Clinical breast cancer · 2026
Noninvasive Prediction of Axillary Sentinel Lymph Node Metastasis via Contrast-Enhanced Ultrasound to Guide Omission of SLNB in Breast Cancer.
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He Zhijian, Li Xiaoyang, He Jun, Huang Peizhen, Li Rizeng, Yu Jian
Аннотация
BACKGROUND: The evaluation of axillary sentinel lymph node (SLN) is integral to the treatment of breast cancer. This study aims to build a noninvasive prediction model of SLN metastasis based on percutaneous contrast-enhanced ultrasound (p-CEUS) for low-risk patients.
MATERIALS AND METHODS: Patients with breast cancer were enrolled in this study at Wenzhou Central Hospital between June 2023 and October 2024. The patients were divided into a modeling group and a validation group in a 2:1 ratio. Clinical and pathological features were assessed with univariate analysis and multivariate logistic regression. A nomogram based on p-CEUS enhancement patterns and other independent predictors for the SLN metastasis identified by multivariate logistic regression was constructed.
RESULTS: A total of 120 patients were included, comprising 80 in the modeling group (mean age, 55.01 ± 9.91 years) and 40 in the validation group (mean age, 55.20 ± 8.35 years). Independent predictors of SLN metastasis by the multivariate logistic regression analysis included tumor size, Ki-67 status and p-CEUS enhancement pattern. The areas under the receiver operating characteristic (ROC) curve of the modeling group and the validation group were 0.855 and 0.873, respectively. At a ≤ 20% probability threshold, the false-negative rate was 6.5%.
CONCLUSIONS: The p-CEUS-based nomogram can accurately predict the risk of SLN metastasis in early breast cancer patients with negative-node status. Patients with predicted metastasis probability ≤ 20%, especially those with tumor size ≤ 2 cm, Ki-67 ≤ 20%, and p-CEUS Type I enhancement, can safely omit SLNB.
MATERIALS AND METHODS: Patients with breast cancer were enrolled in this study at Wenzhou Central Hospital between June 2023 and October 2024. The patients were divided into a modeling group and a validation group in a 2:1 ratio. Clinical and pathological features were assessed with univariate analysis and multivariate logistic regression. A nomogram based on p-CEUS enhancement patterns and other independent predictors for the SLN metastasis identified by multivariate logistic regression was constructed.
RESULTS: A total of 120 patients were included, comprising 80 in the modeling group (mean age, 55.01 ± 9.91 years) and 40 in the validation group (mean age, 55.20 ± 8.35 years). Independent predictors of SLN metastasis by the multivariate logistic regression analysis included tumor size, Ki-67 status and p-CEUS enhancement pattern. The areas under the receiver operating characteristic (ROC) curve of the modeling group and the validation group were 0.855 and 0.873, respectively. At a ≤ 20% probability threshold, the false-negative rate was 6.5%.
CONCLUSIONS: The p-CEUS-based nomogram can accurately predict the risk of SLN metastasis in early breast cancer patients with negative-node status. Patients with predicted metastasis probability ≤ 20%, especially those with tumor size ≤ 2 cm, Ki-67 ≤ 20%, and p-CEUS Type I enhancement, can safely omit SLNB.