Bevilacqua José Luiz B, Kattan Michael W, Fey Jane V, Cody Hiram S, Borgen Patrick I, Van Zee Kimberly J
Department of Surgery, Memorial Sloan-Kettering Cancer Center, MRI 1026, New York, NY 10021, USA.
J Clin Oncol. 2007 Aug 20;25(24):3670-9. doi: 10.1200/JCO.2006.08.8013. Epub 2007 Jul 30.
Lymph node metastasis is a multifactorial event. Several variables have been described as predictors of lymph node metastasis in breast cancer. However, it is difficult to apply these data-usually expressed as odds ratios-to calculate the probability of sentinel lymph node (SLN) metastasis for a specific patient. We developed a user-friendly prediction model (nomogram) based on a large data set to assist in predicting the presence of SLN metastasis.
Clinical and pathologic features of 3,786 sequential SLN biopsy procedures were assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The model was subsequently applied to 1,545 sequential SLN biopsies. A nomogram was created from the logistic regression model. A computerized version of the nomogram was developed and is available on the Memorial Sloan-Kettering Cancer Center (New York, NY) Web site.
Age, tumor size, tumor type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptors were associated with SLN metastasis in multivariate analysis. The nomogram was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.754 when applied to the validation group.
Newly diagnosed breast cancer patients are increasingly interested in information about their disease. This nomogram is a useful tool that helps physicians and patients to accurately predict the likelihood of SLN metastasis.
淋巴结转移是一个多因素事件。已有多个变量被描述为乳腺癌淋巴结转移的预测指标。然而,要应用这些通常以比值比表示的数据来计算特定患者前哨淋巴结(SLN)转移的概率却很困难。我们基于一个大型数据集开发了一个用户友好的预测模型(列线图),以协助预测SLN转移的存在情况。
对3786例连续的SLN活检手术的临床和病理特征进行多变量逻辑回归分析,以预测乳腺癌患者SLN转移的存在情况。随后将该模型应用于1545例连续的SLN活检。根据逻辑回归模型创建了列线图。开发了列线图的计算机版本,可在纪念斯隆凯特琳癌症中心(纽约州纽约市)的网站上获取。
在多变量分析中,年龄、肿瘤大小、肿瘤类型、淋巴管浸润、肿瘤位置、多灶性以及雌激素和孕激素受体与SLN转移相关。该列线图准确且具有鉴别力,应用于验证组时,受试者操作特征曲线下面积为0.754。
新诊断的乳腺癌患者对其疾病信息的兴趣日益增加。这个列线图是一个有用的工具,可帮助医生和患者准确预测SLN转移的可能性。