UNLABELLED: PET can be used to monitor response during chemotherapy and assess biologic target volumes for radiotherapy. Previous simulation studies have shown that the performance of various automatic or semiautomatic tumor delineation methods depends on image characteristics. The purpose of this study was to assess test-retest variability of tumor delineation methods, with emphasis on the effects of several image characteristics (e.g., resolution and contrast). METHODS: Baseline test-retest data from 19 non-small cell lung cancer patients were obtained using (18)F-FDG (n = 10) and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) (n = 9). Images were reconstructed with varying spatial resolution and contrast. Six different types of tumor delineation methods, based on various thresholds or on a gradient, were applied to all datasets. Test-retest variability of metabolic volume and standardized uptake value (SUV) was determined. RESULTS: For both tracers, size of metabolic volume and test-retest variability of both metabolic volume and SUV were affected by the image characteristics and tumor delineation method used. The median volume test-retest variability ranged from 8.3% to 23% and from 7.4% to 29% for (18)F-FDG and (18)F-FLT, respectively. For all image characteristics studied, larger differences (≤10-fold higher) were seen in test-retest variability of metabolic volume than in SUV. CONCLUSION: Test-retest variability of both metabolic volume and SUV varied with tumor delineation method, radiotracer, and image characteristics. The results indicate that a careful optimization of imaging and delineation method parameters is needed when metabolic volume is used, for example, as a response assessment parameter.
UNLABELLED: PET can be used to monitor response during chemotherapy and assess biologic target volumes for radiotherapy. Previous simulation studies have shown that the performance of various automatic or semiautomatic tumor delineation methods depends on image characteristics. The purpose of this study was to assess test-retest variability of tumor delineation methods, with emphasis on the effects of several image characteristics (e.g., resolution and contrast). METHODS: Baseline test-retest data from 19 non-small cell lung cancerpatients were obtained using (18)F-FDG (n = 10) and 3'-deoxy-3'-(18)F-fluorothymidine ((18)F-FLT) (n = 9). Images were reconstructed with varying spatial resolution and contrast. Six different types of tumor delineation methods, based on various thresholds or on a gradient, were applied to all datasets. Test-retest variability of metabolic volume and standardized uptake value (SUV) was determined. RESULTS: For both tracers, size of metabolic volume and test-retest variability of both metabolic volume and SUV were affected by the image characteristics and tumor delineation method used. The median volume test-retest variability ranged from 8.3% to 23% and from 7.4% to 29% for (18)F-FDG and (18)F-FLT, respectively. For all image characteristics studied, larger differences (≤10-fold higher) were seen in test-retest variability of metabolic volume than in SUV. CONCLUSION: Test-retest variability of both metabolic volume and SUV varied with tumor delineation method, radiotracer, and image characteristics. The results indicate that a careful optimization of imaging and delineation method parameters is needed when metabolic volume is used, for example, as a response assessment parameter.
Authors: Madhava P Aryal; Tavarekere N Nagaraja; Stephen L Brown; Mei Lu; Hassan Bagher-Ebadian; Guangliang Ding; Swayamprava Panda; Kelly Keenan; Glauber Cabral; Tom Mikkelsen; James R Ewing Journal: NMR Biomed Date: 2014-08-14 Impact factor: 4.044
Authors: Patsuree Cheebsumon; Floris Hp van Velden; Maqsood Yaqub; Corneline J Hoekstra; Linda M Velasquez; Wendy Hayes; Otto S Hoekstra; Adriaan A Lammertsma; Ronald Boellaard Journal: EJNMMI Res Date: 2011-12-14 Impact factor: 3.138
Authors: Hyung-Jun Im; Meiyappan Solaiyappan; Inki Lee; Tyler Bradshaw; Najat C Daw; Fariba Navid; Barry L Shulkin; Steve Y Cho Journal: Am J Nucl Med Mol Imaging Date: 2018-12-20
Authors: Virginie Frings; Adrianus J de Langen; Maqsood Yaqub; Robert C Schuit; Astrid A M van der Veldt; Otto S Hoekstra; Egbert F Smit; Ronald Boellaard Journal: Mol Imaging Biol Date: 2014-02 Impact factor: 3.488
Authors: Oluwaseun A Odewole; Oyeladun A Oyenuga; Funmilayo Tade; Bital Savir-Baruch; Peter T Nieh; Viraj Master; Zhengjia Chen; Xiaojing Wang; Ashesh B Jani; Leah M Bellamy; Raghuveer K Halkar; Mark M Goodman; David M Schuster Journal: Mol Imaging Biol Date: 2015-04 Impact factor: 3.488