TY - JOUR
T1 - A Dutch Prediction Tool to Assess the Risk of Additional Axillary Non-Sentinel Lymph Node Involvement in Sentinel Node-Positive Breast Cancer Patients
AU - van den Hoven, I
AU - van Klaveren, David
AU - Voogd, AC
AU - Vergouwe, Yvonne
AU - Tjan-Heijnen, V
AU - Roumeni, RMH
PY - 2016
Y1 - 2016
N2 - The performance of previously developed models to predict non sentinel lymph node status for patients with sentinel lymph node-positive breast cancer was poor for our Dutch patient population. Therefore, a new model was developed that includes only 2 predictors, is easy to use, and can help clinicians and their patients in the clinical decision-making regarding axillary treatment. Background: Multiple predictive systems have previously been developed to identify the sentinel lymph node (SLN)-positive patients at low risk of additional axillary non-SLN involvement and for whom completion axillary lymph node dissection (ALND) could be avoided. However, previous studies showed that these tools had poor performance in Dutch patients with breast cancer, probably owing to variations in pathology settings and differences in population characteristics. The aim of the present study was to develop a predictive tool for the risk of non-SLN involvement in a Dutch population with SLN-positive breast cancer. Materials and Methods: The data from 513 patients with SLN-positive breast cancer at 10 participating hospitals, who had undergone ALND from January 2007 to December 2008 were studied. The uni- and multivariable associations of predictors for non-SLN metastases were analyzed, and a predictive model was developed. The discriminatory ability of the model was measured by the area under the receiver operating characteristic curve (AUC) and the agreement between predicted probabilities and observed frequencies was visualized by a calibration plot. Results: A predictive model was developed that included the 2 strongest predictors: the size of the SLN metastases in millimeters and the presence of a negative sentinel lymph node. The model showed good discriminative ability (AUC, 0.75) and good calibration over the complete range of predicted probabilities. Conclusion: We have developed a tool to predict additional non-SLN metastases in Dutch patients with SLN-positive breast cancer that is easy to use in daily clinical breast cancer practice. (C) 2016 Elsevier Inc. All rights reserved.
AB - The performance of previously developed models to predict non sentinel lymph node status for patients with sentinel lymph node-positive breast cancer was poor for our Dutch patient population. Therefore, a new model was developed that includes only 2 predictors, is easy to use, and can help clinicians and their patients in the clinical decision-making regarding axillary treatment. Background: Multiple predictive systems have previously been developed to identify the sentinel lymph node (SLN)-positive patients at low risk of additional axillary non-SLN involvement and for whom completion axillary lymph node dissection (ALND) could be avoided. However, previous studies showed that these tools had poor performance in Dutch patients with breast cancer, probably owing to variations in pathology settings and differences in population characteristics. The aim of the present study was to develop a predictive tool for the risk of non-SLN involvement in a Dutch population with SLN-positive breast cancer. Materials and Methods: The data from 513 patients with SLN-positive breast cancer at 10 participating hospitals, who had undergone ALND from January 2007 to December 2008 were studied. The uni- and multivariable associations of predictors for non-SLN metastases were analyzed, and a predictive model was developed. The discriminatory ability of the model was measured by the area under the receiver operating characteristic curve (AUC) and the agreement between predicted probabilities and observed frequencies was visualized by a calibration plot. Results: A predictive model was developed that included the 2 strongest predictors: the size of the SLN metastases in millimeters and the presence of a negative sentinel lymph node. The model showed good discriminative ability (AUC, 0.75) and good calibration over the complete range of predicted probabilities. Conclusion: We have developed a tool to predict additional non-SLN metastases in Dutch patients with SLN-positive breast cancer that is easy to use in daily clinical breast cancer practice. (C) 2016 Elsevier Inc. All rights reserved.
U2 - 10.1016/j.clbc.2015.09.003
DO - 10.1016/j.clbc.2015.09.003
M3 - Article
SN - 1526-8209
VL - 16
SP - 123
EP - 130
JO - Clinical Breast Cancer
JF - Clinical Breast Cancer
IS - 2
ER -
