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Model selection criteria for dynamic brain PET studies

Overview of attention for article published in EJNMMI Physics, December 2017
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Title
Model selection criteria for dynamic brain PET studies
Published in
EJNMMI Physics, December 2017
DOI 10.1186/s40658-017-0197-0
Pubmed ID
Authors

Sandeep S. V. Golla, Sofie M. Adriaanse, Maqsood Yaqub, Albert D. Windhorst, Adriaan A. Lammertsma, Bart N. M. van Berckel, Ronald Boellaard

Abstract

 Several criteria exist to identify the optimal model for quantification of tracer kinetics. The purpose of this study was to evaluate the correspondence in kinetic model preference identification for brain PET studies among five model selection criteria: Akaike Information Criterion (AIC), AIC unbiased (AICC), model selection criterion (MSC), Schwartz Criterion (SC), and F-test. Six tracers were evaluated: [11C]FMZ, [11C]GMOM, [11C]PK11195, [11C]Raclopride, [18F]FDG, and [11C]PHT, including data from five subjects per tracer. Time activity curves (TACs) were analysed using six plasma input models: reversible single-tissue model (1T2k), irreversible two-tissue model (2T3k), and reversible two-tissue model (2T4k), all with and without blood volume fraction parameter (V B). For each tracer and criterion, the percentage of TACs preferring a certain model was calculated. For all radiotracers, strong agreement was seen across the model selection criteria. The F-test was considered as the reference, as it is a frequently used hypothesis test. The F-test confirmed the AIC preferred model in 87% of all cases. The strongest (but minimal) disagreement across regional TACs was found when comparing AIC with AICC. Despite these regional discrepancies, same preferred kinetic model was obtained using all criteria, with an exception of one FMZ subject. In conclusion, all five model selection criteria resulted in similar conclusions with only minor differences that did not affect overall model selection.

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Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 34%
Student > Ph. D. Student 4 14%
Professor 3 10%
Student > Doctoral Student 1 3%
Lecturer 1 3%
Other 3 10%
Unknown 7 24%
Readers by discipline Count As %
Neuroscience 6 21%
Medicine and Dentistry 4 14%
Chemistry 3 10%
Biochemistry, Genetics and Molecular Biology 2 7%
Computer Science 2 7%
Other 4 14%
Unknown 8 28%