Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm
Abstract Purpose The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis. Methods In this machine learning study, we retrospectively co...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02493-4 |
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| author | Qianmei Yang Cuifang Liu Yongyue Wang Guifang Dong Jinghuan Sun |
| author_facet | Qianmei Yang Cuifang Liu Yongyue Wang Guifang Dong Jinghuan Sun |
| author_sort | Qianmei Yang |
| collection | DOAJ |
| description | Abstract Purpose The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis. Methods In this machine learning study, we retrospectively collected 225 female breast cancer patients who underwent sentinel lymph node biopsy (SLNB). Feature screening was performed using the logistic regression analysis. Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis. Results Combined univariate and multivariate logistic regression analysis, identified Multifocal, LVI, Maximum Diameter, Shape US, Maximum Cortical Thickness as significant predictors. We than successfully leveraged machine learning algorithms, particularly the RANDOM FOREST model, to develop a predictive model for sentinel lymph node metastasis in breast cancer. Finally, the SHAP method identified Maximum Diameter and Maximum Cortical Thickness as the primary decision factors influencing the ML model’s predictions. Conclusion With the integration of pathological and imaging characteristics, ML algorithm can accurately predict sentinel lymph node metastasis in breast cancer patients. The RANDOM FOREST model showed ideal performance. With the incorporation of these models in the clinic, can helpful for clinicians to identify patients at risk of sentinel lymph node metastasis of breast cancer and make more reasonable treatment decisions. |
| format | Article |
| id | doaj-art-a3dc2dc4e7e14b95847098750ef1dc7e |
| institution | OA Journals |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-a3dc2dc4e7e14b95847098750ef1dc7e2025-08-20T01:49:37ZengSpringerDiscover Oncology2730-60112025-05-0116111510.1007/s12672-025-02493-4Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithmQianmei Yang0Cuifang Liu1Yongyue Wang2Guifang Dong3Jinghuan Sun4Department of Ultrasound, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Hospital of Traditional Chinese MedicineDepartment of Radiology, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Hospital of Traditional Chinese MedicineDepartment of Mammary Gland, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Hospital of Traditional Chinese MedicineDepartment of Ultrasound, The First Affiliated Hospital of Chongqing University of Chinese Medicine, Chongqing Hospital of Traditional Chinese MedicineDepartment of Traditional Chinese Medicine, ChongQing JiangJin District Hospital of Chinese Medicine (Jiangjin Hospital, Chongqing University of Chinese Medicin)Abstract Purpose The aim of this study was to develop and validate a machine learning (ML) based prediction model for sentinel lymph node metastasis in breast cancer to identify patients with a high risk of sentinel lymph node metastasis. Methods In this machine learning study, we retrospectively collected 225 female breast cancer patients who underwent sentinel lymph node biopsy (SLNB). Feature screening was performed using the logistic regression analysis. Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. In addition, model interpretation was performed by the Shapley Additive Explanations (SHAP) analysis to clarify the importance of each feature of the model and its decision basis. Results Combined univariate and multivariate logistic regression analysis, identified Multifocal, LVI, Maximum Diameter, Shape US, Maximum Cortical Thickness as significant predictors. We than successfully leveraged machine learning algorithms, particularly the RANDOM FOREST model, to develop a predictive model for sentinel lymph node metastasis in breast cancer. Finally, the SHAP method identified Maximum Diameter and Maximum Cortical Thickness as the primary decision factors influencing the ML model’s predictions. Conclusion With the integration of pathological and imaging characteristics, ML algorithm can accurately predict sentinel lymph node metastasis in breast cancer patients. The RANDOM FOREST model showed ideal performance. With the incorporation of these models in the clinic, can helpful for clinicians to identify patients at risk of sentinel lymph node metastasis of breast cancer and make more reasonable treatment decisions.https://doi.org/10.1007/s12672-025-02493-4Machine learningSentinel lymph node metastasesPredictive modelBreast cancer |
| spellingShingle | Qianmei Yang Cuifang Liu Yongyue Wang Guifang Dong Jinghuan Sun Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm Discover Oncology Machine learning Sentinel lymph node metastases Predictive model Breast cancer |
| title | Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| title_full | Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| title_fullStr | Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| title_full_unstemmed | Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| title_short | Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| title_sort | construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm |
| topic | Machine learning Sentinel lymph node metastases Predictive model Breast cancer |
| url | https://doi.org/10.1007/s12672-025-02493-4 |
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