Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases
Abstract The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-le...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80210-x |
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| author | Huai-wen Zhang Yi-ren Wang Bo Hu Bo Song Zhong-jian Wen Lei Su Xiao-man Chen Xi Wang Ping Zhou Xiao-ming Zhong Hao-wen Pang You-hua Wang |
| author_facet | Huai-wen Zhang Yi-ren Wang Bo Hu Bo Song Zhong-jian Wen Lei Su Xiao-man Chen Xi Wang Ping Zhou Xiao-ming Zhong Hao-wen Pang You-hua Wang |
| author_sort | Huai-wen Zhang |
| collection | DOAJ |
| description | Abstract The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features. |
| format | Article |
| id | doaj-art-727ddab7dd2c46b3843d06cc3f9873a8 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-727ddab7dd2c46b3843d06cc3f9873a82025-08-20T02:22:21ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-80210-xUsing machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastasesHuai-wen Zhang0Yi-ren Wang1Bo Hu2Bo Song3Zhong-jian Wen4Lei Su5Xiao-man Chen6Xi Wang7Ping Zhou8Xiao-ming Zhong9Hao-wen Pang10You-hua Wang11Department of Radiotherapy, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer HospitalSchool of Nursing, Southwest Medical UniversityKey Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hang Kong UniversityDepartment of Neurosurgery, Jingdezhen No.1 People’s HospitalSchool of Nursing, Southwest Medical UniversitySchool of Medical Information and Engineering, Southwest Medical UniversitySchool of Nursing, Southwest Medical UniversityDepartment of Radiotherapy, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer HospitalDepartment of Nursing, The Affiliated Hospital of Southwest Medical UniversityDepartment of Radiotherapy, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer HospitalDepartment of Oncology, The Affiliated Hospital of Southwest Medical UniversityDepartment of Oncology, Gulin County People’s HospitalAbstract The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decision tree, artificial neural network, k-nearest neighbors, LightGBM, and CatBoost algorithms, a stacking ensemble model was developed to classify gross tumor volume (GTV), brainstem, and normal brain tissue based on radiomic features. Multiple evaluation metrics, including the specificity, sensitivity, negative predictive value, positive predictive value, accuracy, Matthews correlation coefficient, and the Youden index, were used to assess the model’s performance. The stacked ensemble model integrated the strengths of the nine base models and consistently outperformed individual base models in classifying GTV (area under the curve [AUC] = 0.928), brainstem (AUC = 0.932), and normal brain tissue (AUC = 0.942). Among the base models, the support vector machine model demonstrated the best performance in the three classifications (AUC = 0.922, 0.909, and 0.928). The higher performance of the stacked ensemble model highlighted the low performance of other models, including the decision tree (AUC = 0.709, 0.706, 0.804) and k-nearest neighbors (AUC = 0.721, 0.663, 0.729) models in certain contexts, such as when faced with high-dimensional feature spaces. While machine learning shows significant promise in medical image analysis, relying solely on a single model may lead to suboptimal results. By combining the strengths of various algorithms, the stacking ensemble model offers a better solution for the classification of brain metastases based on radiomic features.https://doi.org/10.1038/s41598-024-80210-xArtificial intelligenceMachine learningPrediction modelRadiomicsStacking ensemble learning |
| spellingShingle | Huai-wen Zhang Yi-ren Wang Bo Hu Bo Song Zhong-jian Wen Lei Su Xiao-man Chen Xi Wang Ping Zhou Xiao-ming Zhong Hao-wen Pang You-hua Wang Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases Scientific Reports Artificial intelligence Machine learning Prediction model Radiomics Stacking ensemble learning |
| title | Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases |
| title_full | Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases |
| title_fullStr | Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases |
| title_full_unstemmed | Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases |
| title_short | Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases |
| title_sort | using machine learning to develop a stacking ensemble learning model for the ct radiomics classification of brain metastases |
| topic | Artificial intelligence Machine learning Prediction model Radiomics Stacking ensemble learning |
| url | https://doi.org/10.1038/s41598-024-80210-x |
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