Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy
Abstract This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict hemorrhagic transformation (HT). A total of 2...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-02056-1 |
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| author | Lina Jiang Guoping Zhu Yue Wang Jiayi Hong Jingjing Fu Jibo Hu Shengxiang Xiao Jiayi Chu Sheng Hu Wenbo Xiao |
| author_facet | Lina Jiang Guoping Zhu Yue Wang Jiayi Hong Jingjing Fu Jibo Hu Shengxiang Xiao Jiayi Chu Sheng Hu Wenbo Xiao |
| author_sort | Lina Jiang |
| collection | DOAJ |
| description | Abstract This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict hemorrhagic transformation (HT). A total of 239 patients with HIM who underwent MT were enrolled, with 191 patients (80%) in the training cohort and 48 patients (20%) in the validation cohort. Additionally, the model was tested on an internal prospective cohort of 49 patients. A total of 1834 radiomics features and 2048 DL features were extracted from HIM images. Statistical methods, such as analysis of variance, Pearson’s correlation coefficient, principal component analysis, and least absolute shrinkage and selection operator, were used to select the most significant features. A K-Nearest Neighbor classifier was employed to develop a combined model integrating clinical, radiomics, and DL features for HT prediction. Model performance was evaluated using metrics such as accuracy, sensitivity, specificity, receiver operating characteristic curves, and area under curve (AUC). In the training, validation, and test cohorts, the combined model achieved AUCs of 0.926, 0.923, and 0.887, respectively, outperforming other models, including clinical, radiomics, and DL models, as well as hybrid models combining subsets of features (Clinical + Radiomics, DL + Radiomics, and Clinical + DL) in predicting HT. The combined model, which integrates clinical, radiomics, and DL features derived from HIM, demonstrated efficacy in noninvasively predicting HT. These findings suggest its potential utility in guiding clinical decision-making for patients with MT. |
| format | Article |
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| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-2ebc5f769db440d9bb3a55cca973cd1c2025-08-20T01:53:22ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-02056-1Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomyLina Jiang0Guoping Zhu1Yue Wang2Jiayi Hong3Jingjing Fu4Jibo Hu5Shengxiang Xiao6Jiayi Chu7Sheng Hu8Wenbo Xiao9Department of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Neurology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang UniversityDepartment of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang UniversityAbstract This study aimed to develop a predictive model integrating clinical, radiomics, and deep learning (DL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict hemorrhagic transformation (HT). A total of 239 patients with HIM who underwent MT were enrolled, with 191 patients (80%) in the training cohort and 48 patients (20%) in the validation cohort. Additionally, the model was tested on an internal prospective cohort of 49 patients. A total of 1834 radiomics features and 2048 DL features were extracted from HIM images. Statistical methods, such as analysis of variance, Pearson’s correlation coefficient, principal component analysis, and least absolute shrinkage and selection operator, were used to select the most significant features. A K-Nearest Neighbor classifier was employed to develop a combined model integrating clinical, radiomics, and DL features for HT prediction. Model performance was evaluated using metrics such as accuracy, sensitivity, specificity, receiver operating characteristic curves, and area under curve (AUC). In the training, validation, and test cohorts, the combined model achieved AUCs of 0.926, 0.923, and 0.887, respectively, outperforming other models, including clinical, radiomics, and DL models, as well as hybrid models combining subsets of features (Clinical + Radiomics, DL + Radiomics, and Clinical + DL) in predicting HT. The combined model, which integrates clinical, radiomics, and DL features derived from HIM, demonstrated efficacy in noninvasively predicting HT. These findings suggest its potential utility in guiding clinical decision-making for patients with MT.https://doi.org/10.1038/s41598-025-02056-1Intracranial hemorrhagesMachine learningThrombectomyAcute ischemic strokeMulti-detector CT |
| spellingShingle | Lina Jiang Guoping Zhu Yue Wang Jiayi Hong Jingjing Fu Jibo Hu Shengxiang Xiao Jiayi Chu Sheng Hu Wenbo Xiao Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy Scientific Reports Intracranial hemorrhages Machine learning Thrombectomy Acute ischemic stroke Multi-detector CT |
| title | Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| title_full | Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| title_fullStr | Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| title_full_unstemmed | Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| title_short | Development and validation of a multi-omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| title_sort | development and validation of a multi omics hemorrhagic transformation model based on hyperattenuated imaging markers following mechanical thrombectomy |
| topic | Intracranial hemorrhages Machine learning Thrombectomy Acute ischemic stroke Multi-detector CT |
| url | https://doi.org/10.1038/s41598-025-02056-1 |
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