Cargando…

MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke

BACKGROUND: This study aims is to explore whether it is feasible to use magnetic resonance texture analysis (MRTA) in order to distinguish favorable from unfavorable function outcomes and determine the prognostic factors associated with favorable outcomes of stroke. METHODS: The retrospective study...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Yuan, Zhuang, Yuzhong, Ge, Yaqiong, Wu, Pu-Yeh, Zhao, Jing, Wang, Hao, Song, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250246/
https://www.ncbi.nlm.nih.gov/pubmed/35778678
http://dx.doi.org/10.1186/s12880-022-00845-y
_version_ 1784739769444466688
author Zhang, Yuan
Zhuang, Yuzhong
Ge, Yaqiong
Wu, Pu-Yeh
Zhao, Jing
Wang, Hao
Song, Bin
author_facet Zhang, Yuan
Zhuang, Yuzhong
Ge, Yaqiong
Wu, Pu-Yeh
Zhao, Jing
Wang, Hao
Song, Bin
author_sort Zhang, Yuan
collection PubMed
description BACKGROUND: This study aims is to explore whether it is feasible to use magnetic resonance texture analysis (MRTA) in order to distinguish favorable from unfavorable function outcomes and determine the prognostic factors associated with favorable outcomes of stroke. METHODS: The retrospective study included 103 consecutive patients who confirmed unilateral anterior circulation subacute ischemic stroke by computed tomography angiography between January 2018 and September 2019. Patients were divided into favorable outcome (modified Rankin scale, mRS ≤ 2) and unfavorable outcome (mRS > 2) groups according to mRS scores at day 90. Two radiologists manually segmented the infarction lesions based on diffusion-weighted imaging and transferred the images to corresponding apparent diffusion coefficient (ADC) maps in order to extract texture features. The prediction models including clinical characteristics and texture features were built using multiple logistic regression. A univariate analysis was conducted to assess the performance of the mean ADC value of the infarction lesion. A Delong’s test was used to compare the predictive performance of models through the receiver operating characteristic curve. RESULTS: The mean ADC performance was moderate [AUC = 0.60, 95% confidence interval (CI) 0.49–0.71]. The texture feature model of the ADC map (tADC), contained seven texture features, and presented good prediction performance (AUC = 0.83, 95%CI 0.75–0.91). The energy obtained after wavelet transform, and the kurtosis and skewness obtained after Laplacian of Gaussian transformation were identified as independent prognostic factors for the favorable stroke outcomes. In addition, the combination of the tADC model and clinical characteristics (hypertension, diabetes mellitus, smoking, and atrial fibrillation) exhibited a subtly better performance (AUC = 0.86, 95%CI 0.79–0.93; P > 0.05, Delong’s). CONCLUSION: The models based on MRTA on ADC maps are useful to evaluate the clinical function outcomes in patients with unilateral anterior circulation ischemic stroke. Energy obtained after wavelet transform, kurtosis obtained after Laplacian of Gaussian transform, and skewness obtained after Laplacian of Gaussian transform were identified as independent prognostic factors for favorable stroke outcomes.
format Online
Article
Text
id pubmed-9250246
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-92502462022-07-03 MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke Zhang, Yuan Zhuang, Yuzhong Ge, Yaqiong Wu, Pu-Yeh Zhao, Jing Wang, Hao Song, Bin BMC Med Imaging Research BACKGROUND: This study aims is to explore whether it is feasible to use magnetic resonance texture analysis (MRTA) in order to distinguish favorable from unfavorable function outcomes and determine the prognostic factors associated with favorable outcomes of stroke. METHODS: The retrospective study included 103 consecutive patients who confirmed unilateral anterior circulation subacute ischemic stroke by computed tomography angiography between January 2018 and September 2019. Patients were divided into favorable outcome (modified Rankin scale, mRS ≤ 2) and unfavorable outcome (mRS > 2) groups according to mRS scores at day 90. Two radiologists manually segmented the infarction lesions based on diffusion-weighted imaging and transferred the images to corresponding apparent diffusion coefficient (ADC) maps in order to extract texture features. The prediction models including clinical characteristics and texture features were built using multiple logistic regression. A univariate analysis was conducted to assess the performance of the mean ADC value of the infarction lesion. A Delong’s test was used to compare the predictive performance of models through the receiver operating characteristic curve. RESULTS: The mean ADC performance was moderate [AUC = 0.60, 95% confidence interval (CI) 0.49–0.71]. The texture feature model of the ADC map (tADC), contained seven texture features, and presented good prediction performance (AUC = 0.83, 95%CI 0.75–0.91). The energy obtained after wavelet transform, and the kurtosis and skewness obtained after Laplacian of Gaussian transformation were identified as independent prognostic factors for the favorable stroke outcomes. In addition, the combination of the tADC model and clinical characteristics (hypertension, diabetes mellitus, smoking, and atrial fibrillation) exhibited a subtly better performance (AUC = 0.86, 95%CI 0.79–0.93; P > 0.05, Delong’s). CONCLUSION: The models based on MRTA on ADC maps are useful to evaluate the clinical function outcomes in patients with unilateral anterior circulation ischemic stroke. Energy obtained after wavelet transform, kurtosis obtained after Laplacian of Gaussian transform, and skewness obtained after Laplacian of Gaussian transform were identified as independent prognostic factors for favorable stroke outcomes. BioMed Central 2022-07-01 /pmc/articles/PMC9250246/ /pubmed/35778678 http://dx.doi.org/10.1186/s12880-022-00845-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Zhang, Yuan
Zhuang, Yuzhong
Ge, Yaqiong
Wu, Pu-Yeh
Zhao, Jing
Wang, Hao
Song, Bin
MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title_full MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title_fullStr MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title_full_unstemmed MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title_short MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke
title_sort mri whole-lesion texture analysis on adc maps for the prognostic assessment of ischemic stroke
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250246/
https://www.ncbi.nlm.nih.gov/pubmed/35778678
http://dx.doi.org/10.1186/s12880-022-00845-y
work_keys_str_mv AT zhangyuan mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT zhuangyuzhong mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT geyaqiong mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT wupuyeh mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT zhaojing mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT wanghao mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke
AT songbin mriwholelesiontextureanalysisonadcmapsfortheprognosticassessmentofischemicstroke