Use of static and dynamic 18F-FDopa PET parameters for detecting patients with glioma recurrence or progression

Abstract

Background: Static 18 F-FDopa 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 the present study was to evaluate the performances of static and dynamic 18 F-FDopa PET parameters for detecting patients with glioma recurrence/progression as well as to assess further relationships with patient outcome. Fifty-one consecutive patients who underwent an 18 F-FDopa 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), 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: 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 a 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 the 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-FDopa PET parameters, most of this diagnostic information can be achieved by conventional static parameters.

Dynamic amino-acid PET in neuro-oncology: a prognostic tool becomes essential

“…The prognostic information obtained at initial diagnosis is nevertheless more complicated to apply to glioma recurrences. The prognostic value of static parameters for this particular indication appears to be higher than prognostic values exclusively determined from dynamic parameters [29,30].…”