BACKGROUND: 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. PURPOSE: Our goal was to investigate the possibility of using prognostic indicators to predict axillary node status of patients with primary breast cancer. METHODS: 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. RESULTS: 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%. CONCLUSION: 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. IMPLICATIONS: 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.
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BACKGROUND: If axillary lymph node status of breast cancerpatients 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. PURPOSE: Our goal was to investigate the possibility of using prognostic indicators to predict axillary node status of patients with primary breast cancer. METHODS: 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. RESULTS: 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%. CONCLUSION: 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. IMPLICATIONS: These predictive models cannot alleviate the necessity of axillary node dissection for staging of breast cancerpatients 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.