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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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Autores principales: Guo, Yingwei, Yang, Yingjian, Wang, Mingming, Luo, Yu, Guo, Jia, Cao, Fengqiu, Lu, Jiaxi, Zeng, Xueqiang, Miao, Xiaoqiang, Zaman, Asim, Kang, Yan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694195/
https://www.ncbi.nlm.nih.gov/pubmed/36430982
http://dx.doi.org/10.3390/life12111847
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author Guo, Yingwei
Yang, Yingjian
Wang, Mingming
Luo, Yu
Guo, Jia
Cao, Fengqiu
Lu, Jiaxi
Zeng, Xueqiang
Miao, Xiaoqiang
Zaman, Asim
Kang, Yan
author_facet Guo, Yingwei
Yang, Yingjian
Wang, Mingming
Luo, Yu
Guo, Jia
Cao, Fengqiu
Lu, Jiaxi
Zeng, Xueqiang
Miao, Xiaoqiang
Zaman, Asim
Kang, Yan
author_sort Guo, Yingwei
collection PubMed
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.
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spelling pubmed-96941952022-11-26 The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke Outcome Prediction Guo, Yingwei Yang, Yingjian Wang, Mingming Luo, Yu Guo, Jia Cao, Fengqiu Lu, Jiaxi Zeng, Xueqiang Miao, Xiaoqiang Zaman, Asim Kang, Yan Life (Basel) Article 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. MDPI 2022-11-11 /pmc/articles/PMC9694195/ /pubmed/36430982 http://dx.doi.org/10.3390/life12111847 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Yingwei
Yang, Yingjian
Wang, Mingming
Luo, Yu
Guo, Jia
Cao, Fengqiu
Lu, Jiaxi
Zeng, Xueqiang
Miao, Xiaoqiang
Zaman, Asim
Kang, Yan
The Combination of Whole-Brain Features and Local-Lesion Features in DSC-PWI May Improve Ischemic Stroke 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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9694195/
https://www.ncbi.nlm.nih.gov/pubmed/36430982
http://dx.doi.org/10.3390/life12111847
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