A Model Incorporating Axillary Tail Position on Mammography for Preoperative Prediction of Non-sentinel Lymph Node Metastasis in Patients with Initial cN+ Breast Cancer after Neoadjuvant Chemotherapy.

RATIONALE AND OBJECTIVES

This study aimed to develop a model incorporating axillary tail position on mammography (AT) for the prediction of non-sentinel Lymph Node (NSLN) metastasis in patients with initial clinical node positivity (cN+).

METHODS AND MATERIALS

The study reviewed a total of 257 patients with cN+ breast cancer who underwent both sentinel lymph node biopsy (SLNB) and axillary lymph node dissection (ALND) following neoadjuvant chemotherapy (NAC). A logistic regression model was developed based on these factors and the results of post-NAC AT and axillary ultrasound (AUS).

RESULTS

Four clinical factors with p<0.1 in the univariate analysis, including ycT0(odds ratio [OR]: 4.84, 95% confidence interval [CI]: 2.13-11.91, p<0.001), clinical stage before NAC (OR: 2.68, 95%CI: 1.15-6.58, p=0.025), estrogen receptor (ER) expression (OR: 3.29, 95%CI: 1.39-8.39, p=0.009), and HER2 status (OR: 0.21, 95%CI: 0.08-0.50, p=0.001), were independent predictors of NSLN metastases. The clinical model based on the above four factors resulted in the area under the curve (AUC) of 0.82(95%CI: 0.76-0.88) in the training set and 0.83(95% CI: 0.74-0.92) in the validation set. The results of post-NAC AUS and AT were added to the clinical model to construct a clinical imaging model for the prediction of NSLN metastasis with AUC of 0.87(95%CI: 0.81-0.93) in the training set and 0.89(95%CI: 0.82-0.96) in the validation set.

CONCLUSIONS

The study incorporated the results of post-NAC AT and AUS with other clinal factors to develop a model to predict NSLN metastasis in patients with initial cN+ before surgery. This model performed excellently, allowing physicians to select patients for whom unnecessary ALND could be avoided after NAC.