E1066. An Anterior-Posterior Atrophy Index to distinguish Alzheimer’s Disease from Frontotemporal Disorders: An Automated Volumetric MRI Study
Authors
Leah Gerlach;
Medical College of Wisconsin
Vivek Prabhakaran;
University of Wisconsin School of Medicine and Public Health
Piero Antuono;
Medical College of Wisconsin
Elias Granadillo;
Medical College of Wisconsin; University of Wisconsin School of Medicine and Public Health
Objective:
Determining etiologies of dementia can be difficult, yet specific diagnosis is necessary to appropriately manage cognitive decline. Alzheimer Disease (AD) and frontotemporal disorders (FTD) can present with similar clinical syndromes, so additional diagnostic tools are often useful in distinguishing the two. Magnetic resonance imaging (MRI) can be used to identify patterns of brain atrophy in dementia syndromes and aid in diagnosis. Recently, automated software tools like Neuroreader (NR) have been developed to report volumetric data for segments of the brain, which has been shown useful in distinguishing types of dementia in research cohorts. With this, research has shown that the application of an anterior vs. posterior index (API) to volumetric MRI data could aid in distinguishing AD from FTD. However, accuracy of such tools in clinical cohorts is limited. We hypothesize that an API from NR data can accurately predict AD vs FTD diagnosis in a clinical cohort.
Materials and Methods:
Patients with AD or FTD and a completed NR were included. A retrospective chart review was completed to gather demographics, memory diagnoses over time, and cognitive screening and NR data. We derived a simplified API to reflect NR data, receiver operating curves (ROC) and two separate analyses (API and the parietal lobe) assessed the efficacy of the API vs. single brain areas in predicting the diagnosis of AD vs FTD as it was determined clinically using current diagnostic criteria.
Results:
Thirty-nine patients with FTD and 78 patients with AD were included. The two groups are similar in terms of demographics, mini-mental state examination score, and MRI type. The API had an excellent performance with an AUC of 0.816 in addition to a positive association with diagnostic classification on logistic regression analysis (<em>B</em> = 1.491, <em>p</em> < 0.001). Most brain areas in isolation performed poorly, with the exception of the parietal lobe which had an AUC of 0.703, indicating an acceptable performance.
Conclusion:
The API biomarker can successfully distinguish ID and FTD in a clinical setting with an excellent level of performance, performing better than any studied brain area alone.
