Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning
Abstract This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epid...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-45438-z |
| _version_ | 1797559893569306624 |
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| author | Xugang Zhong Yanze Lin Wei Zhang Qing Bi |
| author_facet | Xugang Zhong Yanze Lin Wei Zhang Qing Bi |
| author_sort | Xugang Zhong |
| collection | DOAJ |
| description | Abstract This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients. |
| first_indexed | 2024-03-10T17:51:40Z |
| format | Article |
| id | doaj.art-423967c1b5924c7980d1678f2ef31aa6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:51:40Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-423967c1b5924c7980d1678f2ef31aa62023-11-20T09:19:59ZengNature PortfolioScientific Reports2045-23222023-10-0113112010.1038/s41598-023-45438-zPredicting diagnosis and survival of bone metastasis in breast cancer using machine learningXugang Zhong0Yanze Lin1Wei Zhang2Qing Bi3Center for Rehabilitation Medicine, Cancer Center, Department of Orthopedics, Zhejiang Provincial People’s Hospital Affiliated to Qingdao UniversityCenter for Rehabilitation Medicine, Cancer Center, Department of Orthopedics, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical CollegeCenter for Rehabilitation Medicine, Cancer Center, Department of Orthopedics, Zhejiang Provincial People’s Hospital Affiliated to Qingdao UniversityCenter for Rehabilitation Medicine, Cancer Center, Department of Orthopedics, Zhejiang Provincial People’s Hospital Affiliated to Qingdao UniversityAbstract This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients.https://doi.org/10.1038/s41598-023-45438-z |
| spellingShingle | Xugang Zhong Yanze Lin Wei Zhang Qing Bi Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning Scientific Reports |
| title | Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| title_full | Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| title_fullStr | Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| title_full_unstemmed | Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| title_short | Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| title_sort | predicting diagnosis and survival of bone metastasis in breast cancer using machine learning |
| url | https://doi.org/10.1038/s41598-023-45438-z |
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