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Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone

High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differen...

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Autores principales: Csutak, Csaba, Ștefan, Paul-Andrei, Lenghel, Lavinia Manuela, Moroșanu, Cezar Octavian, Lupean, Roxana-Adelina, Șimonca, Larisa, Mihu, Carmen Mihaela, Lebovici, Andrei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565295/
https://www.ncbi.nlm.nih.gov/pubmed/32947822
http://dx.doi.org/10.3390/brainsci10090638
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author Csutak, Csaba
Ștefan, Paul-Andrei
Lenghel, Lavinia Manuela
Moroșanu, Cezar Octavian
Lupean, Roxana-Adelina
Șimonca, Larisa
Mihu, Carmen Mihaela
Lebovici, Andrei
author_facet Csutak, Csaba
Ștefan, Paul-Andrei
Lenghel, Lavinia Manuela
Moroșanu, Cezar Octavian
Lupean, Roxana-Adelina
Șimonca, Larisa
Mihu, Carmen Mihaela
Lebovici, Andrei
author_sort Csutak, Csaba
collection PubMed
description High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs.
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spelling pubmed-75652952020-10-26 Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone Csutak, Csaba Ștefan, Paul-Andrei Lenghel, Lavinia Manuela Moroșanu, Cezar Octavian Lupean, Roxana-Adelina Șimonca, Larisa Mihu, Carmen Mihaela Lebovici, Andrei Brain Sci Article High-grade gliomas (HGGs) and solitary brain metastases (BMs) have similar imaging appearances, which often leads to misclassification. In HGGs, the surrounding tissues show malignant invasion, while BMs tend to displace the adjacent area. The surrounding edema produced by the two cannot be differentiated by conventional magnetic resonance (MRI) examinations. Forty-two patients with pathology-proven brain tumors who underwent conventional pretreatment MRIs were retrospectively included (HGGs, n = 16; BMs, n = 26). Texture analysis of the peritumoral zone was performed on the T2-weighted sequence using dedicated software. The most discriminative texture features were selected using the Fisher and the probability of classification error and average correlation coefficients. The ability of texture parameters to distinguish between HGGs and BMs was evaluated through univariate, receiver operating, and multivariate analyses. The first percentile and wavelet energy texture parameters were independent predictors of HGGs (75–87.5% sensitivity, 53.85–88.46% specificity). The prediction model consisting of all parameters that showed statistically significant results at the univariate analysis was able to identify HGGs with 100% sensitivity and 66.7% specificity. Texture analysis can provide a quantitative description of the peritumoral zone encountered in solitary brain tumors, that can provide adequate differentiation between HGGs and BMs. MDPI 2020-09-16 /pmc/articles/PMC7565295/ /pubmed/32947822 http://dx.doi.org/10.3390/brainsci10090638 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Csutak, Csaba
Ștefan, Paul-Andrei
Lenghel, Lavinia Manuela
Moroșanu, Cezar Octavian
Lupean, Roxana-Adelina
Șimonca, Larisa
Mihu, Carmen Mihaela
Lebovici, Andrei
Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title_full Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title_fullStr Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title_full_unstemmed Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title_short Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone
title_sort differentiating high-grade gliomas from brain metastases at magnetic resonance: the role of texture analysis of the peritumoral zone
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565295/
https://www.ncbi.nlm.nih.gov/pubmed/32947822
http://dx.doi.org/10.3390/brainsci10090638
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