Cargando…

Effects of Carbidopa Premedication on (18)F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis

SIMPLE SUMMARY: (18)F-FDOPA PET imaging is routinely used and recommended to assess gliomas. Carbidopa is a peripheral enzyme inhibitor. Carbidopa premedication increases the radiotracer uptake on static images. None of the evidence-based data available to date recommends carbidopa premedication. Ou...

Descripción completa

Detalles Bibliográficos
Autores principales: Bros, Marie, Zaragori, Timothée, Rech, Fabien, Blonski, Marie, Hossu, Gabriela, Taillandier, Luc, Marie, Pierre-Yves, Verger, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582429/
https://www.ncbi.nlm.nih.gov/pubmed/34771504
http://dx.doi.org/10.3390/cancers13215340
Descripción
Sumario:SIMPLE SUMMARY: (18)F-FDOPA PET imaging is routinely used and recommended to assess gliomas. Carbidopa is a peripheral enzyme inhibitor. Carbidopa premedication increases the radiotracer uptake on static images. None of the evidence-based data available to date recommends carbidopa premedication. Our study therefore determined the impact of carbidopa premedication on static, radiomics and dynamic parameters for (18)F-FDOPA PET brain tumor imaging. We show that carbidopa premedication leads to higher SUV and TTP dynamic parameters and impacts SUV-dependent radiomics by the same magnitude in healthy brains and tumors. The carbidopa effect is therefore compensated for by correcting for the tumor-to-healthy-brain ratio, a significant advantage for harmonizing data for multicentric studies. Results were obtained from simulations of time-activity curves using compartmental modeling. ABSTRACT: Purpose: This study aimed to determine the impact of carbidopa premedication on static, dynamic and radiomics parameters of (18)F-FDOPA PET in brain tumors. Methods: The study included 54 patients, 18 of whom received carbidopa, who underwent (18)F-FDOPA PET for newly diagnosed gliomas. SUV-derived, 105 radiomics features and TTP dynamic parameters were extracted from volumes of interest in healthy brains and tumors. Simulation of the effects of carbidopa on time-activity curves were generated. Results: All static and TTP dynamic parameters were significantly higher in healthy brain regions of premedicated patients (ΔSUV(mean) = +53%, ΔTTP = +48%, p < 0.001). Furthermore, carbidopa impacted 81% of radiomics features, of which 92% correlated with SUV(mean) (absolute correlation coefficient ≥ 0.4). In tumors, premedication with carbidopa was an independent predictor of SUV(mean) (ΔSUV(mean) = +52%, p < 0.001) and TTP (ΔTTP = +24%, p = 0.025). All parameters were no longer significantly modified by carbidopa premedication when using ratios to healthy brain. Simulated data confirmed that carbidopa leads to higher tumor TTP values, corrected by the ratios. Conclusion: In (18)F-FDOPA PET, carbidopa induces similarly higher SUV and TTP dynamic parameters and similarly impacts SUV-dependent radiomics in healthy brain and tumor regions, which is compensated for by correcting for the tumor-to-healthy-brain ratio. This is a significant advantage for multicentric study harmonization.