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A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas

Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhance...

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Autores principales: Zhang, Jing, Zhang, Guojin, Cao, Yuntai, Ren, Jialiang, Zhao, Zhiyong, Han, Tao, Chen, Kuntao, Zhou, Junlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8815760/
https://www.ncbi.nlm.nih.gov/pubmed/35127543
http://dx.doi.org/10.3389/fonc.2022.811767
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author Zhang, Jing
Zhang, Guojin
Cao, Yuntai
Ren, Jialiang
Zhao, Zhiyong
Han, Tao
Chen, Kuntao
Zhou, Junlin
author_facet Zhang, Jing
Zhang, Guojin
Cao, Yuntai
Ren, Jialiang
Zhao, Zhiyong
Han, Tao
Chen, Kuntao
Zhou, Junlin
author_sort Zhang, Jing
collection PubMed
description Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes.
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spelling pubmed-88157602022-02-05 A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas Zhang, Jing Zhang, Guojin Cao, Yuntai Ren, Jialiang Zhao, Zhiyong Han, Tao Chen, Kuntao Zhou, Junlin Front Oncol Oncology Preoperative distinction between transitional meningioma and atypical meningioma would aid the selection of appropriate surgical techniques, as well as the prognosis prediction. Here, we aimed to differentiate between these two tumors using radiomic signatures based on preoperative, contrast-enhanced T1-weighted and T2-weighted magnetic resonance imaging. A total of 141 transitional meningioma and 101 atypical meningioma cases between January 2014 and December 2018 with a histopathologically confirmed diagnosis were retrospectively reviewed. All patients underwent magnetic resonance imaging before surgery. For each patient, 1227 radiomic features were extracted from contrast-enhanced T1-weighted and T2-weighted images each. Least absolute shrinkage and selection operator regression analysis was performed to select the most informative features of different modalities. Subsequently, stepwise multivariate logistic regression was chosen to further select strongly correlated features and build classification models that can distinguish transitional from atypical meningioma. The diagnostic abilities were evaluated by receiver operating characteristic analysis. Furthermore, a nomogram was built by incorporating clinical characteristics, radiological features, and radiomic signatures, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sex, tumor shape, brain invasion, and four radiomic features differed significantly between transitional meningioma and atypical meningioma. The clinicoradiomic model derived by fusing the above features resulted in the best discrimination ability, with areas under the curves of 0.809 (95% confidence interval, 0.743-0.874) and 0.795 (95% confidence interval, 0.692-0.899) and sensitivity values of 74.0% and 71.4% in the training and validation cohorts, respectively. The clinicoradiomic model demonstrated good performance for the differentiation between transitional and atypical meningioma. It is a quantitative tool that can potentially aid the selection of surgical techniques and the prognosis prediction and can thus be applied in patients with these two meningioma subtypes. Frontiers Media S.A. 2022-01-21 /pmc/articles/PMC8815760/ /pubmed/35127543 http://dx.doi.org/10.3389/fonc.2022.811767 Text en Copyright © 2022 Zhang, Zhang, Cao, Ren, Zhao, Han, Chen and Zhou 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
Zhang, Jing
Zhang, Guojin
Cao, Yuntai
Ren, Jialiang
Zhao, Zhiyong
Han, Tao
Chen, Kuntao
Zhou, Junlin
A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title_full A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title_fullStr A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title_full_unstemmed A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title_short A Magnetic Resonance Imaging-Based Radiomic Model for the Noninvasive Preoperative Differentiation Between Transitional and Atypical Meningiomas
title_sort magnetic resonance imaging-based radiomic model for the noninvasive preoperative differentiation between transitional and atypical meningiomas
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8815760/
https://www.ncbi.nlm.nih.gov/pubmed/35127543
http://dx.doi.org/10.3389/fonc.2022.811767
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