UNLABELLED: The objective of this study was to establish the repeatability and reproducibility limits of several volume-related PET image-derived indices-namely tumor volume (TV), mean standardized uptake value, total glycolytic volume (TGV), and total proliferative volume (TPV)-relative to those of maximum standardized uptake value (SUV(max)), commonly used in clinical practice. METHODS: Fixed and adaptive thresholding, fuzzy C-means, and fuzzy locally adaptive Bayesian methodology were considered for TV delineation. Double-baseline (18)F-FDG (17 lesions, 14 esophageal cancer patients) and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) (12 lesions, 9 breast cancer patients) PET scans, acquired at a mean interval of 4 d and before any treatment, were used for reproducibility evaluation. The repeatability of each method was evaluated for the same datasets and compared with manual delineation. RESULTS: A negligible variability of less than 5% was measured for all segmentation approaches in comparison to manual delineation (5%-35%). SUV(max) reproducibility levels were similar to others previously reported, with a mean percentage difference of 1.8% +/- 16.7% and -0.9% +/- 14.9% for the (18)F-FDG and (18)F-FLT lesions, respectively. The best TV, TGV, and TPV reproducibility limits ranged from -21% to 31% and -30% to 37% for (18)F-FDG and (18)F-FLT images, respectively, whereas the worst reproducibility limits ranged from -90% to 73% and -68% to 52%, respectively. CONCLUSION: The reproducibility of estimating TV, mean standardized uptake value, and derived TGV and TPV was found to vary among segmentation algorithms. Some differences between (18)F-FDG and (18)F-FLT scans were observed, mainly because of differences in overall image quality. The smaller reproducibility limits for volume-derived image indices were similar to those for SUV(max), suggesting that the use of appropriate delineation tools should allow the determination of tumor functional volumes in PET images in a repeatable and reproducible fashion.
UNLABELLED: The objective of this study was to establish the repeatability and reproducibility limits of several volume-related PET image-derived indices-namely tumor volume (TV), mean standardized uptake value, total glycolytic volume (TGV), and total proliferative volume (TPV)-relative to those of maximum standardized uptake value (SUV(max)), commonly used in clinical practice. METHODS: Fixed and adaptive thresholding, fuzzy C-means, and fuzzy locally adaptive Bayesian methodology were considered for TV delineation. Double-baseline (18)F-FDG (17 lesions, 14 esophageal cancerpatients) and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) (12 lesions, 9 breast cancerpatients) PET scans, acquired at a mean interval of 4 d and before any treatment, were used for reproducibility evaluation. The repeatability of each method was evaluated for the same datasets and compared with manual delineation. RESULTS: A negligible variability of less than 5% was measured for all segmentation approaches in comparison to manual delineation (5%-35%). SUV(max) reproducibility levels were similar to others previously reported, with a mean percentage difference of 1.8% +/- 16.7% and -0.9% +/- 14.9% for the (18)F-FDG and (18)F-FLT lesions, respectively. The best TV, TGV, and TPV reproducibility limits ranged from -21% to 31% and -30% to 37% for (18)F-FDG and (18)F-FLT images, respectively, whereas the worst reproducibility limits ranged from -90% to 73% and -68% to 52%, respectively. CONCLUSION: The reproducibility of estimating TV, mean standardized uptake value, and derived TGV and TPV was found to vary among segmentation algorithms. Some differences between (18)F-FDG and (18)F-FLT scans were observed, mainly because of differences in overall image quality. The smaller reproducibility limits for volume-derived image indices were similar to those for SUV(max), suggesting that the use of appropriate delineation tools should allow the determination of tumor functional volumes in PET images in a repeatable and reproducible fashion.
Authors: Iain Murray; Sarah J Chittenden; Ana M Denis-Bacelar; Cecilia Hindorf; Christopher C Parker; Sue Chua; Glenn D Flux Journal: Eur J Nucl Med Mol Imaging Date: 2017-06-13 Impact factor: 9.236
Authors: Wolfgang A Weber; Constantine A Gatsonis; P David Mozley; Lucy G Hanna; Anthony F Shields; Denise R Aberle; Ramaswamy Govindan; Drew A Torigian; Joel S Karp; Jian Q Michael Yu; Rathan M Subramaniam; Robert A Halvorsen; Barry A Siegel Journal: J Nucl Med Date: 2015-04-23 Impact factor: 10.057
Authors: Catherine S Diefenbach; Joseph M Connors; Jonathan W Friedberg; John P Leonard; Brad S Kahl; Richard F Little; Lawrence Baizer; Andrew M Evens; Richard T Hoppe; Kara M Kelly; Daniel O Persky; Anas Younes; Lale Kostakaglu; Nancy L Bartlett Journal: J Natl Cancer Inst Date: 2016-12-31 Impact factor: 13.506
Authors: Hebert Alberto Vargas; Gem M Kramer; Andrew M Scott; Andrew Weickhardt; Andreas A Meier; Nicole Parada; Bradley J Beattie; John L Humm; Kevin D Staton; Pat B Zanzonico; Serge K Lyashchenko; Jason S Lewis; Maqsood Yaqub; Ramon E Sosa; Alfons J van den Eertwegh; Ian D Davis; Uwe Ackermann; Kunthi Pathmaraj; Robert C Schuit; Albert D Windhorst; Sue Chua; Wolfgang A Weber; Steven M Larson; Howard I Scher; Adriaan A Lammertsma; Otto S Hoekstra; Michael J Morris Journal: J Nucl Med Date: 2018-04-06 Impact factor: 10.057
Authors: Robert L Harrison; Brian F Elston; Robert K Doot; Thomas K Lewellen; David A Mankoff; Paul E Kinahan Journal: Transl Oncol Date: 2014-02-01 Impact factor: 4.243