The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures
Abstract Purpose To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). Materials and methods 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corr...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Musculoskeletal Disorders |
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| Online Access: | https://doi.org/10.1186/s12891-023-06939-0 |
| _version_ | 1797578705596317696 |
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| author | Wu-Gen Li Rou Zeng Yong Lu Wei-Xiang Li Tong-Tong Wang Huashan Lin Yun Peng Liang-Geng Gong |
| author_facet | Wu-Gen Li Rou Zeng Yong Lu Wei-Xiang Li Tong-Tong Wang Huashan Lin Yun Peng Liang-Geng Gong |
| author_sort | Wu-Gen Li |
| collection | DOAJ |
| description | Abstract Purpose To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). Materials and methods 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. Results For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. Conclusion Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury. |
| first_indexed | 2024-03-10T22:25:35Z |
| format | Article |
| id | doaj.art-e374516e4c5d4f72bdf53633386b76d3 |
| institution | Directory Open Access Journal |
| issn | 1471-2474 |
| language | English |
| last_indexed | 2024-03-10T22:25:35Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Musculoskeletal Disorders |
| spelling | doaj.art-e374516e4c5d4f72bdf53633386b76d32023-11-19T12:08:13ZengBMCBMC Musculoskeletal Disorders1471-24742023-10-012411910.1186/s12891-023-06939-0The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fracturesWu-Gen Li0Rou Zeng1Yong Lu2Wei-Xiang Li3Tong-Tong Wang4Huashan Lin5Yun Peng6Liang-Geng Gong7Department of Radiology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Radiology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Radiology, Xinjian County People’s HospitalDepartment of Radiology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Radiology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Pharmaceuticals Diagnosis, GE HealthcareDepartment of Radiology, the Second Affiliated Hospital of Nanchang UniversityDepartment of Radiology, the Second Affiliated Hospital of Nanchang UniversityAbstract Purpose To develop and evaluate the performance of radiomics-based computed tomography (CT) combined with machine learning algorithms in detecting occult vertebral fractures (OVFs). Materials and methods 128 vertebrae including 64 with OVF confirmed by magnetic resonance imaging and 64 corresponding control vertebrae from 57 patients who underwent chest/abdominal CT scans, were included. The CT radiomics features on mid-axial and mid-sagittal plane of each vertebra were extracted. The fractured and normal vertebrae were randomly divided into training set and validation set at a ratio of 8:2. Pearson correlation analyses and least absolute shrinkage and selection operator were used for selecting sagittal and axial features, respectively. Three machine-learning algorithms were used to construct the radiomics models based on the residual features. Receiver operating characteristic (ROC) analysis was used to verify the performance of model. Results For mid-axial CT imaging, 6 radiomics parameters were obtained and used for building the models. The logistic regression (LR) algorithm showed the best performance with area under the ROC curves (AUC) of training and validation sets of 0.682 and 0.775. For mid-sagittal CT imaging, 5 parameters were selected, and LR algorithms showed the best performance with AUC of training and validation sets of 0.832 and 0.882. The LR model based on sagittal CT yielded the best performance, with an accuracy of 0.846, sensitivity of 0.846, and specificity of 0.846. Conclusion Machine learning based on CT radiomics features allows for the detection of OVFs, especially the LR model based on the radiomics of sagittal imaging, which indicates it is promising to further combine with deep learning to achieve automatic recognition of OVFs to reduce the associated secondary injury.https://doi.org/10.1186/s12891-023-06939-0Machine learningOccult vertebral fracturesRadiomicsCT |
| spellingShingle | Wu-Gen Li Rou Zeng Yong Lu Wei-Xiang Li Tong-Tong Wang Huashan Lin Yun Peng Liang-Geng Gong The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures BMC Musculoskeletal Disorders Machine learning Occult vertebral fractures Radiomics CT |
| title | The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures |
| title_full | The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures |
| title_fullStr | The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures |
| title_full_unstemmed | The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures |
| title_short | The value of radiomics-based CT combined with machine learning in the diagnosis of occult vertebral fractures |
| title_sort | value of radiomics based ct combined with machine learning in the diagnosis of occult vertebral fractures |
| topic | Machine learning Occult vertebral fractures Radiomics CT |
| url | https://doi.org/10.1186/s12891-023-06939-0 |
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