Clinical and experimental medicine · 2025
Predicting sentinel lymph node metastasis in breast cancer: a study based on the SEER database.
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Li Qingyang, Xu Hu, Bao Baoshi, Xie Yujiao, Guo Shiqi, Gao Zhaofeng, Chen Siyi, Sun Jiahong, Zhu Li, Wang Jiandong
Аннотация
BACKGROUND: Sentinel lymph node biopsy (SLNB), a standard surgical procedure for clinically axillary-negative breast cancer patients, significantly reduces complications compared with axillary lymph node dissection, but it is still a relatively invasive procedure with some complications, affecting patient's quality of life. To identify patients who might benefit from avoiding SLNB, this study aimed to develop a nomogram for predicting sentinel lymph node metastasis (SLNM) in breast cancer patients using the SEER database.
METHODS: We identified breast cancer patients whose 1-5 lymph nodes were examined in the SEER database as those who underwent SLNB. Patients were randomly assigned to the training and validation cohorts at a 3:1 ratio. Univariate and multivariate logistic regression were used to evaluate the relationships between SLNM and patients' clinicopathological characteristics. A nomogram was constructed, and its performance was validated via ROC curves, calibration curves, and decision curve analysis.
RESULTS: Age, race, primary site, T stage, M stage, histological grade, pathological type, estrogen receptor status, and progesterone receptor status are independent predictive factors for SLNM in patients with breast cancer. We successfully developed a predictive nomogram for sentinel lymph node status, with AUC values of 0.711 and 0.700 for the training and validation cohorts, respectively.
CONCLUSION: Our study successfully established an SLNM nomogram that provides richer predictive information. The model exhibits good clinical efficacy and serves as a reference value for populations potentially exempt from SLNB.
METHODS: We identified breast cancer patients whose 1-5 lymph nodes were examined in the SEER database as those who underwent SLNB. Patients were randomly assigned to the training and validation cohorts at a 3:1 ratio. Univariate and multivariate logistic regression were used to evaluate the relationships between SLNM and patients' clinicopathological characteristics. A nomogram was constructed, and its performance was validated via ROC curves, calibration curves, and decision curve analysis.
RESULTS: Age, race, primary site, T stage, M stage, histological grade, pathological type, estrogen receptor status, and progesterone receptor status are independent predictive factors for SLNM in patients with breast cancer. We successfully developed a predictive nomogram for sentinel lymph node status, with AUC values of 0.711 and 0.700 for the training and validation cohorts, respectively.
CONCLUSION: Our study successfully established an SLNM nomogram that provides richer predictive information. The model exhibits good clinical efficacy and serves as a reference value for populations potentially exempt from SLNB.