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.
目的： 探讨术前预测早期乳腺癌患者高淋巴结负荷的方法，以避免不必要的前哨淋巴结活检术。 方法： 收集2014年1月1日至2018年8月1日就诊于郑州大学附属肿瘤医院的2 620例早期乳腺癌患者的临床病理和胸部多层螺旋CT(MSCT)资料。根据术后病理结果将患者分为腋窝高淋巴结负荷(HNB)组和非HNB组。通过单因素和多因素分析确定早期乳腺癌患者腋窝淋巴结负荷的影响因素，建立MSCT对HNB的诊断模型，采用受试者工作特性(ROC)曲线分析确定诊断HNB的最佳界值，采用Kappa检验评价MSCT诊断与病理诊断的一致性。 结果： 2 620例患者中，HNB患者168例。单因素分析显示，肿瘤大小、人表皮生长因子受体2(HER-2)状态、MSCT异常淋巴结数目、MSCT最大异常淋巴结长短径之比、最大异常淋巴结门情况及最大异常淋巴结实质与早期乳腺癌患者腋窝淋巴结负荷有关(均P<0.05)。多因素分析显示，MSCT异常淋巴结数目是早期乳腺癌患者腋窝HNB的独立影响因素，相对于无异常淋巴结的患者，MSCT诊断有1枚、2枚、3枚、≥4枚异常淋巴结患者为腋窝HNB状态的OR值分别为3.305、9.379、126.163和780.953。以MSCT异常淋巴结≥3枚预测早期乳腺癌患者腋窝HNB的受试者工作(ROC)曲线下面积为0.928，灵敏度为82.1%，特异度为95.4%，准确度为94.5%。Kappa检验显示，MSCT诊断与病理诊断的一致性较好(Kappa＝0.629，P<0.001)。 结论： MSCT异常淋巴结数目是早期乳腺癌患者腋窝HNB的独立影响因素，以MSCT异常淋巴结≥3枚为标准可以帮助临床预测早期乳腺癌患者的腋窝HNB状态，使部分患者免除不必要的前哨淋巴结活检术。.
Keywords: Axillary lymph node dissection; Breast neoplasms; High lymph node burden; Sentinel lymph node.