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

Abstract

Background: Static [¹⁸F]-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 [¹⁸F]-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 [¹⁸F]-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 [¹⁸F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.

Keywords: Amino-acid PET; Dynamic analysis; Glioma; Recurrence; [18F]-F-DOPA.

Fig. 1

Receiver operating characteristic (ROC) curves for TBR_max, TBR_mean, TSR_max, TSR_mean, MTV, and slope parameters for differentiating between recurrent or progressive gliomas and treatment-related changes in high-grade gliomas (n = 33, left panel) and in low-grade gliomas (n = 18, right panel). Significant ROC curves are represented in solid lines

Fig. 2

Kaplan-Meier survival plots for the prediction of progression-free survival using maximal tumor-to-background ratios (upper left panel), mean tumor-to-background ratios (middle left panel), metabolic tumor volume (lower left panel), maximal tumor-to-striatum ratio (upper right panel), mean tumor-to-striatum ratio (middle right panel), and slope (lower right panel) as discriminators. Corresponding log-rank test adjusted p values are < 0.0001 for all presented curves

Fig. 3

Representative examples of patients with or without glioma recurrence/progression investigated with [¹⁸F]-F-DOPA PET imaging, with axial slices of [¹⁸F]-F-DOPA PET (left column), dynamic TBR_mean curves (middle column) providing the time-to-peak delay-time (light blue dotted line), and the 10-to-30 min slope (dark blue dotted line), along with, for illustrative purposes, the same slice location recorded on a FLAIR MRI sequence (right column). a 51-year-old woman with no recurrent or progressive glioma (TBR_mean = 0.8, TBR_max = 1.1, TSR_mean = 0.5, TSR_max = 0.7, MTV = 0 mL, TTP = 30 min, and slope = 0.31 h⁻¹). b 46-year-old woman with a progressive IDH-wildtype glioblastoma (TBR_mean = 1.9, TBR_max = 2.5, TSR_mean = 1.2, TSR_max = 1.6, MTV = 6.13 mL, TTP = 7.6 min, and slope = − 1.22 h⁻¹)