Cargando…
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...
Autores principales: | , , , , , , , |
---|---|
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 |
_version_ | 1784645303377330176 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8815760 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT zhangjing amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhangguojin amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT caoyuntai amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT renjialiang amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhaozhiyong amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT hantao amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT chenkuntao amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhoujunlin amagneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhangjing magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhangguojin magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT caoyuntai magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT renjialiang magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhaozhiyong magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT hantao magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT chenkuntao magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas AT zhoujunlin magneticresonanceimagingbasedradiomicmodelforthenoninvasivepreoperativedifferentiationbetweentransitionalandatypicalmeningiomas |