PURPOSE: 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. PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.
PURPOSE: 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. PATIENTS AND METHODS: 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. RESULTS: 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. CONCLUSION: Newly diagnosed breast cancerpatients 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.
Authors: Kiran K Turaga; Alec Chau; Jennifer M Eatrides; John V Kiluk; Nazanin Khakpour; Christine Laronga; M Catherine Lee Journal: Oncologist Date: 2011-05-14
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