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 (Strandberg et al., 2018).
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).
- Metabolic mismatch volume (MMV)
- Analysis of FDG PET studies
- ROI delineation
- Correlation coefficient constrained parametric images
Black QC, Grills IS, Kestin LL, Wong CYO, Wong JW, Martinez AA, Yan D. Defining a radiotherapy target with positron emission tomography. Int J Radiation Oncology Biol Phys. 2004; 60(4): 1272-1282. doi: 10.1016/j.ijrobp.2004.06.254.
Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J Nucl Med 2004; 45: 1519-1527.
Cottereau A-S, Hapdey S, Chartier L, Modzelewski R, Casasnovas O, Itti E, Tilly H, Vera P, Meignan MA, Becker S. Baseline total metabolic tumor volume measured with fixed or different adaptive thresholding methods equally predicts outcome in peripheral T cell lymphoma. J Nucl Med. 2017; 58(2): 276-281. doi: 10.2967/jnumed.116.180406.
van Dalen JA, Hoffmann AL, Dicken V, Vogel WV, Wiering B, Ruers TJ, Karssemeijer N, Oyen WJG. A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl Med Commun. 2007; 28(6): 485-493.
Geets X, Lee JA, Bol A, Lonneux M, Grégoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 2007; 34(9): 1427-1438.
Hatt M, Cheze-Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE, Albarghach NM, Metges J-P, Pradier O, Visvikis D. Reproducibility of 18F-FDG and 3’-deoxy-3’-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010; 51(9): 1368-1376. doi: 10.2967/jnumed.110.078501.
Hirata K, Kobayashi K, Wong K-P, Manabe O, Surmak A, Tamaki N, Huang S-C. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS ONE 2014; 9(8): e105682. doi: 10.1371/journal.pone.0105682.
Kruse V, Mees G, Maes A, D’Asseler Y, Borms M, Cocquyt V, Van De Wiele C. Reproducibility of FDG PET based metabolic tumor volume measurements and of their FDG distribution within. Q J Nucl Med Mol Imaging 2015; 59(4): 462-468.
Updated at: 2018-12-21
Created at: 2018-01-30
Written by: Vesa Oikonen