CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture
Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics cla...
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Frontiers Media S.A.
2021-02-01
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Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2021.619864/full |
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author | Osamah Alwalid Osamah Alwalid Xi Long Xi Long Mingfei Xie Mingfei Xie Jiehua Yang Chunyuan Cen Chunyuan Cen Huan Liu Ping Han Ping Han |
author_facet | Osamah Alwalid Osamah Alwalid Xi Long Xi Long Mingfei Xie Mingfei Xie Jiehua Yang Chunyuan Cen Chunyuan Cen Huan Liu Ping Han Ping Han |
author_sort | Osamah Alwalid |
collection | DOAJ |
description | Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images. |
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last_indexed | 2024-12-19T14:04:41Z |
publishDate | 2021-02-01 |
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spelling | doaj.art-c31d1d8bf1ad406f92b09b2a28b32c192022-12-21T20:18:21ZengFrontiers Media S.A.Frontiers in Neurology1664-22952021-02-011210.3389/fneur.2021.619864619864CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm RuptureOsamah Alwalid0Osamah Alwalid1Xi Long2Xi Long3Mingfei Xie4Mingfei Xie5Jiehua Yang6Chunyuan Cen7Chunyuan Cen8Huan Liu9Ping Han10Ping Han11Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaGE Healthcare, Shanghai, ChinaDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaHubei Province Key Laboratory of Molecular Imaging, Wuhan, ChinaBackground: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture.Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms.Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89–0.95] and 0.86 [95% CI: 0.80–0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. −1.60 and 2.35 vs. −1.01 on training and test cohorts, respectively, p < 0.001).Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.https://www.frontiersin.org/articles/10.3389/fneur.2021.619864/fullintracranial aneurysmaneurysm rupturesubarachnoid hemorrhagemachine learningradiomics |
spellingShingle | Osamah Alwalid Osamah Alwalid Xi Long Xi Long Mingfei Xie Mingfei Xie Jiehua Yang Chunyuan Cen Chunyuan Cen Huan Liu Ping Han Ping Han CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture Frontiers in Neurology intracranial aneurysm aneurysm rupture subarachnoid hemorrhage machine learning radiomics |
title | CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture |
title_full | CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture |
title_fullStr | CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture |
title_full_unstemmed | CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture |
title_short | CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture |
title_sort | ct angiography based radiomics for classification of intracranial aneurysm rupture |
topic | intracranial aneurysm aneurysm rupture subarachnoid hemorrhage machine learning radiomics |
url | https://www.frontiersin.org/articles/10.3389/fneur.2021.619864/full |
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