Use of static and dynamic [18F]-F-DOPA PET parameters for detecting patients with glioma recurrence or progression
Timothée Zaragori, Merwan Ginet, Pierre‐Yves Marie, Véronique Roch, Rachel Grignon, Guillaume Gauchotte, Fabien Rech, Marie Blonski, Zohra Lamiral, Luc Taillandier, Laëtitia Imbert, Antoine Verger
Abstract
Abstract Background Static [ 18 F]-F-DOPA PET images are currently used for identifying patients with glioma recurrence/progression after treatment, although the additional diagnostic value of dynamic parameters remains unknown in this setting. The aim of this study was to evaluate the performances of static and dynamic [ 18 F]-F-DOPA PET parameters for detecting patients with glioma recurrence/progression as well as assess further relationships with patient outcome. Methods Fifty-one consecutive patients who underwent an [ 18 F]-F-DOPA PET for a suspected glioma recurrence/progression at post-resection MRI, were retrospectively included. Static parameters, including mean and maximum tumor-to-normal-brain (TBR) ratios, tumor-to-striatum (TSR) ratios, and metabolic tumor volume (MTV), as well as dynamic parameters with time-to-peak (TTP) values and curve slope, were tested for predicting the following: (1) glioma recurrence/progression at 6 months after the PET exam and (2) survival on longer follow-up. Results All static parameters were significant predictors of glioma recurrence/progression (accuracy ≥ 94%) with all parameters also associated with mean progression-free survival (PFS) in the overall population (all p < 0.001, 29.7 vs. 0.4 months for TBR max , TSR max , and MTV). The curve slope was the sole dynamic PET predictor of glioma recurrence/progression (accuracy = 76.5%) and was also associated with mean PFS ( p < 0.001, 18.0 vs. 0.4 months). However, no additional information was provided relative to static parameters in multivariate analysis. Conclusion Although patients with glioma recurrence/progression can be detected by both static and dynamic [ 18 F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.