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Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

PURPOSE: Dynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[(18)F]fluoro-l-DOPA ((18)F-FDOPA) kinetic model ha...

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Autores principales: Zaragori, Timothée, Doyen, Matthieu, Rech, Fabien, Blonski, Marie, Taillandier, Luc, Imbert, Laëtitia, Verger, Antoine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523996/
https://www.ncbi.nlm.nih.gov/pubmed/34676168
http://dx.doi.org/10.3389/fonc.2021.735257
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author Zaragori, Timothée
Doyen, Matthieu
Rech, Fabien
Blonski, Marie
Taillandier, Luc
Imbert, Laëtitia
Verger, Antoine
author_facet Zaragori, Timothée
Doyen, Matthieu
Rech, Fabien
Blonski, Marie
Taillandier, Luc
Imbert, Laëtitia
Verger, Antoine
author_sort Zaragori, Timothée
collection PubMed
description PURPOSE: Dynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[(18)F]fluoro-l-DOPA ((18)F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time–activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models. METHODS: Thirty-seven tumour time–activity curves from (18)F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3–79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for (18)F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis. RESULTS: The SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered. CONCLUSIONS: The SQ model circumvents the complexities of the (18)F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of (18)F-FDOPA PET images in newly diagnosed gliomas.
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spelling pubmed-85239962021-10-20 Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient? Zaragori, Timothée Doyen, Matthieu Rech, Fabien Blonski, Marie Taillandier, Luc Imbert, Laëtitia Verger, Antoine Front Oncol Oncology PURPOSE: Dynamic amino acid positron emission tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value in the noninvasive prediction of isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[(18)F]fluoro-l-DOPA ((18)F-FDOPA) kinetic model has an underlying complexity, while previous studies have predominantly used a semiquantitative dynamic analysis. Our study addresses whether a semiquantitative analysis can capture all the relevant information contained in time–activity curves for predicting the presence of IDH mutations compared to the more sophisticated graphical and compartmental models. METHODS: Thirty-seven tumour time–activity curves from (18)F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age = 58.3 years, range = 20.3–79.9 years, 16 women, 16 IDH-wild type) were analyzed with a semiquantitative model based on classical parameters, with (SQ) or without (Ref SQ) a reference region, or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for (18)F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed with an area under the curve (AUC) comparison using multivariate analysis of all the parameters included in the model. Moreover, each extracted parameter was assessed in a univariate analysis by a receiver operating characteristic curve analysis. RESULTS: The SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performance to the other models with AUCs of 0.752, 0.814, 0.693, 0.786, and 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan, and 2TCM (p ≥ 0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered. CONCLUSIONS: The SQ model circumvents the complexities of the (18)F-FDOPA kinetic model and yields similar performance in predicting IDH mutations when compared to the other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of (18)F-FDOPA PET images in newly diagnosed gliomas. Frontiers Media S.A. 2021-10-05 /pmc/articles/PMC8523996/ /pubmed/34676168 http://dx.doi.org/10.3389/fonc.2021.735257 Text en Copyright © 2021 Zaragori, Doyen, Rech, Blonski, Taillandier, Imbert and Verger https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Zaragori, Timothée
Doyen, Matthieu
Rech, Fabien
Blonski, Marie
Taillandier, Luc
Imbert, Laëtitia
Verger, Antoine
Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title_full Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title_fullStr Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title_full_unstemmed Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title_short Dynamic (18)F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?
title_sort dynamic (18)f-fdopa pet imaging for newly diagnosed gliomas: is a semiquantitative model sufficient?
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8523996/
https://www.ncbi.nlm.nih.gov/pubmed/34676168
http://dx.doi.org/10.3389/fonc.2021.735257
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