Meretoja, T J3; Heikkilä, P S3; Mansfield, A S3; Cserni, G3; Ambrozay, E3; Boross, G3; Zgajnar, J3; Perhavec, A3; Gazic, B3; Arisio, R3; Tvedskov, T F1; Jensen, Maj-Britt Raaby2; Leidenius, M H K3
1 Klinik for Plastikkirurgi, Brystkirurgi og Brandsårsbehandling, HovedOrtoCentret Rigshospitalet, Rigshospitalet, The Capital Region of Denmark2 Onkologisk Klinik, Finsencentret, Rigshospitalet, The Capital Region of Denmark3 unknown
BACKGROUND: Sentinel node biopsy (SNB) is the "gold standard" in axillary staging in clinically node-negative breast cancer patients. However, axillary treatment is undergoing a paradigm shift and studies are being conducted on whether SNB may be omitted in low-risk patients. The purpose of this study was to evaluate the risk factors for axillary metastases in breast cancer patients with negative preoperative axillary ultrasound. METHODS: A total of 1,395 consecutive patients with invasive breast cancer and SNB formed the original patient series. A univariate analysis was conducted to assess risk factors for axillary metastases. Binary logistic regression analysis was conducted to form a predictive model based on the risk factors. The predictive model was first validated internally in a patient series of 566 further patients and then externally in a patient series of 2,463 patients from four other centers. All statistical tests were two-sided. RESULTS: A total of 426 of the 1,395 (30.5 %) patients in the original patient series had axillary lymph node metastases. Histological size (P < 0.001), multifocality (P < 0.001), lymphovascular invasion (P < 0.001), and palpability of the primary tumor (P < 0.001) were included in the predictive model. Internal validation of the model produced an area under the receiver operating characteristics curve (AUC) of 0.731 and external validation an AUC of 0.79. CONCLUSIONS: We present a predictive model to assess the patient-specific probability of axillary lymph node metastases in patients with clinically node-negative breast cancer. The model performs well in internal and external validation. The model needs to be validated in each center before application to clinical use.
Annals of Surgical Oncology, 2014, Vol 21, Issue 7, p. 2229-36