A novel semiautomated method for background activity and biological tumour volume definition to improve standardisation of 18F-FET PET imaging in glioblastoma

Background: Multicentre clinical trials evaluating the role of F-18-Fluoroethyl-L-tyrosine (F-18-FET) PET as a diagnostic biomarker in glioma management have highlighted a need for standardised methods of data analysis. F-18-FET uptake normalised against background in the contralateral brain is a standard imaging technique to delineate the biological tumour volume (BTV). Quantitative analysis of F-18-FET PET images requires a consistent and robust background activity. Currently, defining background activity involves the manual selection of an arbitrary region of interest, a process that is subject to large variability. This study aims to eliminate methodological errors in background activity definition through the introduction of a semiautomated method for region of interest selection. A new method for background activity definition, involving the semiautomated generation of mirror-image (MI) reference regions, was compared with the current state-of-the-art method, involving manually drawing crescent-shape (gCS) reference regions. The MI and gCS methods were tested by measuring values of background activity and resulting BTV of F-18-FET PET scans of ten patients with recurrent glioblastoma multiforme generated from inputs provided by seven readers. To assess intra-reader variability, each scan was evaluated six times by each reader. Intra-and inter-reader variability in background activity and BTV definition was assessed by means of coefficient of variation. Results: Compared to the gCS method, the MI method showed significantly lower intra- and inter-reader variability both in background activity and in BTV definition. Conclusions: The proposed semiautomated MI method minimises intra- and inter-reader variability, providing a valuable approach for standardisation of F-18-FET PET quantitative parameters.

Automated summary

This study aims to eliminate methodological errors in background activity definition through the introduction of a semiautomated method for region of interest selection. The proposed semiautomated method showed significantly lower variability in background activity and BTV definition compared to the current state-of-the-art method involving manual drawing of reference regions.