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Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma
BACKGROUND: Postoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma. OBJECTIVE: To evaluate the value of th...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082417/ https://www.ncbi.nlm.nih.gov/pubmed/33937027 http://dx.doi.org/10.3389/fonc.2021.625220 |
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author | Xiao, Bing Fan, Yanghua Zhang, Zhe Tan, Zilong Yang, Huan Tu, Wei Wu, Lei Shen, Xiaoli Guo, Hua Wu, Zhen Zhu, Xingen |
author_facet | Xiao, Bing Fan, Yanghua Zhang, Zhe Tan, Zilong Yang, Huan Tu, Wei Wu, Lei Shen, Xiaoli Guo, Hua Wu, Zhen Zhu, Xingen |
author_sort | Xiao, Bing |
collection | PubMed |
description | BACKGROUND: Postoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma. OBJECTIVE: To evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma. METHODS: A total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction. RESULTS: Three significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction. CONCLUSIONS: By integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma. |
format | Online Article Text |
id | pubmed-8082417 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80824172021-04-30 Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma Xiao, Bing Fan, Yanghua Zhang, Zhe Tan, Zilong Yang, Huan Tu, Wei Wu, Lei Shen, Xiaoli Guo, Hua Wu, Zhen Zhu, Xingen Front Oncol Oncology BACKGROUND: Postoperative cerebral edema is common in patients with meningioma. It is of great clinical significance to predict the postoperative cerebral edema exacerbation (CEE) for the development of individual treatment programs in patients with meningioma. OBJECTIVE: To evaluate the value of three-dimensional radiomics Features from Multi-Parameter MRI in predicting the postoperative CEE in patients with meningioma. METHODS: A total of 136 meningioma patients with complete clinical and radiological data were collected for this retrospective study, and they were randomly divided into primary and validation cohorts. Three-dimensional radiomics features were extracted from multisequence MR images, and then screened through Wilcoxon rank sum test, elastic net and recursive feature elimination algorithms. A radiomics signature was established based support vector machine method. By combining clinical with the radiomics signature, a clin-radiomics combined model was constructed for individual CEE prediction. RESULTS: Three significance radiomics features were selected to construct a radiomics signature, with areas under the curves (AUCs) of 0.86 and 0.800 in the primary and validation cohorts, respectively. Two clinical characteristics (peritumoral edema and tumor size) and radiomics signature were determined to establish the clin-radiomics combined model, with an AUC of 0.91 in the primary cohort and 0.83 in the validation cohort. The clin-radiomics combined model showed good discrimination, calibration, and clinically useful for postoperative CEE prediction. CONCLUSIONS: By integrating clinical characteristics with radiomics signature, the clin-radiomics combined model could assist in postoperative CEE prediction before surgery, and provide a basis for surgical treatment decisions in patients with meningioma. Frontiers Media S.A. 2021-04-15 /pmc/articles/PMC8082417/ /pubmed/33937027 http://dx.doi.org/10.3389/fonc.2021.625220 Text en Copyright © 2021 Xiao, Fan, Zhang, Tan, Yang, Tu, Wu, Shen, Guo, Wu and Zhu 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 Xiao, Bing Fan, Yanghua Zhang, Zhe Tan, Zilong Yang, Huan Tu, Wei Wu, Lei Shen, Xiaoli Guo, Hua Wu, Zhen Zhu, Xingen Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title | Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title_full | Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title_fullStr | Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title_full_unstemmed | Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title_short | Three-Dimensional Radiomics Features From Multi-Parameter MRI Combined With Clinical Characteristics Predict Postoperative Cerebral Edema Exacerbation in Patients With Meningioma |
title_sort | three-dimensional radiomics features from multi-parameter mri combined with clinical characteristics predict postoperative cerebral edema exacerbation in patients with meningioma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082417/ https://www.ncbi.nlm.nih.gov/pubmed/33937027 http://dx.doi.org/10.3389/fonc.2021.625220 |
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