Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases

Abstract

Abstract Background Early identification of axillary lymph node metastasis (ALNM) in breast cancer (BC) is still a clinical difficulty. There is still no good method to replace sentinel lymph node biopsy (SLNB). The purpose of our study was to develop and validate a nomogram to predict the probability of ALNM preoperatively based on ultrasonography (US) and clinicopathological features of primary tumors. Methods From September 2019 to April 2022, the preoperative US) and clinicopathological data of 1076 T1-T2 BC patients underwent surgical treatment were collected. Patients were divided into a training set (875 patients from September 2019 to October 2021) and a validation set (201 patients from November 2021 to April 2022). Patients were divided into positive and negative axillary lymph node (ALN) group according pathology of axillary surgery. Compared the US and clinicopathological features between the two groups. The risk factors for ALNM were determined using multivariate logistic regression analysis, and a nomogram was constructed. AUC and calibration were used to assess its performance. Results By univariate and multivariate logistic regression analysis, age (p = 0.009), histologic grades (p = 0.000), molecular subtypes (p = 0.000), tumor location (p = 0.000), maximum diameter (p = 0.000), spiculated margin (p = 0.000) and distance from the skin (p = 0.000) were independent risk factors of ALNM. Then a nomogram was developed. The model was good discriminating with an AUC of 0.705 and 0.745 for the training and validation set, respectively. And the calibration curves demonstrated high agreement. However, in further predicting a heavy nodal disease burden (> 2 nodes), none of the variables were significant. Conclusion This nomogram based on the US and clinicopathological data can predict the presence of ALNM good in T1-T2 BC patients. But it cannot effectively predict a heavy nodal disease burden (> 2 nodes).