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Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas

SIMPLE SUMMARY: Over the past few years, radiomics-based tissue characterization has demonstrated its potential for non-invasive prediction of the genetic profile and grading in cerebral gliomas using multiparametric MRI. The aim of our study was to investigate the feasibility and diagnostic accurac...

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Autores principales: Haubold, Johannes, Hosch, René, Parmar, Vicky, Glas, Martin, Guberina, Nika, Catalano, Onofrio Antonio, Pierscianek, Daniela, Wrede, Karsten, Deuschl, Cornelius, Forsting, Michael, Nensa, Felix, Flaschel, Nils, Umutlu, Lale
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699054/
https://www.ncbi.nlm.nih.gov/pubmed/34944806
http://dx.doi.org/10.3390/cancers13246186
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author Haubold, Johannes
Hosch, René
Parmar, Vicky
Glas, Martin
Guberina, Nika
Catalano, Onofrio Antonio
Pierscianek, Daniela
Wrede, Karsten
Deuschl, Cornelius
Forsting, Michael
Nensa, Felix
Flaschel, Nils
Umutlu, Lale
author_facet Haubold, Johannes
Hosch, René
Parmar, Vicky
Glas, Martin
Guberina, Nika
Catalano, Onofrio Antonio
Pierscianek, Daniela
Wrede, Karsten
Deuschl, Cornelius
Forsting, Michael
Nensa, Felix
Flaschel, Nils
Umutlu, Lale
author_sort Haubold, Johannes
collection PubMed
description SIMPLE SUMMARY: Over the past few years, radiomics-based tissue characterization has demonstrated its potential for non-invasive prediction of the genetic profile and grading in cerebral gliomas using multiparametric MRI. The aim of our study was to investigate the feasibility and diagnostic accuracy of a fully automated radiomics analysis based on a simplified MR protocol derived from various scanner systems to prospectively ease the transition of radiomics-based non-invasive tissue sampling into clinical practice. Using an MRI with non-contrast and post-contrast T1-weighted sequences and FLAIR, our workflow automatically predicts the IDH1/2 mutation, the ATRX expression loss, the 1p19q co-deletion and the MGMT methylation status. It also effectively differentiates low-grade from high-grade gliomas. In summary, the present study demonstrated that a fully automated prediction of grading and the genetic profile of cerebral gliomas could be performed with our proposed method using a simplified MRI protocol that is robust to variations in scanner systems, imaging parameters and field strength. ABSTRACT: Objective: The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. Methods: MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. Results: The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. Conclusion: This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding.
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spelling pubmed-86990542021-12-24 Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas Haubold, Johannes Hosch, René Parmar, Vicky Glas, Martin Guberina, Nika Catalano, Onofrio Antonio Pierscianek, Daniela Wrede, Karsten Deuschl, Cornelius Forsting, Michael Nensa, Felix Flaschel, Nils Umutlu, Lale Cancers (Basel) Article SIMPLE SUMMARY: Over the past few years, radiomics-based tissue characterization has demonstrated its potential for non-invasive prediction of the genetic profile and grading in cerebral gliomas using multiparametric MRI. The aim of our study was to investigate the feasibility and diagnostic accuracy of a fully automated radiomics analysis based on a simplified MR protocol derived from various scanner systems to prospectively ease the transition of radiomics-based non-invasive tissue sampling into clinical practice. Using an MRI with non-contrast and post-contrast T1-weighted sequences and FLAIR, our workflow automatically predicts the IDH1/2 mutation, the ATRX expression loss, the 1p19q co-deletion and the MGMT methylation status. It also effectively differentiates low-grade from high-grade gliomas. In summary, the present study demonstrated that a fully automated prediction of grading and the genetic profile of cerebral gliomas could be performed with our proposed method using a simplified MRI protocol that is robust to variations in scanner systems, imaging parameters and field strength. ABSTRACT: Objective: The aim of this study was to investigate the diagnostic accuracy of a radiomics analysis based on a fully automated segmentation and a simplified and robust MR imaging protocol to provide a comprehensive analysis of the genetic profile and grading of cerebral gliomas for everyday clinical use. Methods: MRI examinations of 217 therapy-naïve patients with cerebral gliomas, each comprising a non-contrast T1-weighted, FLAIR and contrast-enhanced T1-weighted sequence, were included in the study. In addition, clinical and laboratory parameters were incorporated into the analysis. The BraTS 2019 pretrained DeepMedic network was used for automated segmentation. The segmentations generated by DeepMedic were evaluated with 200 manual segmentations with a DICE score of 0.8082 ± 0.1321. Subsequently, the radiomics signatures were utilized to predict the genetic profile of ATRX, IDH1/2, MGMT and 1p19q co-deletion, as well as differentiating low-grade glioma from high-grade glioma. Results: The network provided an AUC (validation/test) for the differentiation between low-grade gliomas vs. high-grade gliomas of 0.981 ± 0.015/0.885 ± 0.02. The best results were achieved for the prediction of the ATRX expression loss with AUCs of 0.979 ± 0.028/0.923 ± 0.045, followed by 0.929 ± 0.042/0.861 ± 0.023 for the prediction of IDH1/2. The prediction of 1p19q and MGMT achieved moderate results, with AUCs of 0.999 ± 0.005/0.711 ± 0.128 for 1p19q and 0.854 ± 0.046/0.742 ± 0.050 for MGMT. Conclusion: This fully automated approach utilizing simplified MR protocols to predict the genetic profile and grading of cerebral gliomas provides an easy and efficient method for non-invasive tumor decoding. MDPI 2021-12-08 /pmc/articles/PMC8699054/ /pubmed/34944806 http://dx.doi.org/10.3390/cancers13246186 Text en © 2021 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 Article
Haubold, Johannes
Hosch, René
Parmar, Vicky
Glas, Martin
Guberina, Nika
Catalano, Onofrio Antonio
Pierscianek, Daniela
Wrede, Karsten
Deuschl, Cornelius
Forsting, Michael
Nensa, Felix
Flaschel, Nils
Umutlu, Lale
Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title_full Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title_fullStr Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title_full_unstemmed Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title_short Fully Automated MR Based Virtual Biopsy of Cerebral Gliomas
title_sort fully automated mr based virtual biopsy of cerebral gliomas
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8699054/
https://www.ncbi.nlm.nih.gov/pubmed/34944806
http://dx.doi.org/10.3390/cancers13246186
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