Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern

Eva Štokelj; Petra Tomše; Tadej Tomanič; Vijay Dhawan; David Eidelberg; Maja Trošt; Urban Simončič; Alzheimer’s Disease Neuroimaging Initiative

doi:10.1186/s13550-023-01001-5

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern

EJNMMI Res. 2023 May 24;13(1):47. doi: 10.1186/s13550-023-01001-5.

Authors

Eva Štokelj¹, Petra Tomše², Tadej Tomanič³, Vijay Dhawan⁴, David Eidelberg⁴, Maja Trošt^{2

5

6}, Urban Simončič^{3

7}; Alzheimer’s Disease Neuroimaging Initiative

Affiliations

¹ Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia. eva.rebec@fmf.uni-lj.si.
² Department of Nuclear Medicine, University Medical Centre Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia.
³ Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia.
⁴ Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA.
⁵ Department of Neurology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.
⁶ Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
⁷ Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia.

Abstract

Background: Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified.

Methods: 240 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions.

Results: ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images.

Conclusions: While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Keywords: 2-[18F]FDG-PET; Alzheimer’s disease; Cohort size; Image resolution; Metabolic brain pattern; Network analysis.

Abstract

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