Abstract P2-01-12: Development of prediction model for omission of sentinel lymph node biopsy in T1 breast cancer
Authors: JN Cho, EJ Song, MH Lee, S-Y Jung, S Lee, H-S Kang, SH Sim, IH Park, KS Lee, YJ Kim, S-K Kim, Y Kwon, B-H Nam, ES Lee
Journal: Cancer Research (2017)

Abstract INTRODUCTION Axillary sentinel lymph node (SLN) biopsy is a standard method for axillary nodal staging in the treatment of breast cancer. However, along with the trends to SLN performed only without additional axillary lymph node dissection, it's time to be considered omission of SLN for selective patients. We developed a prediction model to assess the negative probability of sentinel lymph node metastasis, specifically focus on the patients with clinical T1 breast cancer. METHODS and MATERIALS The study group consisted of 513 consecutive patients with clinical T1 breast cancer, who had undergone primary surgery between 2007 and 2012. The clinicopathologic factors and imaging modalities including breast ultrasound (US), magnetic resonance imaging (MRI), chest computed tomography (CT), and positron emission tomography (PET) were evaluated. Patients who fulfilled our inclusion criteria were randomized into experimental and validation set by 3:1 ratio. In the experimental group (n = 256), multivariate logistic regression analysis was used to analyze the association of each variable with the likelihood of SLN metastases. A prediction model was developed based on the patients in the experimental group and was validated with internal patient cohorts. RESULTS Of the 513 patients, 119 (23.1%) were found to have SLN metastases. In univariate analysis, presence of lymphovascular invasion (P &amp;lt; 0.001) and suspicious finding of preoperative image studies (US, PET, and MRI, P &amp;lt; 0.001) were independent positive predictors of SLN metastases. In multivariate analysis of experimental group, estrogen receptor status (P = 0.012), presence of lymphatic invasion (P &amp;lt; 0.001), and suspicious finding of preoperative image studies (US, PET, and MRI, P &amp;lt; 0.001) were each associated with involvement of SLN. A prediction model based on this analysis consists of 9 rows including 6 variables (age, estrogen receptor status, presence of lymphatic invasion, and results of preoperative US, PET or CT, MRI). The sum of assigned points for all six variables made corresponding value of negative probability of SLN metastasis. The accuracy of prediction model applied to the validation group, as measured by the area under the receiver operating curve was 0.789. CONCLUSIONS The prediction model developed here may be a useful tool to assess SLN involvement for clinical T1 breast cancer patients. And prospective study for additional validation of the prediction model is currently in preparation, exploring the possibility of SLN biopsy omission. Citation Format: Cho JN, Song EJ, Lee MH, Jung S-Y, Lee S, Kang H-S, Sim SH, Park IH, Lee KS, Kim YJ, Kim S-K, Kwon Y, Nam B-H, Lee ES. Development of prediction model for omission of sentinel lymph node biopsy in T1 breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P2-01-12.