Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma
Abstract Objectives Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and vali...
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-023-00991-x |
| _version_ | 1797859795417432064 |
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| author | Ping Yin Junwen Zhong Ying Liu Tao Liu Chao Sun Xiaoming Liu Jingjing Cui Lei Chen Nan Hong |
| author_facet | Ping Yin Junwen Zhong Ying Liu Tao Liu Chao Sun Xiaoming Liu Jingjing Cui Lei Chen Nan Hong |
| author_sort | Ping Yin |
| collection | DOAJ |
| description | Abstract Objectives Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. Methods 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (t radscore = -5.829, χ 2 ALP = 97.137, t size = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). Conclusion The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making. |
| first_indexed | 2024-04-09T21:35:22Z |
| format | Article |
| id | doaj.art-02ff991980a54aa78b2d0f010bf33884 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-04-09T21:35:22Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-02ff991980a54aa78b2d0f010bf338842023-03-26T11:19:38ZengBMCBMC Medical Imaging1471-23422023-03-012311810.1186/s12880-023-00991-xClinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcomaPing Yin0Junwen Zhong1Ying Liu2Tao Liu3Chao Sun4Xiaoming Liu5Jingjing Cui6Lei Chen7Nan Hong8Department of Radiology, Peking University People’s HospitalDepartment of Radiology, Peking University People’s HospitalDepartment of Radiology, Peking University People’s HospitalDepartment of Radiology, Peking University People’s HospitalDepartment of Radiology, Peking University People’s HospitalDepartment of Research and Development, United Imaging Intelligence (Beijing) Co.,LtdDepartment of Research and Development, United Imaging Intelligence (Beijing) Co.,LtdDepartment of Radiology, Peking University People’s HospitalDepartment of Radiology, Peking University People’s HospitalAbstract Objectives Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the important factors affecting the long-term survival of OS. To develop and validate machine learning radiomics model based on radiographic and clinical features that could predict LM in OS within 3 years. Methods 486 patients (LM = 200, non-LM = 286) with histologically proven OS were retrospectively analyzed and divided into a training set (n = 389) and a validation set (n = 97). Radiographic features and risk factors (sex, age, tumor location, etc.) associated with LM of patients were evaluated. We built eight clinical-radiomics models (k-nearest neighbor [KNN], logistic regression [LR], support vector machine [SVM], random forest [RF], Decision Tree [DT], Gradient Boosting Decision Tree [GBDT], AdaBoost, and extreme gradient boosting [XGBoost]) and compared their performance. The area under the receiver operating characteristic curve (AUC) and accuracy (ACC) were used to evaluate different models. Results The radscore, ALP, and tumor size had significant differences between the LM and non-LM groups (t radscore = -5.829, χ 2 ALP = 97.137, t size = -3.437, P < 0.01). Multivariable LR analyses showed that ALP was an important indicator for predicting LM of OS (odds ratio [OR] = 7.272, P < 0.001). Among the eight models, the SVM-based clinical-radiomics model had the best performance in the validation set (AUC = 0.807, ACC = 0.784). Conclusion The clinical-radiomics model had good performance in predicting LM in OS, which would be helpful in clinical decision-making.https://doi.org/10.1186/s12880-023-00991-xOsteosarcomaRadiomicsLung metastasisPlain radiographsMachine learning |
| spellingShingle | Ping Yin Junwen Zhong Ying Liu Tao Liu Chao Sun Xiaoming Liu Jingjing Cui Lei Chen Nan Hong Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma BMC Medical Imaging Osteosarcoma Radiomics Lung metastasis Plain radiographs Machine learning |
| title | Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma |
| title_full | Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma |
| title_fullStr | Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma |
| title_full_unstemmed | Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma |
| title_short | Clinical-radiomics models based on plain X-rays for prediction of lung metastasis in patients with osteosarcoma |
| title_sort | clinical radiomics models based on plain x rays for prediction of lung metastasis in patients with osteosarcoma |
| topic | Osteosarcoma Radiomics Lung metastasis Plain radiographs Machine learning |
| url | https://doi.org/10.1186/s12880-023-00991-x |
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