Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia
The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1188590/full |
| _version_ | 1827796789192818688 |
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| author | Fuxu Wang Anbang Ma Zeyu Wu Mingchen Xie Peng Lun Peng Sun |
| author_facet | Fuxu Wang Anbang Ma Zeyu Wu Mingchen Xie Peng Lun Peng Sun |
| author_sort | Fuxu Wang |
| collection | DOAJ |
| description | The study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model’s diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder. |
| first_indexed | 2024-03-11T19:12:02Z |
| format | Article |
| id | doaj.art-5864efdb07d547b39de79bd9bb9ed9d7 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-11T19:12:02Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-5864efdb07d547b39de79bd9bb9ed9d72023-10-09T11:02:37ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-10-011710.3389/fnins.2023.11885901188590Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgiaFuxu Wang0Anbang Ma1Zeyu Wu2Mingchen Xie3Peng Lun4Peng Sun5Department of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, ChinaShanghai Xunshi Technology Co., Ltd., Shanghai, ChinaDepartment of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, ChinaDepartment of Neurosurgery, Affiliated Hospital of Qingdao University, Qingdao, ChinaThe study aims to develop a magnetic resonance imaging (MRI)-based radiomics model for the diagnosis of classic trigeminal neuralgia (cTN). This study involved 350 patients with cTN and 100 control participants. MRI data were collected retrospectively for all the enrolled subjects. The symptomatic side trigeminal nerve regions of patients and both sides of the trigeminal nerve regions of control participants were manually labeled on MRI images. Radiomics features of the areas labeled were extracted. Principle component analysis (PCA) and least absolute shrinkage and selection operator (LASSO) regression were utilized as the preliminary feature reduction methods to decrease the high dimensionality of radiomics features. Machine learning methods were established, including LASSO logistic regression, support vector machine (SVM), and Adaboost methods, evaluating each model’s diagnostic abilities using 10-fold cross-validation. All the models showed excellent diagnostic ability in predicting trigeminal neuralgia. A prospective study was conducted, 20 cTN patients and 20 control subjects were enrolled to validate the clinical utility of all models. Results showed that the radiomics models based on MRI can predict trigeminal neuralgia with high accuracy, which could be used as a diagnostic tool for this disorder.https://www.frontiersin.org/articles/10.3389/fnins.2023.1188590/fulltrigeminal neuralgiaMRIradiologymachine learningdiagnosis |
| spellingShingle | Fuxu Wang Anbang Ma Zeyu Wu Mingchen Xie Peng Lun Peng Sun Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia Frontiers in Neuroscience trigeminal neuralgia MRI radiology machine learning diagnosis |
| title | Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| title_full | Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| title_fullStr | Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| title_full_unstemmed | Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| title_short | Development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| title_sort | development and validation of radiomics models for the prediction of diagnosis of classic trigeminal neuralgia |
| topic | trigeminal neuralgia MRI radiology machine learning diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1188590/full |
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