Glycolysis is increased in metabolically active tumours and in inflamed tissue, which can be detected using FDG PET imaging. FDG uptake is usually quantitated using semi-quantitative methods, most frequently SUV and especially SUVmax, despite its well-known limitations. In many recent studies, volume-based parameters such as metabolic tumour volume (MTV) and total lesion glycolysis (TLG) have been used (Hirata et al., 2014) and found to provide better prognostic indices than traditional SUV (Im et al., 2015; Vallius et al., 2018); comparison of the results is difficult since different methods are being used, and parameters are also heavily dependent on the PET scanner and reconstruction methods.
Metabolic volume is defined as the lesion volume within a delineated boundary. Several delineation methods have been used, including
- fixed threshold based on certain SUV (Im et al., 2016)
- relative threshold based on certain voxel SUV per SUVmax or SUVpeak
- tumour-to-background or contrast based methods (Schaefer et al., 2013; Avramovic et al., 2017)
- gradient (watershed) based region-growing methods (Geets et al., 2007; Lee et al., 2007; Liao et al., 2012; Kao et al., 2012)
- cluster based methods
Fixed and relative threshold based and lesion-to-background methods are easily applicable, but dependent on the image quality (resolution and noise) and lesion size. The more complex methods are more robust for the image quality, but dependent on the applied algorithm and software implementation (Schaefer et al., 2016; Gallamini & Kostakoglu, 2017).
Metabolic volume can be multiplied with mean SUV of that volume to get metabolic activity (total lesion activity, lesion metabolic activity).
Cardiac metabolic activity (CMV) was used to analyze FDG studies in cardiac sarcoidosis (Ahmadian et al., 2014). SUV threshold should be determined based on the blood activity from heart cavity or aorta, instead of using fixed threshold or liver as reference region (Ahmadian et al., 2017 Furuya et al., 2018).
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Created at: 2018-01-30
Updated at: 2018-10-09
Written by: Vesa Oikonen