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Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction

Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemi...

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Autores principales: Zhai, Heng, Liu, Zhijun, Wu, Sheng, Cao, Ziqin, Xu, Yan, Lv, Yinzhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353395/
https://www.ncbi.nlm.nih.gov/pubmed/35937879
http://dx.doi.org/10.3389/fnins.2022.923708
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author Zhai, Heng
Liu, Zhijun
Wu, Sheng
Cao, Ziqin
Xu, Yan
Lv, Yinzhang
author_facet Zhai, Heng
Liu, Zhijun
Wu, Sheng
Cao, Ziqin
Xu, Yan
Lv, Yinzhang
author_sort Zhai, Heng
collection PubMed
description Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (n = 44) and non-HT group (n = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (p < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI.
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spelling pubmed-93533952022-08-06 Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction Zhai, Heng Liu, Zhijun Wu, Sheng Cao, Ziqin Xu, Yan Lv, Yinzhang Front Neurosci Neuroscience Massive cerebral infarction (MCI) is a devastating condition and associated with high rate of morbidity and mortality. Hemorrhagic transformation (HT) is a common complication after acute MCI, and often results in poor outcomes. Although several predictors of HT have been identified in acute ischemic stroke (AIS), the association between the predictors and HT remains controversial. Therefore, we aim to explore the value of texture analysis on magnetic resonance image (MRI) for predicting HT after acute MCI. This retrospective study included a total of 98 consecutive patients who were admitted for acute MCI between January 2019 and October 2020. Patients were divided into the HT group (n = 44) and non-HT group (n = 54) according to the follow-up computed tomography (CT) images. A total of 11 quantitative texture features derived from images of diffusion-weighted image (DWI) or T2-weighted-Fluid-Attenuated Inversion Recovery (T2/FLAIR) were extracted for each patient. Receiver operating characteristic (ROC) analysis were performed to determine the predictive performance of textural features, with HT as the outcome measurement. There was no significant difference in the baseline demographic and clinical characteristics between the two groups. The distribution of atrial fibrillation and National Institutes of Health Stroke Scale (NIHSS) were significantly higher in patients with HT than those without HT. Among the textural parameters extracted from DWI images, six parameters, f2 (contrast), f3 (correlation), f4 (sum of squares), f5 (inverse difference moment), f10 (difference variance), and f11 (difference entropy), differs significantly between the two groups (p < 0.05). Moreover, five of six parameters (f2, f3, f5, f10, and f11) have good predictive performances of HT with the area under the ROC curve (AUC) values of 0.795, 0.779, 0.791, 0.780, and 0.797, respectively. However, the texture features f2, f3, and f10 in T2/FLAIR images were the only three significant predictors of HT in patients with acute MCI, but with a relatively low AUC values of 0.652, 0.652, and 0.670, respectively. In summary, our preliminary results showed DWI-based texture analysis has a good predictive validity for HT in patients with acute MCI. Multiparametric MRI texture analysis model should be developed to improve the prediction performance of HT following acute MCI. Frontiers Media S.A. 2022-07-22 /pmc/articles/PMC9353395/ /pubmed/35937879 http://dx.doi.org/10.3389/fnins.2022.923708 Text en Copyright © 2022 Zhai, Liu, Wu, Cao, Xu and Lv. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhai, Heng
Liu, Zhijun
Wu, Sheng
Cao, Ziqin
Xu, Yan
Lv, Yinzhang
Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title_full Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title_fullStr Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title_full_unstemmed Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title_short Predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
title_sort predictive value of magnetic resonance imaging-based texture analysis for hemorrhage transformation in large cerebral infarction
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9353395/
https://www.ncbi.nlm.nih.gov/pubmed/35937879
http://dx.doi.org/10.3389/fnins.2022.923708
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