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Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations
Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy. Methods: This retrospective s...
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/PMC8714660/ https://www.ncbi.nlm.nih.gov/pubmed/34975726 http://dx.doi.org/10.3389/fneur.2021.767165 |
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author | Zhao, Shaozhi Zhao, Qi Jiao, Yuming Li, Hao Weng, Jiancong Huo, Ran Wang, Jie Xu, Hongyuan Zhang, Junze Li, Yan Wu, Zhenzhou Wang, Shuo Cao, Yong Zhao, Jizong |
author_facet | Zhao, Shaozhi Zhao, Qi Jiao, Yuming Li, Hao Weng, Jiancong Huo, Ran Wang, Jie Xu, Hongyuan Zhang, Junze Li, Yan Wu, Zhenzhou Wang, Shuo Cao, Yong Zhao, Jizong |
author_sort | Zhao, Shaozhi |
collection | PubMed |
description | Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy. Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness. Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness. Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy. |
format | Online Article Text |
id | pubmed-8714660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87146602021-12-30 Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations Zhao, Shaozhi Zhao, Qi Jiao, Yuming Li, Hao Weng, Jiancong Huo, Ran Wang, Jie Xu, Hongyuan Zhang, Junze Li, Yan Wu, Zhenzhou Wang, Shuo Cao, Yong Zhao, Jizong Front Neurol Neurology Objectives: To investigate the association between radiomics features and epilepsy in patients with unruptured brain arteriovenous malformations (bAVMs) and to develop a prediction model based on radiomics features and clinical characteristics for bAVM-related epilepsy. Methods: This retrospective study enrolled 176 patients with unruptured bAVMs. After manual lesion segmentation, a total of 858 radiomics features were extracted from time-of-flight magnetic resonance angiography (TOF-MRA). A radiomics model was constructed, and a radiomics score was calculated. Meanwhile, the demographic and angioarchitectural characteristics of patients were assessed to build a clinical model. Incorporating the radiomics score and independent clinical risk factors, a combined model was constructed. The performance of the models was assessed with respect to discrimination, calibration, and clinical usefulness. Results: The clinical model incorporating 3 clinical features had an area under the curve (AUC) of 0.71. Fifteen radiomics features were used to build the radiomics model, which had a higher AUC of 0.78. Incorporating the radiomics score and clinical risk factors, the combined model showed a favorable discrimination ability and calibration, with an AUC of 0.82. Decision curve analysis (DCA) demonstrated that the combined model outperformed the clinical model and radiomics model in terms of clinical usefulness. Conclusions: The radiomics features extracted from TOF-MRA were associated with epilepsy in patients with unruptured bAVMs. The radiomics-clinical nomogram, which was constructed based on the model incorporating the radiomics score and clinical features, showed favorable predictive efficacy for bAVM-related epilepsy. Frontiers Media S.A. 2021-12-15 /pmc/articles/PMC8714660/ /pubmed/34975726 http://dx.doi.org/10.3389/fneur.2021.767165 Text en Copyright © 2021 Zhao, Zhao, Jiao, Li, Weng, Huo, Wang, Xu, Zhang, Li, Wu, Wang, Cao and Zhao. 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 | Neurology Zhao, Shaozhi Zhao, Qi Jiao, Yuming Li, Hao Weng, Jiancong Huo, Ran Wang, Jie Xu, Hongyuan Zhang, Junze Li, Yan Wu, Zhenzhou Wang, Shuo Cao, Yong Zhao, Jizong Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title | Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title_full | Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title_fullStr | Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title_full_unstemmed | Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title_short | Radiomics Analysis for Predicting Epilepsy in Patients With Unruptured Brain Arteriovenous Malformations |
title_sort | radiomics analysis for predicting epilepsy in patients with unruptured brain arteriovenous malformations |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8714660/ https://www.ncbi.nlm.nih.gov/pubmed/34975726 http://dx.doi.org/10.3389/fneur.2021.767165 |
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