UNLABELLED: Interim (18)F-FDG PET (after 1-4 cycles of chemotherapy) may be useful for tailoring a risk-adapted therapeutic strategy in lymphoma. The purpose of this study was to investigate whether semiquantification of standardized uptake values (SUVs) may help to improve the prognostic value of (18)F-FDG PET, compared with visual analysis, after 4 cycles of chemotherapy. METHODS: In a previous report, we showed that a 65.7% reduction in maximal SUV (SUVmax) between baseline (PET0) and 2 cycles of chemotherapy (PET2) better predicted event-free survival in 92 prospective patients with diffuse large B-cell lymphoma, by reducing false-positive interpretation of visual analysis. Eighty patients also underwent (18)F-FDG PET after induction had been completed, at 4 cycles of chemotherapy (PET4). Images were interpreted visually (as negative or positive) and by computing the optimal percentage of SUVmax reduction between PET0 and PET4. Survival curves were estimated using Kaplan-Meier analysis and compared using the log-rank test. Median follow-up was 41 mo. RESULTS: With visual analysis, the 2-y estimate for event-free survival was 82% in the PET4-negative group, compared with 25% in the PET4-positive group (P < 0.0001, accuracy of predicting event-free survival, 81.3%). An optimal cutoff of 72.9% SUVmax reduction from PET0 to PET4 yielded a 2-y estimate for event-free survival of 79% in patients with reduction of more than 72.9%, versus 32% in those with reduction of 72.9% or less (P < 0.0001; accuracy of predicting event-free survival, 77.5%). CONCLUSION: Although SUV semiquantification helps reduce false-positive interim (18)F-FDG PET interpretations at 2 cycles, its performance is equivalent to visual analysis at 4 cycles, when most of the therapeutic effect has occurred upstream. This approach may be useful for objectively tailoring consolidation strategies.
UNLABELLED: Interim (18)F-FDG PET (after 1-4 cycles of chemotherapy) may be useful for tailoring a risk-adapted therapeutic strategy in lymphoma. The purpose of this study was to investigate whether semiquantification of standardized uptake values (SUVs) may help to improve the prognostic value of (18)F-FDG PET, compared with visual analysis, after 4 cycles of chemotherapy. METHODS: In a previous report, we showed that a 65.7% reduction in maximal SUV (SUVmax) between baseline (PET0) and 2 cycles of chemotherapy (PET2) better predicted event-free survival in 92 prospective patients with diffuse large B-cell lymphoma, by reducing false-positive interpretation of visual analysis. Eighty patients also underwent (18)F-FDG PET after induction had been completed, at 4 cycles of chemotherapy (PET4). Images were interpreted visually (as negative or positive) and by computing the optimal percentage of SUVmax reduction between PET0 and PET4. Survival curves were estimated using Kaplan-Meier analysis and compared using the log-rank test. Median follow-up was 41 mo. RESULTS: With visual analysis, the 2-y estimate for event-free survival was 82% in the PET4-negative group, compared with 25% in the PET4-positive group (P < 0.0001, accuracy of predicting event-free survival, 81.3%). An optimal cutoff of 72.9% SUVmax reduction from PET0 to PET4 yielded a 2-y estimate for event-free survival of 79% in patients with reduction of more than 72.9%, versus 32% in those with reduction of 72.9% or less (P < 0.0001; accuracy of predicting event-free survival, 77.5%). CONCLUSION: Although SUV semiquantification helps reduce false-positive interim (18)F-FDG PET interpretations at 2 cycles, its performance is equivalent to visual analysis at 4 cycles, when most of the therapeutic effect has occurred upstream. This approach may be useful for objectively tailoring consolidation strategies.
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: Bouthaina S Dabaja; Ann M Vanderplas; Allison L Crosby-Thompson; Gregory A Abel; Myron S Czuczman; Jonathan W Friedberg; Leo I Gordon; Mark Kaminski; Joyce Niland; Michael Millenson; Auayporn P Nademanee; Andrew Zelenetz; Ann S LaCasce; Maria Alma Rodriguez Journal: Cancer Date: 2014-12-09 Impact factor: 6.860