Chen Jia-ying, Chen Jia-jian, Xue Jing-yan, Chen Ying, Liu Guang-yu, Han Qi-xia, Yang Wen-tao, Shen Zhen-zhou, Shao Zhi-min, Wu Jiong
Department of Breast Surgery, Fudan University Shanghai Cancer Center, 270 Dong An Road, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
World J Surg. 2015 Dec;39(12):2919-27. doi: 10.1007/s00268-015-3189-z.
We have developed a new nomogram to predict the probability of a patient with 1-2 metastatic sentinel lymph nodes (SLNs) to present further axillary disease.
Data were collected from 480 patients who were diagnosed with 1-2 positive lymph nodes and thus underwent axillary lymph node dissection between March 2005 and June 2011. Clinical and pathological features of the patients were assessed with multivariable logistic regression. The Shanghai Cancer Center Non-SLN nomogram (SCC-NSLN) was created from the logistic regression model. This new model was subsequently applied to 481 patients from July 2011 to December 2013. The predictive accuracy of the SCC-NSLN nomogram was measured by calculating the area under the receiver operating characteristic curve (AUC).
Based on the results of the univariate analysis, the variables that were significantly associated with the incidence of non-SLN metastasis in an SLN-positive patient included lymphovascular invasion, neural invasion, the number of positive SLNs, the number of negative SLNs, and the size of SLN metastasis (P < 0.05). Using multivariate analysis, lymphovascular invasion, the number of positive SLNs, the number of negative SLNs, and the size of SLN metastasis were identified as independent predictors of non-SLN metastasis. The SCC-NSLN nomogram was then developed using these four variables. The new model was accurate and discriminating on both the modeling and validation groups (AUC: 0.7788 vs 0.7953). The false-negative rates of the SCC-NSLN nomogram were 3.54 and 9.29 % for the predicted probability cut-off points of 10 and 15 % when applied to patients who have 1-2 positive SLNs.
The SCC-NSLN nomogram could serve as an acceptable clinical tool in clinical discussions with patients. The omission of ALND might be possible if the probability of non-SLN involvement is <10 and <15 % in accordance with the acceptable risk determined by medical staff and patients.
我们开发了一种新的列线图,用于预测有1 - 2枚转移性前哨淋巴结(SLN)的患者出现进一步腋窝疾病的概率。
收集了2005年3月至2011年6月期间480例被诊断为有1 - 2枚阳性淋巴结并因此接受腋窝淋巴结清扫术的患者的数据。采用多变量逻辑回归评估患者的临床和病理特征。从逻辑回归模型创建了上海癌症中心非前哨淋巴结列线图(SCC - NSLN)。随后将这个新模型应用于2011年7月至2013年12月的481例患者。通过计算受试者操作特征曲线(AUC)下的面积来衡量SCC - NSLN列线图的预测准确性。
基于单变量分析结果,与SLN阳性患者中非前哨淋巴结转移发生率显著相关的变量包括淋巴管侵犯、神经侵犯、阳性SLN的数量、阴性SLN的数量以及SLN转移灶的大小(P < 0.05)。通过多变量分析,淋巴管侵犯、阳性SLN的数量、阴性SLN的数量以及SLN转移灶的大小被确定为非前哨淋巴结转移的独立预测因素。然后使用这四个变量开发了SCC - NSLN列线图。新模型在建模组和验证组中均准确且具有鉴别力(AUC:0.7788对0.7953)。当应用于有1 - 2枚阳性SLN的患者时,SCC - NSLN列线图在预测概率截断点为10%和15%时的假阴性率分别为3.54%和9.29%。
SCC - NSLN列线图可作为与患者进行临床讨论时可接受的临床工具。如果根据医护人员和患者确定的可接受风险,非前哨淋巴结受累的概率<10%和<15%,则有可能省略腋窝淋巴结清扫术(ALND)。