How to assess intra- and inter-observer agreement with quantitative PET using variance component analysis: a proposal for standardisation

BMC Medical Imaging

Research Article

Eivind Antonsen Segtnan¹  Mie Holm Vilstrup¹  Oke Gerke²  Poul Flemming Høilund-Carlsen³  Ulrich Halekoh⁴ 

[1] Department of Nuclear Medicine, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense C, Denmark;Department of Nuclear Medicine, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense C, Denmark;Centre of Health Economics Research, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark;Department of Nuclear Medicine, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense C, Denmark;Department of Clinical Research, University of Southern Denmark, Winsløwparken 19, 5000, Odense C, Denmark;Epidemiology, Biostatistics and Biodemography, University of Southern Denmark, J. B. Winsløws Vej 9b, 5000, Odense C, Denmark;

关键词: Bland-Altman limits of agreement;    Intra-observer;    Inter-observer;    Intraobserver;    Interobserver;    Intra-rater;    Inter-rater;    Repeatability coefficient;    Sample size;    Standardised uptake value;   

DOI  :  10.1186/s12880-016-0159-3

 received in 2016-09-02, accepted in 2016-09-15,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundQuantitative measurement procedures need to be accurate and precise to justify their clinical use. Precision reflects deviation of groups of measurement from another, often expressed as proportions of agreement, standard errors of measurement, coefficients of variation, or the Bland-Altman plot. We suggest variance component analysis (VCA) to estimate the influence of errors due to single elements of a PET scan (scanner, time point, observer, etc.) to express the composite uncertainty of repeated measurements and obtain relevant repeatability coefficients (RCs) which have a unique relation to Bland-Altman plots. Here, we present this approach for assessment of intra- and inter-observer variation with PET/CT exemplified with data from two clinical studies.MethodsIn study 1, 30 patients were scanned pre-operatively for the assessment of ovarian cancer, and their scans were assessed twice by the same observer to study intra-observer agreement. In study 2, 14 patients with glioma were scanned up to five times. Resulting 49 scans were assessed by three observers to examine inter-observer agreement. Outcome variables were SUVmax in study 1 and cerebral total hemispheric glycolysis (THG) in study 2.ResultsIn study 1, we found a RC of 2.46 equalling half the width of the Bland-Altman limits of agreement. In study 2, the RC for identical conditions (same scanner, patient, time point, and observer) was 2392; allowing for different scanners increased the RC to 2543. Inter-observer differences were negligible compared to differences owing to other factors; between observer 1 and 2: −10 (95 % CI: −352 to 332) and between observer 1 vs 3: 28 (95 % CI: −313 to 370).ConclusionsVCA is an appealing approach for weighing different sources of variation against each other, summarised as RCs. The involved linear mixed effects models require carefully considered sample sizes to account for the challenge of sufficiently accurately estimating variance components.

【 授权许可】

CC BY   
© The Author(s). 2016