[Establishment of a preoperative prediction model for axillary lymph node burden in patients with early breast...

Objective: To explore the method of predicting high lymph node load in patients with early breast cancer to avoid unnecessary sentinel lymph node biopsy. Methods: The clinicopathological and thoracic multi-slice spiral CT (MSCT) data of 2620 patients with early (cT1~2N0M0) breast cancer treated in the Affiliated Cancer Hospital of Zhengzhou University from January 1, 2014 to August 1, 2018 were collected. According to the postoperative pathological results, the patients were divided into the group with axillaryhigh lymph node burden (HNB) and the non-HNB group. The influencing factors of axillary lymph node burden in patients with early breast cancer were determined by univariate and multivariate analysis, and the diagnostic model of MSCT to HNB was established. The best cutoff value for the diagnosis of HNB was determined through analyzing the receiver operative characteristic (ROC) curve, and the consistency between MSCT diagnosis and pathological diagnosis was evaluated by Kappa test. Results: Among the 2 620 patients, 168 were diagnosed of HNB. Univariate analysis showed that the tumor size, the status of human epidermal growth factor receptor 2 (HER-2), the number of abnormal lymph nodes showed in MSCT, the ratio of the length to the diameter of the maximum abnormal lymph node as shown in MSCT, the condition of the maximum abnormal lymph node door, and the parenchyma of the maximum abnormal lymph node were related to axillary lymph node burden in patients with early breast cancer (P<0.05). Multivariate analysis showed that the number of abnormal lymph nodes showed in MSCT was an independent influencing factor of axillary HNB in patients with early breast cancer. Compared with patients without abnormal lymph nodes, the OR values of patients with 1, 2, 3 or more abnormal lymph nodes displayed by MSCT and in axillary HNB status were 3.305, 9.379, 126.163 and 780.953, respectively. Using 3 or more abnormal lymph nodes detected by MSCT to predict the area under the ROC curve of axillary HNB in patients with early breast cancer, the area was 0.928, the sensitivity was 82.1%, the specificity was 95.4%, and the accuracy was 94.5%. Kappa test showed that the consistency between MSCT diagnosis and pathological diagnosis was relatively high (Kappa=0.629, P<0.001). Conclusions: The number of abnormal lymph nodes showed in MSCT is an independent influencing factor of axillary HNB in patients with early breast cancer. Taking 3 or more abnormal lymph nodes showed in MSCT as the threshold can help to predict the axillary HNB status of early breast cancer patients and exempt some of them from unnecessary sentinel lymph node biopsy.