Timothée Zaragori1,2, Merwan Ginet1, Pierre-Yves Marie1,3, Véronique Roch1, Rachel Grignon1, Guillaume Gauchotte4,5, Fabien Rech6,7, Marie Blonski7,8, Zohra Lamiral3, Luc Taillandier7,8, Laëtitia Imbert1,2, Antoine Verger9,10. 1. Department of Nuclear Medicine & Nancyclotep Imaging platform, Université de Lorraine, CHRU-Nancy, F-54000, Nancy, France. 2. IADI, INSERM, UMR 1254, Université de Lorraine, F-54000, Nancy, France. 3. INSERM, U1116, Université de Lorraine, F-54000, Nancy, France. 4. Department of Pathology, Université de Lorraine, CHRU-Nancy, F-54000, Nancy, France. 5. INSERM U1256, Université de Lorraine, F-54000, Nancy, France. 6. Department of Neurosurgery, Université de Lorraine, CHRU-Nancy, F-54000, Nancy, France. 7. Centre de Recherche en Automatique de Nancy CRAN, CNRS UMR 7039, Université de Lorraine, F-54000, Nancy, France. 8. Department of Neuro-oncology, Université de Lorraine, CHRU-Nancy, F-54000, Nancy, France. 9. Department of Nuclear Medicine & Nancyclotep Imaging platform, Université de Lorraine, CHRU-Nancy, F-54000, Nancy, France. a.verger@chru-nancy.fr. 10. IADI, INSERM, UMR 1254, Université de Lorraine, F-54000, Nancy, France. a.verger@chru-nancy.fr.
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 TBRmax, TSRmax, 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.
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 TBRmax, TSRmax, 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.
Authors: Mariam Aboian; Ramon Barajas; Julia Shatalov; Vahid Ravanfar; Emma Bahroos; Elizabeth Tong; Jennie W Taylor; N Oberheim Bush; Patricia Sneed; Youngho Seo; Soonmee Cha; Miguel Hernandez-Pampaloni Journal: Neurooncol Pract Date: 2020-10-14
Authors: David Sipos; Zoltan László; Zoltan Tóth; Peter Kovács; Jozsef Tollár; Akos Gulybán; Ferenc Lakosi; Imre Repa; Arpad Kovács Journal: Front Oncol Date: 2021-07-06 Impact factor: 6.244