Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics
Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients wit...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/16/2627 |
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| author | Beisheng Yang Wenjie Li Xiaojia Wu Weijia Zhong Jing Wang Yu Zhou Tianxing Huang Lu Zhou Zhiming Zhou |
| author_facet | Beisheng Yang Wenjie Li Xiaojia Wu Weijia Zhong Jing Wang Yu Zhou Tianxing Huang Lu Zhou Zhiming Zhou |
| author_sort | Beisheng Yang |
| collection | DOAJ |
| description | Different machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures. |
| first_indexed | 2024-03-11T00:00:08Z |
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| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T00:00:08Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj.art-4e78a6c5d98b4cc9989c9f87cedbacf32023-11-19T00:47:49ZengMDPI AGDiagnostics2075-44182023-08-011316262710.3390/diagnostics13162627Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and RadiomicsBeisheng Yang0Wenjie Li1Xiaojia Wu2Weijia Zhong3Jing Wang4Yu Zhou5Tianxing Huang6Lu Zhou7Zhiming Zhou8Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDepartment of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400000, ChinaDifferent machine learning algorithms have different characteristics and applicability. This study aims to predict ruptured intracranial aneurysms by radiomics models based on different machine learning algorithms and evaluate their differences in the same data condition. A total of 576 patients with intracranial aneurysms (192 ruptured and 384 unruptured intracranial aneurysms) from two institutions are included and randomly divided into training and validation cohorts in a ratio of 7:3. Of the 107 radiomics features extracted from computed tomography angiography images, seven features stood out. Then, radiomics features and 12 common machine learning algorithms, including the decision-making tree, support vector machine, logistic regression, Gaussian Naive Bayes, k-nearest neighbor, random forest, extreme gradient boosting, bagging classifier, AdaBoost, gradient boosting, light gradient boosting machine, and CatBoost were applied to construct models for predicting ruptured intracranial aneurysms, and the predictive performance of all models was compared. In the validation cohort, the area under curve (AUC) values of models based on AdaBoost, gradient boosting, and CatBoost for predicting ruptured intracranial aneurysms were 0.889, 0.883, and 0.864, respectively, with no significant differences among them. Of note, the performance of these models was significantly superior to that of the other nine models. The AUC of the AdaBoost model in the cross-validation was within the range of 0.842 to 0.918. Radiomics models based on the machine learning algorithms can be used to predict ruptured intracranial aneurysms, and the prediction efficacy differs among machine learning algorithms. The boosting algorithms might be superior in the application of radiomics combined with the machine learning algorithm to predict aneurysm ruptures.https://www.mdpi.com/2075-4418/13/16/2627intracranial aneurysmruptureradiomicsmachine learningcomputed tomography angiography |
| spellingShingle | Beisheng Yang Wenjie Li Xiaojia Wu Weijia Zhong Jing Wang Yu Zhou Tianxing Huang Lu Zhou Zhiming Zhou Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics Diagnostics intracranial aneurysm rupture radiomics machine learning computed tomography angiography |
| title | Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics |
| title_full | Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics |
| title_fullStr | Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics |
| title_full_unstemmed | Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics |
| title_short | Comparison of Ruptured Intracranial Aneurysms Identification Using Different Machine Learning Algorithms and Radiomics |
| title_sort | comparison of ruptured intracranial aneurysms identification using different machine learning algorithms and radiomics |
| topic | intracranial aneurysm rupture radiomics machine learning computed tomography angiography |
| url | https://www.mdpi.com/2075-4418/13/16/2627 |
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