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Abstract Details

Machine Learning of Quantified Volumetric MR Imaging for Diagnostic Delineation of Alzheimer’s Dementia and Mild Cognitive Impairment in the Alzheimer's Disease Neuroimaging Initiative
Aging, Dementia, and Behavioral Neurology
P6 - Poster Session 6 (11:30 AM-1:00 PM)
9-011

Alzheimer’s disease (AD) is the most common cause of dementia. Recommended clinical use of imaging has been limited to visual evaluations of brain MRI for ruling out “organic” causes of dementia such as stroke or tumor. No role of imaging currently exists for identifying mild cognitive impairment (MCI). Development of FDA cleared quantitative software allows for quantification of multiple brain regions.

To evaluate discriminative ability of a newer program for identifying AD from MCI and controls.

Volumetric 1.5 and 3.0T MRI brain scans (n = 1143 with mean age 74.6 years) were obtained from ADNI using standard protocols5. This cohort consisted of controls (n = 261), early mild cognitive impairment (EMCI, n = 310), late mild cognitive impairment (LMCI, n = 223), and AD (n = 349). Neuroreader  was used to compute brain volumes. Machine learning was done using cross-validated discriminant analysis algorithm in IBM SPSS Modeler (v. 18, Armonk, NY). Area under the curve (AUC) was generated for AD and MCI subgroups.

Regionally quantified volumetric MR imaging data separated AD from non-AD groups with AUC of 89%, 85% sensitivity, and 79% specificity. Automated volumetrics delineated LMCI from other groups with AUC of 72%, 70% sensitivity, and 62% specificity (Figure 1B). EMCI was distinguished form LMCI, AD, and controls groups with AUC of 80%, 76% sensitivity, and 70% specificity. Early MCI was distinguished from controls with 94% accuracy. Predictive regions delineating AD from MCI subgroups and controls included total CSF volume, hippocampal asymmetry and temporal lobe volumes.

Machine learning analysis of quantified brain regions on MR imaging provides good diagnostic delineation of AD from MCI subgroups and normal controls. Overlap between LMCI and AD and EMCI and controls may partially account for reduced diagnostic performance in MCI. Future studies will utilize longitudinal imaging for improved delineative outcomes.

Authors/Disclosures
Cyrus A. Raji, MD, PhD (Washington University in St Louis)
PRESENTER
Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Brainreader ApS. Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Apollo Health . Prof. Raji has received personal compensation in the range of $10,000-$49,999 for serving as an Expert Witness for Neurevolution Medicine.
Somayeh Meysami, MD (Pacific Neuroscience Institute, Providence Saint Johns Health Center) Dr. Meysami has nothing to disclose.
No disclosure on file
Jamila Ahdidan No disclosure on file
No disclosure on file
Mario F. Mendez, MD, PhD, FAAN (VA Greater Los Angeles Healthcare System and UCLA) Dr. Mendez has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Medical 好色先生 Speakers' Bureau. Dr. Mendez has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for UpToDate. The institution of Dr. Mendez has received research support from NIH. Dr. Mendez has received publishing royalties from a publication relating to health care.