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Abstract #2015

Assessment of mild cognitive impairment detection in a community-dwelling population using quantitative, multiparametric MRI-based classification

Mark J.R.J. Bouts1,2,3, Jeroen van der Grond2, Meike W. Vernooij4,5, Tijn M. Schouten1,2,3, Frank de Vos1,2,3, Lotte G.M. Cremers4,5, Mark de Rooij1,3, Wiro J. Niessen4,6,7, M. Arfan Ikram4,5,8, and Serge A.R.B. Rombouts1,2,3

1Psychology, Leiden University, Leiden, Netherlands, 2Radiology, Leiden University Medical Center, Leiden, Netherlands, 3Leiden Institute for Brain and Cognition, Leiden University, Leiden, Netherlands, 4Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands, 5Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands, 6Medical Informatics, Erasmus MC University Medical Center, Rotterdam, Netherlands, 7Applied Sciences, Delft University of Technology, Delft, Netherlands, 8Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands

Multiparametric MRI-based classification algorithms improve classification of dementia over single measure classifications. Yet, how accurate these algorithms are in identifying subjects with mild cognitive impairment (MCI) in a general population is unclear. We evaluated single and multiparametric algorithms that include structural and diffusion tensor MRI in their potential to accurately differentiate MCI from normal aging subjects in a community-dwelling population. While highest classification rates were observed for multiparametric algorithms, overall classification performance was low (AUC: 0.524-0.631). Our results suggest that accurate MRI-based single subject detection of MCI within a population-based setting may be difficult to achieve using MR imaging alone.

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