Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging
ObjectiveThis study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and parent artery characteristics using high-resolution vessel wall imaging (HR-VWI).MethodsA retrospective...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2024.1507082/full |
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| author | Zihang Wang Zihang Wang Chang Yan Chang Yan Wenqing Yuan Wenqing Yuan Shuangyan Jiang Shuangyan Jiang Yongxiang Jiang Ting Chen |
| author_facet | Zihang Wang Zihang Wang Chang Yan Chang Yan Wenqing Yuan Wenqing Yuan Shuangyan Jiang Shuangyan Jiang Yongxiang Jiang Ting Chen |
| author_sort | Zihang Wang |
| collection | DOAJ |
| description | ObjectiveThis study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and parent artery characteristics using high-resolution vessel wall imaging (HR-VWI).MethodsA retrospective analysis was conducted on 298 patients with 386 aneurysms. Patients were randomly divided into training (n = 308) and validation (n = 78) sets. Key predictors, including aneurysm size, shape, aneurysm wall and parent artery wall enhancement, were identified through univariate analysis and then used to build the prediction model using multivariate logistic regression. The model was visualized as a nomogram and compared to PHASES and ELAPSS scores.ResultsThe logistic regression model demonstrated superior predictive performance with an area under the curve of 0.814, which was significantly higher than PHASES and ELAPSS scores (p < 0.05). The model revealed strong calibration and good agreement between predicted and observed rupture probabilities.ConclusionThe multivariate model based on HR-VWI, which incorporates aneurysm and parent artery features, provides a more accurate prediction of IA rupture risk than conventional scoring systems, offering a valuable tool for clinical decision-making. |
| format | Article |
| id | doaj-art-c7835b41e6c14c97a90c8ad962ea012a |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-c7835b41e6c14c97a90c8ad962ea012a2025-01-29T15:03:04ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011510.3389/fneur.2024.15070821507082Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imagingZihang Wang0Zihang Wang1Chang Yan2Chang Yan3Wenqing Yuan4Wenqing Yuan5Shuangyan Jiang6Shuangyan Jiang7Yongxiang Jiang8Ting Chen9Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaThe Second Clinical College, Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaThe Second Clinical College, Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaThe Second Clinical College, Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaThe Second Clinical College, Chongqing Medical University, Chongqing, ChinaDepartment of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaObjectiveThis study aimed to develop and validate a multivariate logistic regression model for predicting intracranial aneurysm (IA) rupture by integrating clinical data, aneurysm morphology, and parent artery characteristics using high-resolution vessel wall imaging (HR-VWI).MethodsA retrospective analysis was conducted on 298 patients with 386 aneurysms. Patients were randomly divided into training (n = 308) and validation (n = 78) sets. Key predictors, including aneurysm size, shape, aneurysm wall and parent artery wall enhancement, were identified through univariate analysis and then used to build the prediction model using multivariate logistic regression. The model was visualized as a nomogram and compared to PHASES and ELAPSS scores.ResultsThe logistic regression model demonstrated superior predictive performance with an area under the curve of 0.814, which was significantly higher than PHASES and ELAPSS scores (p < 0.05). The model revealed strong calibration and good agreement between predicted and observed rupture probabilities.ConclusionThe multivariate model based on HR-VWI, which incorporates aneurysm and parent artery features, provides a more accurate prediction of IA rupture risk than conventional scoring systems, offering a valuable tool for clinical decision-making.https://www.frontiersin.org/articles/10.3389/fneur.2024.1507082/fullintracranial aneurysmparent arteryHR-VWIPHASES scoreELAPSS score |
| spellingShingle | Zihang Wang Zihang Wang Chang Yan Chang Yan Wenqing Yuan Wenqing Yuan Shuangyan Jiang Shuangyan Jiang Yongxiang Jiang Ting Chen Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging Frontiers in Neurology intracranial aneurysm parent artery HR-VWI PHASES score ELAPSS score |
| title | Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging |
| title_full | Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging |
| title_fullStr | Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging |
| title_full_unstemmed | Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging |
| title_short | Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging |
| title_sort | enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high resolution vessel wall imaging |
| topic | intracranial aneurysm parent artery HR-VWI PHASES score ELAPSS score |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2024.1507082/full |
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