Research Article High-Dimensional Medial Lobe Morphometry: An Automated MRI Biomarker for the New AD Diagnostic Criteria Simon Duchesne, 1,2 Fernando Valdivia, 2 Abderazzak Mouiha, 2 and Nicolas Robitaille 2 1 D´ epartment de Radiologie, Facult´ e de M´ edecine, Universit´ e Laval, Quebec, QC, Canada G1V 0A6 2 Institut Universitaire de Sant´ e Mentale de Qu´ ebec, 2601 de la Canardi´ ere/F-3582, Quebec, QC, Canada G1J 2G3 Correspondence should be addressed to Simon Duchesne; simon.duchesne@crulrg.ulaval.ca Received 24 March 2014; Accepted 25 July 2014; Published 31 August 2014 Academic Editor: Lucilla Parnetti Copyright © 2014 Simon Duchesne et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction. Medial temporal lobe atrophy assessment via magnetic resonance imaging (MRI) has been proposed in recent criteria as an in vivo diagnostic biomarker of Alzheimer’s disease (AD). However, practical application of these criteria in a clinical setting will require automated MRI analysis techniques. To this end, we wished to validate our automated, high-dimensional morphometry technique to the hypothetical prediction of future clinical status from baseline data in a cohort of subjects in a large, multicentric setting, compared to currently known clinical status for these subjects. Materials and Methods. Te study group consisted of 214 controls, 371 mild cognitive impairment (147 having progressed to probable AD and 224 stable), and 181 probable AD from the Alzheimer’s Disease Neuroimaging Initiative, with data acquired on 58 diferent 1.5T scanners. We measured the sensitivity and specifcity of our technique in a hierarchical fashion, frst testing the efect of intensity standardization, then between diferent volumes of interest, and fnally its generalizability for a large, multicentric cohort. Results. We obtained 73.2% prediction accuracy with 79.5% sensitivity for the prediction of MCI progression to clinically probable AD. Te positive predictive value was 81.6% for MCI progressing on average within 1.5 (0.3 s.d.) year. Conclusion. With high accuracy, the technique’s ability to identify discriminant medial temporal lobe atrophy has been demonstrated in a large, multicentric environment. It is suitable as an aid for clinical diagnostic of AD. 1. Introduction 1.1. Medial Temporal Lobe Atrophy as a Structural Biomarker of Alzheimer’s Disease Progression. Early identifcation of patients most at risk of progression to dementia due to Alzheimer’s disease (AD) remains a crucial clinical and research issue. To address this concern new criteria have been proposed to increase diagnostic certainty and better identify individuals in a prodromal state, mild cognitive impairment (MCI) due to AD [1–3]. In vivo biomarkers of disease progression, both chemical and imaging, lie at the heart of these criteria. Te earliest AD-associated brain alterations, according to histopathological staging [4], occur in medial temporal lobe structures, in particular the hippocampus and entorhinal cortices; they have been reported in amnestic MCI subjects [5, 6]. Te AD neurodegenerative cascade results in dendritic pruning, loss of synapses, and eventually neuronal death, resulting in cerebral atrophy of which structural magnetic resonance imaging (MRI) is able to measure. Tus, medial temporal atrophy (MTA) has been reported extensively on the continuum from MCI to AD [7, 8] and is a recognized imaging biomarker in the new criteria [1–3]. Te most validated procedure to estimate MTA relies on expert manual outlining (i.e., segmentation) of indi- vidual or ensembles of structures on high resolution T1- weighted MRI, following an established set of anatomical landmarks [9]. While manual segmentation is accepted as the best available technique, it cannot be widely used within a large-scale clinical setting, as the investment in expertise and resources is prohibitively great. Tis type of applica- tion thus necessitates semiautomated or ideally completely automated image processing techniques, as a cost-efcient strategy. Hindawi Publishing Corporation International Journal of Alzheimer’s Disease Volume 2014, Article ID 278096, 12 pages http://dx.doi.org/10.1155/2014/278096