Acta radiologica (Stockholm, Sweden : 1987) · 2024
The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study.
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Gerlach Leah R, Prabhakaran Vivek, Antuono Piero G, Granadillo Elias
Аннотация
BACKGROUND: Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.
PURPOSE: To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.
MATERIAL AND METHODS: A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where VA is weighted volume of frontal and temporal lobes and VP of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.
RESULTS: A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).
CONCLUSION: The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.
PURPOSE: To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.
MATERIAL AND METHODS: A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where VA is weighted volume of frontal and temporal lobes and VP of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.
RESULTS: A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).
CONCLUSION: The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.