| 摘要: |
| ［摘要］ 目的 回顾性分析与广西前哨淋巴结（SLN）阳性乳腺癌患者非前哨淋巴结（NSLN）癌转移相关的临床病理因素，建立预测模型量化NSLN的转移风险，使低危的SLN阳性患者避免不必要的腋窝淋巴结清扫（ALND）。方法 共有83例SLN阳性并行补充ALND的乳腺癌患者纳入该研究。对与NSLN转移相关的临床病理因素进行单因素及多因素分析，建立一个多变量的NSLN转移风险预测模型。并将新建模型运用于83例研究对象，计算受试者操作曲线下面积（AUC）评估该模型预测的准确度。连续变量用Mann-Whitney U检验，分类变量用χ2检验或确切概率法。结果 肿瘤大小、肿瘤分级、有无血管淋巴管浸润、SLN癌灶大小等四个影响因素在logistic回归分析中被证实是NSLN转移的独立预测因素，并纳入最终的NSLN转移预测模型。将新建模型运用于83例研究对象，AUC为0.832(95%CI=0.744～0.919)。结论 新建立的预测模型能较好地区分广西乳腺癌SLN阳性患者NSLN有无肿瘤侵犯，但新建模型预测的准确度，仍需要进行前瞻性的中心内部验证及外部验证进行评估。 |
| 关键词: 乳腺癌 非前哨淋巴结 风险预测模型 |
| DOI：10.3969/j.issn.1674-3806.2014.06.02 |
| 分类号:R 737 |
| 基金项目:广西卫生厅科研课题（编号：Z2008147） |
|
| Analysis of variables predicting non-sentinel lymph node involvement in breast cancer patients from Guangxi and development of a new predictive model |
|
LUO Ming，LI Fu，HE Liu-sheng，et al.
|
|
Department of Gastrointestinal Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
|
| Abstract: |
| ［Abstract］ Objective To analyse the clinical pathologic factors predicting non-sentinel node(NSLN) involvement and develop a new model to quantify the risk of NSLN metastasis in sentinel lymph nodes(SLN)-positive breast cancer patients from Guangxi, in order to avoid unnecessary axillary lymph node disection(ALND) in low-risk patients with positive SLN.Methods Eighty-three patients with positive sentinel lymph node biopsy(SLNB) followed by ALND were enrolled into our retrospective study.Univariate and multivariate analysis was used to identify variables predicting non-sentinel node involvement and a multivariable predictive model was developed. In order to assess the accuracy,new model was applied to the original series of 83 patients and the area under the receiver operating characteristic curve(AUC) was calculated.Distribution of continuous variables was analyzed using the Mann-Whitney U test, and the χ2 test was used for categorical variables.Results Size of the primary tumor, histological grade, lymphovascular invasion and size of SLN metastasis were revealed to be independent predictors of NSLN involvement in multivariate logistic regression and included in the final predictive model.For the original series of 83 patients, the AUC was 0.832(95%CI=0.744 to 0.919).Conclusion In our study,new predictive tool was developed to assess the risk of additional axillary metastases after positive SLNB in breast cancer and represent considerable discrimination in our own population. but its accuracy needs to be assessed by prospective validation in both internal center and external centers. |
| Key words: Breast cancer Non-sentinel lymph node Predictive model |
