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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-97865-5 |
| _version_ | 1818422562095366144 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T13:28:13Z |
| format | Article |
| id | doaj.art-b22de609d9784200bdc8e7f85ef91a87 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T13:28:13Z |
| publishDate | 2021-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b22de609d9784200bdc8e7f85ef91a872022-12-21T22:59:47ZengNature PortfolioScientific Reports2045-23222021-09-011111910.1038/s41598-021-97865-5Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiersDixiang Song0Yixuan Zhai1Xiaogang Tao2Chao Zhao3Minkai Wang4Xinting Wei5Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Neurosurgery, The First Affiliated Hospital of Zhengzhou UniversityAbstract 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.https://doi.org/10.1038/s41598-021-97865-5 |
| spellingShingle | Dixiang Song Yixuan Zhai Xiaogang Tao Chao Zhao Minkai Wang Xinting Wei Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers Scientific Reports |
| title | 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_short | 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 |
| url | https://doi.org/10.1038/s41598-021-97865-5 |
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