Diagnostic performance of an automated analysis software for the diagnosis of Alzheimer's dementia with (18)F FDG PET.

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Diagnostic performance of an automated analysis software for the diagnosis of Alzheimer's dementia with (18)F FDG PET.

Am J Nucl Med Mol Imaging. 2017;7(1):12-23

Authors: Partovi S, Yuh R, Pirozzi S, Lu Z, Couturier S, Grosse U, Schluchter MD, Nelson A, Jones R, O'Donnell JK, Faulhaber P

Abstract
The objective of this study was to assess the ability of a quantitative software-aided approach to improve the diagnostic accuracy of (18)F FDG PET for Alzheimer's dementia over visual analysis alone. Twenty normal subjects (M:F-12:8; mean age 80.6 years) and twenty mild AD subjects (M:F-12:8; mean age 70.6 years) with (18)F FDG PET scans were obtained from the ADNI database. Three blinded readers interpreted these PET images first using a visual qualitative approach and then using a quantitative software-aided approach. Images were classified on two five-point scales based on normal/abnormal (1-definitely normal; 5-definitely abnormal) and presence of AD (1-definitely not AD; 5-definitely AD). Diagnostic sensitivity, specificity, and accuracy for both approaches were compared based on the aforementioned scales. The sensitivity, specificity, and accuracy for the normal vs. abnormal readings of all readers combined were higher when comparing the software-aided vs. visual approach (sensitivity 0.93 vs. 0.83 P = 0.0466; specificity 0.85 vs. 0.60 P = 0.0005; accuracy 0.89 vs. 0.72 P<0.0001). The specificity and accuracy for absence vs. presence of AD of all readers combined were higher when comparing the software-aided vs. visual approach (specificity 0.90 vs. 0.70 P = 0.0008; accuracy 0.81 vs. 0.72 P = 0.0356). Sensitivities of the software-aided and visual approaches did not differ significantly (0.72 vs. 0.73 P = 0.74). The quantitative software-aided approach appears to improve the performance of (18)F FDG PET for the diagnosis of mild AD. It may be helpful for experienced (18)F FDG PET readers analyzing challenging cases.

PMID: 28123864 [PubMed]

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