Van Zee Kimberly J, Manasseh Donna-Marie E, Bevilacqua Jose L B, Boolbol Susan K, Fey Jane V, Tan Lee K, Borgen Patrick I, Cody Hiram S, Kattan Michael W
Departments of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.
Ann Surg Oncol. 2003 Dec;10(10):1140-51. doi: 10.1245/aso.2003.03.015.
The standard of care for breast cancer patients with sentinel lymph node (SLN) metastases includes complete axillary lymph node dissection (ALND). However, many question the need for complete ALND in every patient with detectable SLN metastases, particularly those perceived to have a low risk of non-SLN metastases. Accurate estimates of the likelihood of additional disease in the axilla could assist greatly in decision-making regarding further treatment.
Pathological features of the primary tumor and SLN metastases of 702 patients who underwent complete ALND were assessed with multivariable logistic regression to predict the presence of additional disease in the non-SLNs of these patients. A nomogram was created using pathological size, tumor type and nuclear grade, lymphovascular invasion, multifocality, and estrogen-receptor status of the primary tumor; method of detection of SLN metastases; number of positive SLNs; and number of negative SLNs. The model was subsequently applied prospectively to 373 patients.
The nomogram for the retrospective population was accurate and discriminating, with an area under the receiver operating characteristic (ROC) curve of 0.76. When applied to the prospective group, the model accurately predicted likelihood of non-SLN disease (ROC, 0.77).
We have developed a user-friendly nomogram that uses information commonly available to the surgeon to easily and accurately calculate the likelihood of having additional, non-SLN metastases for an individual patient.
对于前哨淋巴结（SLN）转移的乳腺癌患者，标准治疗方案包括完整腋窝淋巴结清扫术（ALND）。然而，许多人质疑是否有必要对每例可检测到SLN转移的患者都进行完整的ALND，尤其是那些被认为非SLN转移风险较低的患者。准确估计腋窝出现额外病灶的可能性将极大地有助于进一步治疗的决策。
对702例行完整ALND的患者的原发肿瘤和SLN转移的病理特征进行多变量逻辑回归分析，以预测这些患者非SLN中是否存在额外病灶。根据原发肿瘤的病理大小、肿瘤类型和核分级、淋巴管浸润、多灶性以及雌激素受体状态；SLN转移的检测方法；阳性SLN的数量；以及阴性SLN的数量创建了一个列线图。随后将该模型前瞻性地应用于373例患者。
回顾性研究人群的列线图准确且具有鉴别力，受试者操作特征（ROC）曲线下面积为0.76。当应用于前瞻性组时，该模型准确预测了非SLN疾病的可能性（ROC，0.77）。
我们开发了一种用户友好的列线图，它使用外科医生通常可获得的信息，轻松准确地计算个体患者出现额外非SLN转移的可能性。
