UNLABELLED: The aim of this prospective study was to evaluate the value of (18)F-FDG PET for the assessment of chemotherapy response in patients with non-small cell lung cancer. Furthermore, part of the objective of this study was to compare 2 methods to quantify changes in glucose metabolism. METHODS: In 51 patients, dynamic (18)F-FDG PET was performed before and at 5-8 wk into treatment. Simplified methods to measure glucose metabolism (standardized uptake value [SUV]) and quantitative measures (metabolic rate of glucose [MR(Glu)]), derived from Patlak analysis, were evaluated. The overall survival and progression-free survival with respect to MR(Glu) and SUV were calculated using Kaplan-Meier estimates. Fractional changes in tumor glucose use were stratified by the median value and also the predefined EORTC (European Organization for Research and Treatment of Cancer) metabolic response criteria, and criteria applying cutoff levels similar to those of RECIST (Response Evaluation Criteria in Solid Tumors) were evaluated. RESULTS: When stratifying at the median value of DeltaMR(Glu) and DeltaSUV, the difference in overall survival (P = 0.017 for DeltaMR(Glu), P = 0.018 for DeltaSUV) and progression-free survival (P = 0.002 for DeltaMR(Glu), P = 0.0009 for DeltaSUV) was highly significant. When applying the predefined criteria for metabolic response, the cutoff levels as also used for size measurement (RECIST) showed significant differences for DeltaSUV between response categories in progression-free survival (P = 0.0003) as well as overall survival (P = 0.027). CONCLUSION: The degree of chemotherapy-induced changes in tumor glucose metabolism as determined by (18)F-FDG PET is highly predictive for patient outcome, stratifying patients into groups with widely differing overall survival and progression-free survival probabilities. The use of (18)F-FDG PET for therapy monitoring seems clinically feasible, because simplified methods to measure tumor glucose use (SUV) are sufficiently reliable and can replace more complex, quantitative measures (MR(Glu)) in this patient population.
UNLABELLED: The aim of this prospective study was to evaluate the value of (18)F-FDG PET for the assessment of chemotherapy response in patients with non-small cell lung cancer. Furthermore, part of the objective of this study was to compare 2 methods to quantify changes in glucose metabolism. METHODS: In 51 patients, dynamic (18)F-FDG PET was performed before and at 5-8 wk into treatment. Simplified methods to measure glucose metabolism (standardized uptake value [SUV]) and quantitative measures (metabolic rate of glucose [MR(Glu)]), derived from Patlak analysis, were evaluated. The overall survival and progression-free survival with respect to MR(Glu) and SUV were calculated using Kaplan-Meier estimates. Fractional changes in tumorglucose use were stratified by the median value and also the predefined EORTC (European Organization for Research and Treatment of Cancer) metabolic response criteria, and criteria applying cutoff levels similar to those of RECIST (Response Evaluation Criteria in Solid Tumors) were evaluated. RESULTS: When stratifying at the median value of DeltaMR(Glu) and DeltaSUV, the difference in overall survival (P = 0.017 for DeltaMR(Glu), P = 0.018 for DeltaSUV) and progression-free survival (P = 0.002 for DeltaMR(Glu), P = 0.0009 for DeltaSUV) was highly significant. When applying the predefined criteria for metabolic response, the cutoff levels as also used for size measurement (RECIST) showed significant differences for DeltaSUV between response categories in progression-free survival (P = 0.0003) as well as overall survival (P = 0.027). CONCLUSION: The degree of chemotherapy-induced changes in tumor glucose metabolism as determined by (18)F-FDG PET is highly predictive for patient outcome, stratifying patients into groups with widely differing overall survival and progression-free survival probabilities. The use of (18)F-FDG PET for therapy monitoring seems clinically feasible, because simplified methods to measure tumorglucose use (SUV) are sufficiently reliable and can replace more complex, quantitative measures (MR(Glu)) in this patient population.
Authors: Bernadette G Dijkman; Olga C J Schuurbiers; Dennis Vriens; Monika Looijen-Salamon; Johan Bussink; Johanna N H Timmer-Bonte; Miranda M Snoeren; Wim J G Oyen; Henricus F M van der Heijden; Lioe-Fee de Geus-Oei Journal: Eur J Nucl Med Mol Imaging Date: 2010-06-10 Impact factor: 9.236
Authors: Lieselot Brepoels; Marijke De Saint-Hubert; Sigrid Stroobants; Gregor Verhoef; Jan Balzarini; Luc Mortelmans; Felix M Mottaghy Journal: Eur J Nucl Med Mol Imaging Date: 2010-05-12 Impact factor: 9.236
Authors: Gianpiero Manca; Eleonora Vanzi; Domenico Rubello; Francesco Giammarile; Gaia Grassetto; Ka Kit Wong; Alan C Perkins; Patrick M Colletti; Duccio Volterrani Journal: Eur J Nucl Med Mol Imaging Date: 2016-01-19 Impact factor: 9.236
Authors: Klaus Strobel; Reinhard Dummer; Hans C Steinert; Katrin Baumann Conzett; Karin Schad; Marisol Pérez Lago; Jan D Soyka; P Veit-Haibach; Burkhardt Seifert; V Kalff Journal: Eur J Nucl Med Mol Imaging Date: 2008-05-06 Impact factor: 9.236
Authors: Willem Grootjans; Lioe-Fee de Geus-Oei; Antoi P W Meeuwis; Charlotte S van der Vos; Martin Gotthardt; Wim J G Oyen; Eric P Visser Journal: Eur Radiol Date: 2014-08-06 Impact factor: 5.315
Authors: William N William; Apar Pataer; Neda Kalhor; Arlene M Correa; David C Rice; Ignacio I Wistuba; John Heymach; J Jack Lee; Edward S Kim; Reginald Munden; Kathryn A Gold; Vassiliki Papadimitrakopoulou; Stephen G Swisher; Jeremy J Erasmus Journal: J Thorac Oncol Date: 2013-02 Impact factor: 15.609