Ravdin P M, De Laurentiis M, Vendely T, Clark G M
Division of Oncology, University of Texas Health Science Center at San Antonio 78284-7884.
J Natl Cancer Inst. 1994 Dec 7;86(23):1771-5. doi: 10.1093/jnci/86.23.1771.
If axillary lymph node status of breast cancer patients could be accurately predicted from basic clinical information and from characteristics of their primary tumors, then many patients could be spared axillary lymph node dissection. Tumor size alone does not allow the identification of groups with very low or high risk of being axillary node positive.
Our goal was to investigate the possibility of using prognostic indicators to predict axillary node status of patients with primary breast cancer.
Data from 26,683 patients from the National Breast Cancer Tissue Resource were used in this study. Patients in this dataset were randomly assigned to a training set (patient information used to construct predictive models) or a validation set (patient information used to prospectively evaluate predictive models). The records of a total of 11,964 case patients that had complete prognostic factors and pathologic data were analyzed: 5963 patients in the training set and 6001 patients in the validation set. All of the patients studied had tumors 5 cm or less in size and at least 15 axillary lymph nodes that had been examined. Data used for construction of the predictive models were available for all patients and included tumor size, number of nodes positive, patient age, quantitative estrogen receptor levels, quantitative progesterone receptor (PgR) levels, DNA flow cytometry-derived ploidy, and S-phase fraction. Logistic regression models were used to predict nodal status.
Multivariate predictive models were produced that used tumor size, patient age, S phase, and PgR as independent predictors. These models allowed identification of patient risks of being node positive ranging from 6%-79% and as having 10 or more positive nodes ranging from less than 1% to slightly more than 30%.
Addition of prognostic indicator information to tumor size can refine estimates of whether a patient is likely to be node positive. However, no patient subsets could be identified as having greater than 95% chance of being node negative or node positive.
These predictive models cannot alleviate the necessity of axillary node dissection for staging of breast cancer patients in situations in which nodal status would affect therapeutic decisions. Subsets of patients could be identified who had a less than 5% chance of having 10 or more positive nodes. Thus, some patients could be spared axillary dissection if it was being performed solely to identify patients with this high-risk feature.
如果能根据乳腺癌患者的基本临床信息及其原发肿瘤的特征准确预测腋窝淋巴结状态，那么许多患者可以避免进行腋窝淋巴结清扫术。仅肿瘤大小并不能识别腋窝淋巴结转移风险极低或极高的患者群体。
我们的目标是研究使用预后指标预测原发性乳腺癌患者腋窝淋巴结状态的可能性。
本研究使用了来自国家乳腺癌组织资源库的26683例患者的数据。该数据集中的患者被随机分配到训练集（用于构建预测模型的患者信息）或验证集（用于前瞻性评估预测模型的患者信息）。对总共11964例具有完整预后因素和病理数据的病例患者记录进行了分析：训练集5963例患者，验证集6001例患者。所有研究患者的肿瘤大小均为5厘米或更小，且至少检查了15个腋窝淋巴结。用于构建预测模型的数据适用于所有患者，包括肿瘤大小、阳性淋巴结数量、患者年龄、雌激素受体定量水平、孕激素受体（PgR）定量水平、DNA流式细胞术检测的倍体以及S期分数。采用逻辑回归模型预测淋巴结状态。
构建了多变量预测模型，该模型将肿瘤大小、患者年龄、S期和PgR作为独立预测因子。这些模型能够识别出淋巴结转移阳性风险为6% - 79%的患者，以及有10个或更多阳性淋巴结的风险从小于1%到略高于30%的患者。
在肿瘤大小基础上增加预后指标信息可以更精确地估计患者是否可能出现淋巴结转移阳性。然而，无法识别出淋巴结转移阴性或阳性概率大于95%的患者亚组。
在淋巴结状态会影响治疗决策的情况下，这些预测模型无法消除对乳腺癌患者进行腋窝淋巴结清扫以进行分期的必要性。可以识别出有10个或更多阳性淋巴结概率小于5%的患者亚组。因此，如果仅为了识别具有这种高风险特征的患者而进行腋窝清扫，那么一些患者可以避免该手术。
