E Laffon1, F Lamare, H de Clermont, I A Burger, R Marthan. 1. Service de Médecine Nucléaire, Hôpital du Haut-Lévèque, CHU de Bordeaux, Avenue de Magellan, 33604, Pessac, France, elaffon@u-bordeaux2.fr.
Abstract
OBJECTIVES: To assess variability of the average standard uptake value (SUV) computed by varying the number of hottest voxels within an (18)F-fluorodeoxyglucose ((18)F-FDG)-positive lesion. This SUV metric was compared with the maximal SUV (SUV(max): the hottest voxel) and peak SUV (SUV(peak): SUV(max) and its 26 neighbouring voxels). METHODS: Twelve lung cancer patients (20 lesions) were analysed using PET dynamic acquisition involving ten successive 2.5-min frames. In each frame and lesion, average SUV obtained from the N = 5, 10, 15, 20, 25 or 30 hottest voxels (SUV(max-N)), SUV(max) and SUV(peak) were assessed. The relative standard deviations (SDrs) from ten frames were calculated for each SUV metric and lesion, yielding the mean relative SD from 20 lesions for each SUV metric (SDr(N), SDr(max) and SDr(peak)), and hence relative measurement error and repeatability (MEr-R). RESULTS: For each N, SDr(N) was significantly lower than SDr(max) and SDr(peak). SDr(N) correlated strongly with N: 6.471 × N(-0.103) (r = 0.994; P < 0.01). MEr-R of SUV(max-30) was 8.94-12.63% (95% CL), versus 13.86-19.59% and 13.41-18.95% for SUV(max) and SUV(peak) respectively. CONCLUSIONS: Variability of SUV(max-N) is significantly lower than for SUV(max) and SUV(peak). Further prospective studies should be performed to determine the optimal total hottest volume, as voxel volume may depend on the PET system. KEY POINTS: • PET imaging provides functional parameters of (18) F-FDG-positive lesions, such as SUVmax and SUVpeak. • Averaging SUV from several hottest voxels (SUVmax-N) is a further SUV metric. • Variability of SUVmax-N is significantly lower than SUVmax and SUVpeak variability. • SUVmax-N should improve SUV accuracy for predicting outcome or assessing treatment response. • An optimal total hottest volume should be determined through further prospective studies.
OBJECTIVES: To assess variability of the average standard uptake value (SUV) computed by varying the number of hottest voxels within an (18)F-fluorodeoxyglucose ((18)F-FDG)-positive lesion. This SUV metric was compared with the maximal SUV (SUV(max): the hottest voxel) and peak SUV (SUV(peak): SUV(max) and its 26 neighbouring voxels). METHODS: Twelve lung cancerpatients (20 lesions) were analysed using PET dynamic acquisition involving ten successive 2.5-min frames. In each frame and lesion, average SUV obtained from the N = 5, 10, 15, 20, 25 or 30 hottest voxels (SUV(max-N)), SUV(max) and SUV(peak) were assessed. The relative standard deviations (SDrs) from ten frames were calculated for each SUV metric and lesion, yielding the mean relative SD from 20 lesions for each SUV metric (SDr(N), SDr(max) and SDr(peak)), and hence relative measurement error and repeatability (MEr-R). RESULTS: For each N, SDr(N) was significantly lower than SDr(max) and SDr(peak). SDr(N) correlated strongly with N: 6.471 × N(-0.103) (r = 0.994; P < 0.01). MEr-R of SUV(max-30) was 8.94-12.63% (95% CL), versus 13.86-19.59% and 13.41-18.95% for SUV(max) and SUV(peak) respectively. CONCLUSIONS: Variability of SUV(max-N) is significantly lower than for SUV(max) and SUV(peak). Further prospective studies should be performed to determine the optimal total hottest volume, as voxel volume may depend on the PET system. KEY POINTS: • PET imaging provides functional parameters of (18) F-FDG-positive lesions, such as SUVmax and SUVpeak. • Averaging SUV from several hottest voxels (SUVmax-N) is a further SUV metric. • Variability of SUVmax-N is significantly lower than SUVmax and SUVpeak variability. • SUVmax-N should improve SUV accuracy for predicting outcome or assessing treatment response. • An optimal total hottest volume should be determined through further prospective studies.
Authors: Irene A Burger; Dominic M Huser; Cyrill Burger; Gustav K von Schulthess; Alfred Buck Journal: Nucl Med Biol Date: 2012-03-03 Impact factor: 2.408
Authors: Steven M. Larson; Yusuf Erdi; Timothy Akhurst; Madhu Mazumdar; Homer A. Macapinlac; Ronald D. Finn; Cecille Casilla; Melissa Fazzari; Neil Srivastava; Henry W.D. Yeung; John L. Humm; Jose Guillem; Robert Downey; Martin Karpeh; Alfred E. Cohen; Robert Ginsberg Journal: Clin Positron Imaging Date: 1999-05
Authors: Daniela Thorwarth; Stefan Welz; David Mönnich; Christina Pfannenberg; Konstantin Nikolaou; Matthias Reimold; Christian La Fougère; Gerald Reischl; Paul-Stefan Mauz; Frank Paulsen; Markus Alber; Claus Belka; Daniel Zips Journal: J Nucl Med Date: 2019-05-10 Impact factor: 10.057
Authors: Edwin E G W Ter Voert; Urs J Muehlematter; Gaspar Delso; Daniele A Pizzuto; Julian Müller; Hannes W Nagel; Irene A Burger Journal: EJNMMI Res Date: 2018-07-27 Impact factor: 3.138
Authors: Brigitte Fuenfgeld; Philipp Mächler; Dorothee R Fischer; Giuseppe Esposito; Elisabeth Jane Rushing; Philipp A Kaufmann; Paul Stolzmann; Martin W Huellner Journal: PLoS One Date: 2020-04-17 Impact factor: 3.240
Authors: Martin Lyngby Lassen; Jacek Kwiecinski; Damini Dey; Sebastien Cadet; Guido Germano; Daniel S Berman; Philip D Adamson; Alastair J Moss; Marc R Dweck; David E Newby; Piotr J Slomka Journal: Eur J Nucl Med Mol Imaging Date: 2019-08-05 Impact factor: 9.236