UNLABELLED: The purpose of this study was to assess the prognostic value of early (18)F-FDG PET using standardized uptake value (SUV) compared with visual analysis in patients with diffuse large B-cell lymphoma (DLBCL). METHODS: Ninety-two patients with newly diagnosed DLBCL underwent (18)F-FDG PET prospectively before and after 2 cycles of chemotherapy (at midtherapy). Maximum SUV (SUVmax) and mean SUV (SUVmean) normalized to body weight and body surface area, as well as tumor-to-normal ratios, were computed on the most intense uptake areas. The SUVs, tumor-to-normal ratios, and their changes over time were compared with visual analysis for predicting event-free survival (EFS) and overall survival, using receiver-operating-characteristic (ROC) analysis. Survival curves were estimated with Kaplan-Meier analysis and compared using the log-rank test. RESULTS: With visual analysis, the accuracy of early PET to predict EFS was 65.2%. The 2-y estimate for EFS was 51% (95% confidence interval [CI], 34%-68%) in the PET-positive group compared with 79% (95% CI, 68%-90%) in the PET-negative group (P = 0.009). An optimal cutoff value of 65.7% SUVmax reduction from baseline to midtherapy obtained from ROC analysis yielded an accuracy of 76.1% to predict EFS. The 2-y estimate for EFS was 21% (95% CI, 0%-42%) in patients with SUVmax reduction <or= 65.7% compared with 79% (95% CI, 69%-88%) in those with reduction > 65.7% (P < 0.0001). Fourteen patients considered as positive on visual analysis could have been reclassified as good responders. CONCLUSION: SUV-based assessment of therapeutic response during first-line chemotherapy improves the prognostic value of early (18)F-FDG PET compared with visual analysis in DLBCL.
UNLABELLED: The purpose of this study was to assess the prognostic value of early (18)F-FDG PET using standardized uptake value (SUV) compared with visual analysis in patients with diffuse large B-cell lymphoma (DLBCL). METHODS: Ninety-two patients with newly diagnosed DLBCL underwent (18)F-FDG PET prospectively before and after 2 cycles of chemotherapy (at midtherapy). Maximum SUV (SUVmax) and mean SUV (SUVmean) normalized to body weight and body surface area, as well as tumor-to-normal ratios, were computed on the most intense uptake areas. The SUVs, tumor-to-normal ratios, and their changes over time were compared with visual analysis for predicting event-free survival (EFS) and overall survival, using receiver-operating-characteristic (ROC) analysis. Survival curves were estimated with Kaplan-Meier analysis and compared using the log-rank test. RESULTS: With visual analysis, the accuracy of early PET to predict EFS was 65.2%. The 2-y estimate for EFS was 51% (95% confidence interval [CI], 34%-68%) in the PET-positive group compared with 79% (95% CI, 68%-90%) in the PET-negative group (P = 0.009). An optimal cutoff value of 65.7% SUVmax reduction from baseline to midtherapy obtained from ROC analysis yielded an accuracy of 76.1% to predict EFS. The 2-y estimate for EFS was 21% (95% CI, 0%-42%) in patients with SUVmax reduction <or= 65.7% compared with 79% (95% CI, 69%-88%) in those with reduction > 65.7% (P < 0.0001). Fourteen patients considered as positive on visual analysis could have been reclassified as good responders. CONCLUSION: SUV-based assessment of therapeutic response during first-line chemotherapy improves the prognostic value of early (18)F-FDG PET compared with visual analysis in DLBCL.
Authors: Sally F Barrington; Wendi Qian; Edward J Somer; Antonella Franceschetto; Bruno Bagni; Eva Brun; Helén Almquist; Annika Loft; Liselotte Højgaard; Massimo Federico; Andrea Gallamini; Paul Smith; Peter Johnson; John Radford; Michael J O'Doherty Journal: Eur J Nucl Med Mol Imaging Date: 2010-05-27 Impact factor: 9.236
Authors: Mark Hertzberg; Maher K Gandhi; Judith Trotman; Belinda Butcher; John Taper; Amanda Johnston; Devinder Gill; Shir-Jing Ho; Gavin Cull; Keith Fay; Geoff Chong; Andrew Grigg; Ian D Lewis; Sam Milliken; William Renwick; Uwe Hahn; Robin Filshie; George Kannourakis; Anne-Marie Watson; Pauline Warburton; Andrew Wirth; John F Seymour; Michael S Hofman; Rodney J Hicks Journal: Haematologica Date: 2016-11-10 Impact factor: 9.941
Authors: Christopher Breault; Jonathan Piper; Abhinay D Joshi; Sara D Pirozzi; Aaron S Nelson; Ming Lu; Michael J Pontecorvo; Mark A Mintun; Michael D Devous Journal: Am J Nucl Med Mol Imaging Date: 2017-07-15
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