Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers

Abstract This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients wi...

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Autores principales:	Dixiang Song, Yixuan Zhai, Xiaogang Tao, Chao Zhao, Minkai Wang, Xinting Wei
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b22de609d9784200bdc8e7f85ef91a87
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spelling	oai:doaj.org-article:b22de609d9784200bdc8e7f85ef91a872021-12-02T15:15:04ZPrediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers10.1038/s41598-021-97865-52045-2322https://doaj.org/article/b22de609d9784200bdc8e7f85ef91a872021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97865-5https://doaj.org/toc/2045-2322Abstract This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.Dixiang SongYixuan ZhaiXiaogang TaoChao ZhaoMinkai WangXinting WeiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dixiang Song Yixuan Zhai Xiaogang Tao Chao Zhao Minkai Wang Xinting Wei Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
description	Abstract This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.
format	article
author	Dixiang Song Yixuan Zhai Xiaogang Tao Chao Zhao Minkai Wang Xinting Wei
author_facet	Dixiang Song Yixuan Zhai Xiaogang Tao Chao Zhao Minkai Wang Xinting Wei
author_sort	Dixiang Song
title	Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
title_short	Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
title_full	Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
title_fullStr	Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
title_full_unstemmed	Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
title_sort	prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b22de609d9784200bdc8e7f85ef91a87
work_keys_str_mv	AT dixiangsong predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers AT yixuanzhai predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers AT xiaogangtao predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers AT chaozhao predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers AT minkaiwang predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers AT xintingwei predictionofbloodsupplyinvestibularschwannomasusingradiomicsmachinelearningclassifiers
_version_	1718387593590079488

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers

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