Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment
BackgroundThis study aimed to develop a clinical-radiomics model using hyperattenuated imaging markers (HIM), characterized by hyperattenuation on head non-contrast computed tomography immediately after thrombectomy, to predict the risk of hemorrhagic transformation (HT) in patients undergoing endov...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1471274/full |
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| author | Sheng Hu Junyu Liu Jiayi Hong Yuting Chen Ziwen Wang Jibo Hu Shiying Gai Xiaochao Yu Jingjing Fu |
| author_facet | Sheng Hu Junyu Liu Jiayi Hong Yuting Chen Ziwen Wang Jibo Hu Shiying Gai Xiaochao Yu Jingjing Fu |
| author_sort | Sheng Hu |
| collection | DOAJ |
| description | BackgroundThis study aimed to develop a clinical-radiomics model using hyperattenuated imaging markers (HIM), characterized by hyperattenuation on head non-contrast computed tomography immediately after thrombectomy, to predict the risk of hemorrhagic transformation (HT) in patients undergoing endovascular mechanical thrombectomy (MT).MethodsA total of 159 consecutive patients with HIM were screened immediately after MT for inclusion. The datasets were randomly divided into training and test cohorts at a ratio of 8:2. An optimal machine learning (ML) algorithm was used for model development. Subsequently, models for clinical, radiomics, and clinical-radiomics were developed. The performance of the models was measured using receiver operating characteristic (ROC) and decision curve analyses (DCA). The interpretability and predictor importance of the model were analyzed using Shapley additive explanations.ResultsOf the 159 patients, 100 (62.9%) exhibited HT. The support vector machine (SVM) was the optimal ML algorithm for constructing the models. In predicting HT, the areas under the curve (AUCs) of the clinical model were 0.918 (95% confidence interval [CI] = 0.869–0.966) in the training cohort and 0.854 (95% CI = 0.724–0.984) in the test cohort. The AUCs of the radiomics model were 0.869 (95% CI = 0.802–0.936) and 0.829 (95% CI = 0.668–0.990), while those of the clinical-radiomics model were 0.944 (95% CI = 0.905–0.984) and 0.925 (95% CI = 0.832–1.000).ConclusionThe suggested clinical-radiomics model based on HIM is a reliable method that can provide a risk evaluation of HT in individuals undergoing MT. |
| format | Article |
| id | doaj-art-46daf3df4ebe43b6a90bbf7c9cc61614 |
| institution | DOAJ |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neurology |
| spelling | doaj-art-46daf3df4ebe43b6a90bbf7c9cc616142025-08-20T03:11:25ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-02-011610.3389/fneur.2025.14712741471274Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatmentSheng Hu0Junyu Liu1Jiayi Hong2Yuting Chen3Ziwen Wang4Jibo Hu5Shiying Gai6Xiaochao Yu7Jingjing Fu8Department of Radiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Neurology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Neurology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Neurology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Radiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Radiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Neurosurgery, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Radiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaDepartment of Neurology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, ChinaBackgroundThis study aimed to develop a clinical-radiomics model using hyperattenuated imaging markers (HIM), characterized by hyperattenuation on head non-contrast computed tomography immediately after thrombectomy, to predict the risk of hemorrhagic transformation (HT) in patients undergoing endovascular mechanical thrombectomy (MT).MethodsA total of 159 consecutive patients with HIM were screened immediately after MT for inclusion. The datasets were randomly divided into training and test cohorts at a ratio of 8:2. An optimal machine learning (ML) algorithm was used for model development. Subsequently, models for clinical, radiomics, and clinical-radiomics were developed. The performance of the models was measured using receiver operating characteristic (ROC) and decision curve analyses (DCA). The interpretability and predictor importance of the model were analyzed using Shapley additive explanations.ResultsOf the 159 patients, 100 (62.9%) exhibited HT. The support vector machine (SVM) was the optimal ML algorithm for constructing the models. In predicting HT, the areas under the curve (AUCs) of the clinical model were 0.918 (95% confidence interval [CI] = 0.869–0.966) in the training cohort and 0.854 (95% CI = 0.724–0.984) in the test cohort. The AUCs of the radiomics model were 0.869 (95% CI = 0.802–0.936) and 0.829 (95% CI = 0.668–0.990), while those of the clinical-radiomics model were 0.944 (95% CI = 0.905–0.984) and 0.925 (95% CI = 0.832–1.000).ConclusionThe suggested clinical-radiomics model based on HIM is a reliable method that can provide a risk evaluation of HT in individuals undergoing MT.https://www.frontiersin.org/articles/10.3389/fneur.2025.1471274/fullhemorrhagic transformationmachine learningthrombectomyacute ischemic strokemulti-detector CT |
| spellingShingle | Sheng Hu Junyu Liu Jiayi Hong Yuting Chen Ziwen Wang Jibo Hu Shiying Gai Xiaochao Yu Jingjing Fu Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment Frontiers in Neurology hemorrhagic transformation machine learning thrombectomy acute ischemic stroke multi-detector CT |
| title | Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment |
| title_full | Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment |
| title_fullStr | Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment |
| title_full_unstemmed | Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment |
| title_short | Machine learning, clinical-radiomics approach with HIM for hemorrhagic transformation prediction after thrombectomy and treatment |
| title_sort | machine learning clinical radiomics approach with him for hemorrhagic transformation prediction after thrombectomy and treatment |
| topic | hemorrhagic transformation machine learning thrombectomy acute ischemic stroke multi-detector CT |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1471274/full |
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