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Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F-FDG PET.
Tylski P., Stute S., Grotus N., Doyeux K., Hapdey S. et al
The Journal of Nuclear Medicine 51, 2 (2010) 268-76 - http://www.hal.inserm.fr/inserm-00466261
Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18)F-FDG PET.
Perrine Tylski ()1, Simon Stute1, Nicolas Grotus1, Kaya Doyeux2, Sébastien Hapdey2, Isabelle Gardin2, Bruno Vanderlinden3, Irène Buvat1
1 :  IMNC - Imagerie et Modélisation en Neurobiologie et Cancérologie
CNRS : UMR8165 – IN2P3 – Université Paris XI - Paris Sud – Université Paris VII - Paris Diderot
BATIMENT 104 15 Rue Georges Clémenceau 91406 ORSAY CEDEX
2 :  LITIS - Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes
Institut National des Sciences Appliquées [INSA] - Rouen – Université du Havre – Université de Rouen : EA4108
Avenue de l'Université UFR des Sciences et Techniques 76800 Saint-Etienne du Rouvray
3 :  Nuclear Medicine Department
Université Libre de Bruxelles
Bordet Institute, Brussels
In (18)F-FDG PET, tumors are often characterized by their metabolically active volume and standardized uptake value (SUV). However, many approaches have been proposed to estimate tumor volume and SUV from (18)F-FDG PET images, none of them being widely agreed upon. We assessed the accuracy and robustness of 5 methods for tumor volume estimates and of 10 methods for SUV estimates in a large variety of configurations. METHODS: PET acquisitions of an anthropomorphic phantom containing 17 spheres (volumes between 0.43 and 97 mL, sphere-to-surrounding-activity concentration ratios between 2 and 68) were used. Forty-one nonspheric tumors (volumes between 0.6 and 92 mL, SUV of 2, 4, and 8) were also simulated and inserted in a real patient (18)F-FDG PET scan. Four threshold-based methods (including one, T(bgd), accounting for background activity) and a model-based method (Fit) described in the literature were used for tumor volume measurements. The mean SUV in the resulting volumes were calculated, without and with partial-volume effect (PVE) correction, as well as the maximum SUV (SUV(max)). The parameters involved in the tumor segmentation and SUV estimation methods were optimized using 3 approaches, corresponding to getting the best of each method or testing each method in more realistic situations in which the parameters cannot be perfectly optimized. RESULTS: In the phantom and simulated data, the T(bgd) and Fit methods yielded the most accurate volume estimates, with mean errors of 2% +/- 11% and -8% +/- 21% in the most realistic situations. Considering the simulated data, all SUV not corrected for PVE had a mean bias between -31% and -46%, much larger than the bias observed with SUV(max) (-11% +/- 23%) or with the PVE-corrected SUV based on T(bgd) and Fit (-2% +/- 10% and 3% +/- 24%). CONCLUSION: The method used to estimate tumor volume and SUV greatly affects the reliability of the estimates. The T(bgd) and Fit methods yielded low errors in volume estimates in a broad range of situations. The PVE-corrected SUV based on T(bgd) and Fit were more accurate and reproducible than SUV(max).
Sciences du Vivant/Neurosciences

The Journal of Nuclear Medicine
Articles dans des revues avec comité de lecture

Algorithms – Artificial Intelligence – Computer Simulation – Differential Threshold – Fluorodeoxyglucose F18 – Humans – Image Enhancement – Image Interpretation – Computer-Assisted – Models – Biological – Neoplasms – Pattern Recognition – Automated – Phantoms – Imaging – Positron-Emission Tomography – Radiopharmaceuticals – Reproducibility of Results – Sensitivity and Specificity – Algorithms – Fluorine Radioisotopes – Liver Neoplasms – Lung Neoplasms – Monte Carlo Method – Software