Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis
Abstract Acute ischemic stroke (AIS) presents significant heterogeneity in clinical and thrombus imaging characteristics, which can profoundly impact therapeutic decisions and outcomes. This study analyzed 520 AIS patients who underwent endovascular thrombectomy, integrating clinical variables and t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05120-y |
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| author | Wenjuan Wu Yue Cheng Long Chen Qingyue Fu Jingxuan Jiang Lei Zhang Ximing Wang |
| author_facet | Wenjuan Wu Yue Cheng Long Chen Qingyue Fu Jingxuan Jiang Lei Zhang Ximing Wang |
| author_sort | Wenjuan Wu |
| collection | DOAJ |
| description | Abstract Acute ischemic stroke (AIS) presents significant heterogeneity in clinical and thrombus imaging characteristics, which can profoundly impact therapeutic decisions and outcomes. This study analyzed 520 AIS patients who underwent endovascular thrombectomy, integrating clinical variables and thrombus imaging features to identify potential subtypes through unsupervised clustering and principal component analysis. Three distinct subtypes emerged: Cluster 1, characterized by middle cerebral artery occlusion, shorter thrombus lengths, and favorable outcomes; Cluster 2, comprising predominantly male smokers and drinkers with no significant outcome differences; and Cluster 3, consisting of older patients with higher stroke severity, internal carotid artery occlusion, longer thrombus lengths, and poor outcomes. Key features driving subtype differentiation included atrial fibrillation, thrombus perviousness, and clot burden scores. Significant variations in recanalization and hemorrhagic transformation rates were also observed among clusters. These findings underscore the potential of integrating thrombus imaging characteristics into personalized treatment strategies, offering a more precise approach to prognosis and management for AIS patients. |
| format | Article |
| id | doaj-art-1178d3dea8044126bfe3bbb7f0c79ec6 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1178d3dea8044126bfe3bbb7f0c79ec62025-08-20T03:03:37ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-05120-ySubtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysisWenjuan Wu0Yue Cheng1Long Chen2Qingyue Fu3Jingxuan Jiang4Lei Zhang5Ximing Wang6Department of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Wuxi No.2 People’s Hospital, Jiangnan University Medical Center, Affiliated Wuxi Clinical College of Nantong UniversityDepartment of Interventional Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityDepartment of Radiology, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDepartment of Radiology, Wuxi No.2 People’s Hospital, Jiangnan University Medical Center, Affiliated Wuxi Clinical College of Nantong UniversityDepartment of Radiology, The First Affiliated Hospital of Soochow UniversityAbstract Acute ischemic stroke (AIS) presents significant heterogeneity in clinical and thrombus imaging characteristics, which can profoundly impact therapeutic decisions and outcomes. This study analyzed 520 AIS patients who underwent endovascular thrombectomy, integrating clinical variables and thrombus imaging features to identify potential subtypes through unsupervised clustering and principal component analysis. Three distinct subtypes emerged: Cluster 1, characterized by middle cerebral artery occlusion, shorter thrombus lengths, and favorable outcomes; Cluster 2, comprising predominantly male smokers and drinkers with no significant outcome differences; and Cluster 3, consisting of older patients with higher stroke severity, internal carotid artery occlusion, longer thrombus lengths, and poor outcomes. Key features driving subtype differentiation included atrial fibrillation, thrombus perviousness, and clot burden scores. Significant variations in recanalization and hemorrhagic transformation rates were also observed among clusters. These findings underscore the potential of integrating thrombus imaging characteristics into personalized treatment strategies, offering a more precise approach to prognosis and management for AIS patients.https://doi.org/10.1038/s41598-025-05120-yAcute ischemic stroke (AIS)Thrombus imaging characteristicsUnsupervised clusteringPrincipal component analysis (PCA)Endovascular mechanical thrombectomy (EVT) |
| spellingShingle | Wenjuan Wu Yue Cheng Long Chen Qingyue Fu Jingxuan Jiang Lei Zhang Ximing Wang Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis Scientific Reports Acute ischemic stroke (AIS) Thrombus imaging characteristics Unsupervised clustering Principal component analysis (PCA) Endovascular mechanical thrombectomy (EVT) |
| title | Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis |
| title_full | Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis |
| title_fullStr | Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis |
| title_full_unstemmed | Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis |
| title_short | Subtype identification of clinical and thrombus imaging features in acute ischemic stroke: using clustering analysis and principal component analysis |
| title_sort | subtype identification of clinical and thrombus imaging features in acute ischemic stroke using clustering analysis and principal component analysis |
| topic | Acute ischemic stroke (AIS) Thrombus imaging characteristics Unsupervised clustering Principal component analysis (PCA) Endovascular mechanical thrombectomy (EVT) |
| url | https://doi.org/10.1038/s41598-025-05120-y |
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