial College London.
• IEEE Trans Biomed Eng. 2017 Jan 1; 64 (1): 155-165.

ObjectiveIdentifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance images.MethodsWe propose a novel grading biomarker for the prediction of MCI-to-AD conversion. First, we comprehensively study the effects of several important factors on the performance in the prediction task including registration accuracy, age correction, feature selection, and the selection of training data. Based on the studies of these factors, a grading biomarker is then calculated for each MCI subject using sparse representation techniques. Finally, the grading biomarker is combined with age and cognitive measures to provide a more accurate prediction of MCI-to-AD conversion.ResultsUsing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the proposed global grading biomarker achieved an area under the receiver operating characteristic curve (AUC) in the range of 79-81% for the prediction of MCI-to-AD conversion within three years in tenfold cross validations. The classification AUC further increases to 84-92% when age and cognitive measures are combined with the proposed grading biomarker.ConclusionThe obtained accuracy of the proposed biomarker benefits from the contributions of different factors: a tradeoff registration level to align images to the template space, the removal of the normal aging effect, selection of discriminative voxels, the calculation of the grading biomarker using AD and normal control groups, and the integration of sparse representation technique and the combination of cognitive measures.SignificanceThe evaluation on the ADNI dataset shows the efficacy of the proposed biomarker and demonstrates a significant contribution in accurate prediction of MCI-to-AD conversion.

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