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Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudopro...

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Autores principales: Sidibe, Ingrid, Tensaouti, Fatima, Roques, Margaux, Cohen-Jonathan-Moyal, Elizabeth, Laprie, Anne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869397/
https://www.ncbi.nlm.nih.gov/pubmed/35203493
http://dx.doi.org/10.3390/biomedicines10020285
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author Sidibe, Ingrid
Tensaouti, Fatima
Roques, Margaux
Cohen-Jonathan-Moyal, Elizabeth
Laprie, Anne
author_facet Sidibe, Ingrid
Tensaouti, Fatima
Roques, Margaux
Cohen-Jonathan-Moyal, Elizabeth
Laprie, Anne
author_sort Sidibe, Ingrid
collection PubMed
description Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis.
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spelling pubmed-88693972022-02-25 Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review Sidibe, Ingrid Tensaouti, Fatima Roques, Margaux Cohen-Jonathan-Moyal, Elizabeth Laprie, Anne Biomedicines Systematic Review Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis. MDPI 2022-01-26 /pmc/articles/PMC8869397/ /pubmed/35203493 http://dx.doi.org/10.3390/biomedicines10020285 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Systematic Review
Sidibe, Ingrid
Tensaouti, Fatima
Roques, Margaux
Cohen-Jonathan-Moyal, Elizabeth
Laprie, Anne
Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title_full Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title_fullStr Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title_full_unstemmed Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title_short Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review
title_sort pseudoprogression in glioblastoma: role of metabolic and functional mri-systematic review
topic Systematic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8869397/
https://www.ncbi.nlm.nih.gov/pubmed/35203493
http://dx.doi.org/10.3390/biomedicines10020285
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