Objective:To explore the diagnostic efficacy of percutaneous lymphatic contrast ultrasound (LCEUS) combined with clinicopathological features in the diagnosis of breast cancer sentinel lymph node (SLN).Methods:A total of 135 breast cancer patients who underwent sentinel lymph node biopsy and axillary lymph node radical resection were prospectively collected in the Affiliated Hospital of Nanjing University of Chinese Medicine from July 2018 to June 2021, and the breast masses and SLNs were evaluated by routine ultrasound, contrast-enhanced ultrasound and LCEUS within one week before surgery. The surgeons recorded the patients′ clinical characteristics before surgeries, and the pathology of the masses and SLNs were recorded after surgeries. Univariate analysis and multivariate Logistic regression analysis were used to explore the correlation between ultrasound clinicopathological features of breast cancer and SLN metastasis, then to establish a model, and evaluate the diagnostic efficacy of the model.Results:Univariate analysis showed that SLN metastasis of breast cancer was associated with age, axillary palpation of enlarged lymph nodes, pathological type of mass, clear hilum of lymph nodes, cortical thickening of lymph nodes, marginal blood flow in lymph nodes, and the appearances of LCEUS(all P<0.05). Multivariate Logistic regression analysis showed age, palpation of axillary lymph nodes and the appearances of LCEUS were independent predictors of SLN properties, the OR values were 6.90 ( P=0.030), 16.06 ( P<0.001) and 12.71 ( P<0.001), respectively. The regression equation was Logit(P)=0.887+ 1.932× axillary lymph node palpation + 2.776× marginal blood flow + 2.542×LCEUS. Conclusions:LCEUS combined with marginal blood flow in lymph nodes and palpation of axillary lymph nodes can help to determine the SLN state.

Objective:To analyze the multimodal ultrasound characteristics of primary breast cancer and sentinel lymph node (SLN) and to establish a nomogram model for predicting SLN metastasis in invasive breast cancer, thereby providing reference for precise clinical diagnosis and treatment.Methods:A total of 329 patients diagnosed with invasive breast cancer and admitted to the Affiliated Hospital of Nanjing University of Chinese Medicine from June 2018 to October 2023 were retrospectively enrolled. They were randomly divided into a training cohort ( n=230) and a validation cohort ( n=99) in a ratio of 7 to 3. In the training cohort, ultrasound findings and clinical parameters were analyzed, univariate and multivariate Logistic regression analyses were used to identify independent predictive factors for SLN metastasis, and a nomogram model was constructed based on these factors. The ROC curve, calibration curve, and decision curve analysis (DCA) were plotted between the training and validation cohorts to assess the discrimination, calibration, and clinical applicability of the nomogram model. Results:Regression analysis identified 3 independent risk factors for establishing the nomogram prediction model: ratio of the long diameter to the short diameter of SLN ( P=0.020), lymphatic contrast-enhanced ultrasound (LCEUS) enhancement pattern ( P<0.001) and intravenous contrast-enhanced ultrasound (ICEUS) enhancement mode ( P=0.002). The area under the curve (AUC) of the training cohort was 0.888, the accuracy was 0.865; the AUC of the validation cohort was 0.870, the accuracy was 0.859, demonstrating good predictive performance of the model in both cohorts. The calibration curve demonstrated that the nomogram has a strong concordance between predicted and actual probability. DCA demonstrated that the nomogram could increase net benefit within a certain probability threshold range. Conclusions:The nomogram based on ratio of the long diameter to the short diameter of SLN, LCEUS enhancement pattern and ICEUS enhancement mode can effectively predict SLN status in patients with invasive breast cancer, facilitating precise diagnosis and treatment.