The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction
Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in th...
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MDPI AG
2022-11-01
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| Online Access: | https://www.mdpi.com/2075-1729/12/11/1847 |
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| author | Yingwei Guo Yingjian Yang Mingming Wang Yu Luo Jia Guo Fengqiu Cao Jiaxi Lu Xueqiang Zeng Xiaoqiang Miao Asim Zaman Yan Kang |
| author_facet | Yingwei Guo Yingjian Yang Mingming Wang Yu Luo Jia Guo Fengqiu Cao Jiaxi Lu Xueqiang Zeng Xiaoqiang Miao Asim Zaman Yan Kang |
| author_sort | Yingwei Guo |
| collection | DOAJ |
| description | Accurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions. |
| first_indexed | 2024-03-09T18:13:46Z |
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| institution | Directory Open Access Journal |
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| last_indexed | 2024-03-09T18:13:46Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
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| series | Life |
| spelling | doaj.art-60b7e99715d741619e6e28a0867de03b2023-11-24T08:56:54ZengMDPI AGLife2075-17292022-11-011211184710.3390/life12111847The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome PredictionYingwei Guo0Yingjian Yang1Mingming Wang2Yu Luo3Jia Guo4Fengqiu Cao5Jiaxi Lu6Xueqiang Zeng7Xiaoqiang Miao8Asim Zaman9Yan Kang10College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, ChinaDepartment of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, ChinaDepartment of Radiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai 200434, ChinaDepartment of Psychiatry, Columbia University, New York, NY 10027, USACollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, ChinaCollege of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, ChinaCollege of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, ChinaCollege of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen 518118, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, ChinaAccurate and reliable outcome predictions can help evaluate the functional recovery of ischemic stroke patients and assist in making treatment plans. Given that recovery factors may be hidden in the whole-brain features, this study aims to validate the role of dynamic radiomics features (DRFs) in the whole brain, DRFs in local ischemic lesions, and their combination in predicting functional outcomes of ischemic stroke patients. First, the DRFs in the whole brain and the DRFs in local lesions of dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) images are calculated. Second, the least absolute shrinkage and selection operator (Lasso) is used to generate four groups of DRFs, including the outstanding DRFs in the whole brain (Lasso (WB)), the outstanding DRFs in local lesions (Lasso (LL)), the combination of them (combined DRFs), and the outstanding DRFs in the combined DRFs (Lasso (combined)). Then, the performance of the four groups of DRFs is evaluated to predict the functional recovery in three months. As a result, Lasso (combined) in the four groups achieves the best AUC score of 0.971, which improves the score by 8.9% compared with Lasso (WB), and by 3.5% compared with Lasso (WB) and combined DRFs. In conclusion, the outstanding combined DRFs generated from the outstanding DRFs in the whole brain and local lesions can predict functional outcomes in ischemic stroke patients better than the single DRFs in the whole brain or local lesions.https://www.mdpi.com/2075-1729/12/11/1847DSC-PWIdynamic radiomics featureswhole brainlocal lesionslassooutcome prediction |
| spellingShingle | Yingwei Guo Yingjian Yang Mingming Wang Yu Luo Jia Guo Fengqiu Cao Jiaxi Lu Xueqiang Zeng Xiaoqiang Miao Asim Zaman Yan Kang The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction Life DSC-PWI dynamic radiomics features whole brain local lesions lasso outcome prediction |
| title | The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction |
| title_full | The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction |
| title_fullStr | The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction |
| title_full_unstemmed | The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction |
| title_short | The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction |
| title_sort | combination of whole brain features and local lesion features in dsc pwi may improve ischemic stroke outcome prediction |
| topic | DSC-PWI dynamic radiomics features whole brain local lesions lasso outcome prediction |
| url | https://www.mdpi.com/2075-1729/12/11/1847 |
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