CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion
Background and aimsSecondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to pred...
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| Format: | Article |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2023.1152730/full |
| _version_ | 1827949671772848128 |
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| author | Shadamu Yusuying Shadamu Yusuying Yao Lu Yao Lu Shun Zhang Junjie Wang Juan Chen Daming Wang Daming Wang Jun Lu Jun Lu Peng Qi |
| author_facet | Shadamu Yusuying Shadamu Yusuying Yao Lu Yao Lu Shun Zhang Junjie Wang Juan Chen Daming Wang Daming Wang Jun Lu Jun Lu Peng Qi |
| author_sort | Shadamu Yusuying |
| collection | DOAJ |
| description | Background and aimsSecondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images.Materials and methodsA total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram.ResultsIn the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort.ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE. |
| first_indexed | 2024-04-09T13:13:37Z |
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| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-09T13:13:37Z |
| publishDate | 2023-05-01 |
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| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-8ebe8136d3e54d358613d60de96672e02023-05-12T05:57:31ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-05-011410.3389/fneur.2023.11527301152730CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusionShadamu Yusuying0Shadamu Yusuying1Yao Lu2Yao Lu3Shun Zhang4Junjie Wang5Juan Chen6Daming Wang7Daming Wang8Jun Lu9Jun Lu10Peng Qi11Department of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaGraduate School of Peking Union Medical College, Beijing, ChinaBeijing Hospital, National Center of Gerontology, Beijing Institute of Geriatrics, Beijing, ChinaDepartment of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Radiology, Beijing Hospital, National Center of Gerontology, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaGraduate School of Peking Union Medical College, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaGraduate School of Peking Union Medical College, Beijing, ChinaDepartment of Neurosurgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaBackground and aimsSecondary embolization (SE) during mechanical thrombectomy (MT) for cerebral large vessel occlusion (LVO) could reduce the anterior blood flow and worsen clinical outcomes. The current SE prediction tools have limited accuracy. In this study, we aimed to develop a nomogram to predict SE following MT for LVO based on clinical features and radiomics extracted from computed tomography (CT) images.Materials and methodsA total of 61 patients with LVO stroke treated by MT at Beijing Hospital were included in this retrospective study, of whom 27 developed SE during the MT procedure. The patients were randomly divided (7:3) into training (n = 42) and testing (n = 19) cohorts. The thrombus radiomics features were extracted from the pre-interventional thin-slice CT images, and the conventional clinical and radiological indicators associated with SE were recorded. A support vector machine (SVM) learning model with 5-fold cross-verification was used to obtain the radiomics and clinical signatures. For both signatures, a prediction nomogram for SE was constructed. The signatures were then combined using the logistic regression analysis to construct a combined clinical radiomics nomogram.ResultsIn the training cohort, the area under the receiver operating characteristic curve (AUC) of the nomograms was 0.963 for the combined model, 0.911 for the radiomics, and 0.891 for the clinical model. Following validation, the AUCs were 0.762 for the combined model, 0.714 for the radiomics model, and 0.637 for the clinical model. The combined clinical and radiomics nomogram had the best prediction accuracy in both the training and test cohort.ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.https://www.frontiersin.org/articles/10.3389/fneur.2023.1152730/fullstrokemechanical thrombectomythrombussecondary embolizationradiomicsnomogram |
| spellingShingle | Shadamu Yusuying Shadamu Yusuying Yao Lu Yao Lu Shun Zhang Junjie Wang Juan Chen Daming Wang Daming Wang Jun Lu Jun Lu Peng Qi CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion Frontiers in Neurology stroke mechanical thrombectomy thrombus secondary embolization radiomics nomogram |
| title | CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| title_full | CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| title_fullStr | CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| title_full_unstemmed | CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| title_short | CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| title_sort | ct based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion |
| topic | stroke mechanical thrombectomy thrombus secondary embolization radiomics nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2023.1152730/full |
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