Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities

Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images.Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training...

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Autores principales:	Yuan Shao, Jingru Ruan, Yuyun Xu, Zhenyu Shu, Xiaodong He
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	white matter penumbra radiomics magnetic resonance imaging texture analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/58eb88fb16c6427f932822d97463becd
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spelling	oai:doaj.org-article:58eb88fb16c6427f932822d97463becd2021-12-01T22:11:10ZComparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities1662-519610.3389/fninf.2021.789295https://doaj.org/article/58eb88fb16c6427f932822d97463becd2021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.789295/fullhttps://doaj.org/toc/1662-5196Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images.Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups. Two regions of interest (ROIs)—ROI whole-brain white matter (WBWM) and ROI WMH penumbra (WMHp)—were segmented from the baseline image. For predicting WMH progression, logistic regression was applied to create radiomic models in the two ROIs. Then, age, sex, clinical course, vascular risk factors, and imaging factors were incorporated into a stepwise regression analysis to construct the combined diagnosis model. Finally, the presence of a correlation between radiomic findings and the speed of progression was analyzed.Results: The area under the curve (AUC) was higher for the WMHp-based radiomic model than the WBWM-based radiomic model in training, testing, and validation groups (0.791, 0.768, and 0.767 vs. 0.725, 0.693, and 0.691, respectively). The WBWM-based combined model was established by combining age, hypertension, and rad-score of the ROI WBWM. Also, the WMHp-based combined model is built by combining the age and rad-score of the ROI WMHp. Compared with the WBWM-based model (AUC = 0.779, 0.716, 0.673 in training, testing, and validation groups, respectively), the WMHp-based combined model has higher diagnostic efficiency and better generalization ability (AUC = 0.793, 0.774, 0.777 in training, testing, and validation groups, respectively). The speed of WMH progression was related to the rad-score from ROI WMHp (r = 0.49) but not from ROI WBWM.Conclusion: The heterogeneity of the penumbra could help identify the individuals at high risk of WMH progression and the rad-score of it was correlated with the speed of progression.Yuan ShaoJingru RuanYuyun XuZhenyu ShuXiaodong HeFrontiers Media S.A.articlewhite matterpenumbraradiomicsmagnetic resonance imagingtexture analysisNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	white matter penumbra radiomics magnetic resonance imaging texture analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	white matter penumbra radiomics magnetic resonance imaging texture analysis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Yuan Shao Jingru Ruan Yuyun Xu Zhenyu Shu Xiaodong He Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
description	Purpose: The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images.Methods: In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups. Two regions of interest (ROIs)—ROI whole-brain white matter (WBWM) and ROI WMH penumbra (WMHp)—were segmented from the baseline image. For predicting WMH progression, logistic regression was applied to create radiomic models in the two ROIs. Then, age, sex, clinical course, vascular risk factors, and imaging factors were incorporated into a stepwise regression analysis to construct the combined diagnosis model. Finally, the presence of a correlation between radiomic findings and the speed of progression was analyzed.Results: The area under the curve (AUC) was higher for the WMHp-based radiomic model than the WBWM-based radiomic model in training, testing, and validation groups (0.791, 0.768, and 0.767 vs. 0.725, 0.693, and 0.691, respectively). The WBWM-based combined model was established by combining age, hypertension, and rad-score of the ROI WBWM. Also, the WMHp-based combined model is built by combining the age and rad-score of the ROI WMHp. Compared with the WBWM-based model (AUC = 0.779, 0.716, 0.673 in training, testing, and validation groups, respectively), the WMHp-based combined model has higher diagnostic efficiency and better generalization ability (AUC = 0.793, 0.774, 0.777 in training, testing, and validation groups, respectively). The speed of WMH progression was related to the rad-score from ROI WMHp (r = 0.49) but not from ROI WBWM.Conclusion: The heterogeneity of the penumbra could help identify the individuals at high risk of WMH progression and the rad-score of it was correlated with the speed of progression.
format	article
author	Yuan Shao Jingru Ruan Yuyun Xu Zhenyu Shu Xiaodong He
author_facet	Yuan Shao Jingru Ruan Yuyun Xu Zhenyu Shu Xiaodong He
author_sort	Yuan Shao
title	Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
title_short	Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
title_full	Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
title_fullStr	Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
title_full_unstemmed	Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities
title_sort	comparing the performance of two radiomic models to predict progression and progression speed of white matter hyperintensities
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/58eb88fb16c6427f932822d97463becd
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Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities

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