MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases
Abstract Objective This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). Methods Patients with...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-10-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-023-01523-5 |
| _version_ | 1827634049638727680 |
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| author | Yongye Chen Siyuan Qin Weili Zhao Qizheng Wang Ke Liu Peijin Xin Huishu Yuan Hongqing Zhuang Ning Lang |
| author_facet | Yongye Chen Siyuan Qin Weili Zhao Qizheng Wang Ke Liu Peijin Xin Huishu Yuan Hongqing Zhuang Ning Lang |
| author_sort | Yongye Chen |
| collection | DOAJ |
| description | Abstract Objective This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). Methods Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis. Results We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745–0.825). The combined model achieved the best performance (AUC = 0.828). Conclusion The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT. Critical relevance statement Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT. Key points • Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes. Graphical Abstract |
| first_indexed | 2024-03-09T15:07:59Z |
| format | Article |
| id | doaj.art-961d03f856634371b1c6bc5066f559a6 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-09T15:07:59Z |
| publishDate | 2023-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-961d03f856634371b1c6bc5066f559a62023-11-26T13:33:08ZengSpringerOpenInsights into Imaging1869-41012023-10-0114111110.1186/s13244-023-01523-5MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastasesYongye Chen0Siyuan Qin1Weili Zhao2Qizheng Wang3Ke Liu4Peijin Xin5Huishu Yuan6Hongqing Zhuang7Ning Lang8Department of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalDepartment of radiotherapy, Peking University Third HospitalDepartment of Radiology, Peking University Third HospitalAbstract Objective This study aimed to extract radiomics features from MRI using machine learning (ML) algorithms and integrate them with clinical features to build response prediction models for patients with spinal metastases undergoing stereotactic body radiotherapy (SBRT). Methods Patients with spinal metastases who were treated using SBRT at our hospital between July 2018 and April 2023 were recruited. We assessed their response to treatment using the revised Response Evaluation Criteria in Solid Tumors (version 1.1). The lesions were categorized into progressive disease (PD) and non-PD groups. Radiomics features were extracted from T1-weighted image (T1WI), T2-weighted image (T2WI), and fat-suppression T2WI sequences. Feature selection involved intraclass correlation coefficients, minimal-redundancy-maximal-relevance, and least absolute shrinkage and selection operator methods. Thirteen ML algorithms were employed to construct the radiomics prediction models. Clinical, conventional imaging, and radiomics features were integrated to develop combined models. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the clinical value was assessed using decision curve analysis. Results We included 194 patients with 142 (73.2%) lesions in the non-PD group and 52 (26.8%) in the PD group. Each region of interest generated 2264 features. The clinical model exhibited a moderate predictive value (area under the ROC curve, AUC = 0.733), while the radiomics models demonstrated better performance (AUC = 0.745–0.825). The combined model achieved the best performance (AUC = 0.828). Conclusion The MRI-based radiomics models exhibited valuable predictive capability for treatment outcomes in patients with spinal metastases undergoing SBRT. Critical relevance statement Radiomics prediction models have the potential to contribute to clinical decision-making and improve the prognosis of patients with spinal metastases undergoing SBRT. Key points • Stereotactic body radiotherapy effectively delivers high doses of radiation to treat spinal metastases. • Accurate prediction of treatment outcomes has crucial clinical significance. • MRI-based radiomics models demonstrated good performance to predict treatment outcomes. Graphical Abstracthttps://doi.org/10.1186/s13244-023-01523-5SpineNeoplasm metastasisMRIRadiosurgeryTreatment outcome |
| spellingShingle | Yongye Chen Siyuan Qin Weili Zhao Qizheng Wang Ke Liu Peijin Xin Huishu Yuan Hongqing Zhuang Ning Lang MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases Insights into Imaging Spine Neoplasm metastasis MRI Radiosurgery Treatment outcome |
| title | MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| title_full | MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| title_fullStr | MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| title_full_unstemmed | MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| title_short | MRI feature-based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| title_sort | mri feature based radiomics models to predict treatment outcome after stereotactic body radiotherapy for spinal metastases |
| topic | Spine Neoplasm metastasis MRI Radiosurgery Treatment outcome |
| url | https://doi.org/10.1186/s13244-023-01523-5 |
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