Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study
ObjectiveTo explore the value of delta radiomics from cerebral CT perfusion (CTP) in predicting hemorrhagic transformation after intravenous thrombolysis for acute cerebral infarction (HT-ACI).MethodsClinical and imaging data of 419 patients with acute cerebral infarction who underwent CTP after tre...
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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.2025.1545631/full |
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| author | Xiaxia Wu Xiaxia Wu Jinfang Yang Jinfang Yang Xianqun Ji Yingjian Ye Ping Song Lina Song Peng An Peng An |
| author_facet | Xiaxia Wu Xiaxia Wu Jinfang Yang Jinfang Yang Xianqun Ji Yingjian Ye Ping Song Lina Song Peng An Peng An |
| author_sort | Xiaxia Wu |
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| description | ObjectiveTo explore the value of delta radiomics from cerebral CT perfusion (CTP) in predicting hemorrhagic transformation after intravenous thrombolysis for acute cerebral infarction (HT-ACI).MethodsClinical and imaging data of 419 patients with acute cerebral infarction who underwent CTP after treatment between November 2016 and August 2024 were retrospectively collected. Based on post-thrombolysis cranial CT or MRI results, patients were divided into the HT-ACI group (114 cases) and the non-HT-ACI group (305 cases). The dataset was split into a training set and a test set in a 7:3 ratio based on time nodes. In the training set, regions of interest (ROI) within the cerebral infarction area on CTP images were delineated using 3D slicer software, and delta radiomic features were extracted. Hemodynamic parameters such as cerebral blood volume (CBV), cerebral blood flow (CBF), and time to peak (TTP) were obtained using CTP techniques. These were combined with baseline patient data (e.g., age, sex, NIHSS score, medical history) to establish various models for predicting HT-ACI through multivariable logistic regression analysis. The predictive performance of the models was compared using DeLong curves, clinical net benefit was assessed using decision curves, and model predictions were validated using the XGboost algorithm. These results were then validated in the test set, and a nomogram and calibration curve were constructed for clinical application.ResultsIn the training set, significant differences were observed between the two groups in NIHSS score, pre-illness usually use of anticoagulants, age, infarction size, ADC difference, CBF, and Delta radscore (P < 0.05). The combined model [AUC 0.878, OR 0.0217, 95%CI 0.835–0.913] demonstrated superior predictive performance compared to the clinical model [AUC 0.725, OR 0.0310, 95%CI 0.670–0.775] and the imaging model [AUC 0.818, OR 0.0259, 95%CI 0.769–0.861]. This was confirmed by the XGboost algorithm, and decision curves confirmed the higher clinical net benefit of the combined model. Similar results were validated in the test set, and a novel nomogram was constructed to simplify the prediction process for HT-ACI.ConclusionThe combined model established based on delta radiomics from CTP may provide early insights into the hemodynamic status of acutely ischemic brain tissue, holding significant clinical importance for predicting HT-ACI. This method could offer a powerful imaging reference for clinical decision-making in patients with ACI, helping to reduce the risk of HT-ACI and improve patient outcomes. |
| format | Article |
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| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-fab4a8e5f98e4326a25f9c79ab6ecb352025-02-12T05:14:59ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-02-011610.3389/fneur.2025.15456311545631Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot studyXiaxia Wu0Xiaxia Wu1Jinfang Yang2Jinfang Yang3Xianqun Ji4Yingjian Ye5Ping Song6Lina Song7Peng An8Peng An9Department of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Neurology, NICU and Epidemiology, Xiangyang Key Laboratory of Maternal-Fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Neurology, NICU and Epidemiology, Xiangyang Key Laboratory of Maternal-Fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Radiology and Surgery, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, ChinaDepartment of Neurology, NICU and Epidemiology, Xiangyang Key Laboratory of Maternal-Fetal Medicine on Fetal Congenital Heart Disease, Xiangyang No.1 People's Hospital, Hubei University of Medicine, Xiangyang, Hubei, ChinaObjectiveTo explore the value of delta radiomics from cerebral CT perfusion (CTP) in predicting hemorrhagic transformation after intravenous thrombolysis for acute cerebral infarction (HT-ACI).MethodsClinical and imaging data of 419 patients with acute cerebral infarction who underwent CTP after treatment between November 2016 and August 2024 were retrospectively collected. Based on post-thrombolysis cranial CT or MRI results, patients were divided into the HT-ACI group (114 cases) and the non-HT-ACI group (305 cases). The dataset was split into a training set and a test set in a 7:3 ratio based on time nodes. In the training set, regions of interest (ROI) within the cerebral infarction area on CTP images were delineated using 3D slicer software, and delta radiomic features were extracted. Hemodynamic parameters such as cerebral blood volume (CBV), cerebral blood flow (CBF), and time to peak (TTP) were obtained using CTP techniques. These were combined with baseline patient data (e.g., age, sex, NIHSS score, medical history) to establish various models for predicting HT-ACI through multivariable logistic regression analysis. The predictive performance of the models was compared using DeLong curves, clinical net benefit was assessed using decision curves, and model predictions were validated using the XGboost algorithm. These results were then validated in the test set, and a nomogram and calibration curve were constructed for clinical application.ResultsIn the training set, significant differences were observed between the two groups in NIHSS score, pre-illness usually use of anticoagulants, age, infarction size, ADC difference, CBF, and Delta radscore (P < 0.05). The combined model [AUC 0.878, OR 0.0217, 95%CI 0.835–0.913] demonstrated superior predictive performance compared to the clinical model [AUC 0.725, OR 0.0310, 95%CI 0.670–0.775] and the imaging model [AUC 0.818, OR 0.0259, 95%CI 0.769–0.861]. This was confirmed by the XGboost algorithm, and decision curves confirmed the higher clinical net benefit of the combined model. Similar results were validated in the test set, and a novel nomogram was constructed to simplify the prediction process for HT-ACI.ConclusionThe combined model established based on delta radiomics from CTP may provide early insights into the hemodynamic status of acutely ischemic brain tissue, holding significant clinical importance for predicting HT-ACI. This method could offer a powerful imaging reference for clinical decision-making in patients with ACI, helping to reduce the risk of HT-ACI and improve patient outcomes.https://www.frontiersin.org/articles/10.3389/fneur.2025.1545631/fullacute cerebral infarctionintravenous thrombolysishemorrhagic transformationCT perfusion imagingdelta radiomicsprediction model |
| spellingShingle | Xiaxia Wu Xiaxia Wu Jinfang Yang Jinfang Yang Xianqun Ji Yingjian Ye Ping Song Lina Song Peng An Peng An Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study Frontiers in Neurology acute cerebral infarction intravenous thrombolysis hemorrhagic transformation CT perfusion imaging delta radiomics prediction model |
| title | Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study |
| title_full | Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study |
| title_fullStr | Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study |
| title_full_unstemmed | Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study |
| title_short | Delta radiomics modeling based on CTP for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction: an 8-year retrospective pilot study |
| title_sort | delta radiomics modeling based on ctp for predicting hemorrhagic transformation after intravenous thrombolysis in acute cerebral infarction an 8 year retrospective pilot study |
| topic | acute cerebral infarction intravenous thrombolysis hemorrhagic transformation CT perfusion imaging delta radiomics prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1545631/full |
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