Abstract P3-03-09: A nomogram predicting lymph node metastasis in T1 breast cancer based on the surveillance, epidemiology, and end results program
Authors: Y-X Zhao, Y-R Liu, Y-Z Jiang, S Xie, Z-M Shao
Journal: Cancer Research (2019)

Abstract Background: In developed countries, T1 stage breast cancers have become the most frequently diagnosed invasive breast diseases. Patients with early stage breast cancer with lymph nodes metastasis have been proven to have more aggressive biologically phenotypes. The risk of missing metastases using sentinel lymph node biopsy can range from 1% to 4%, with a false negative rate of 10%. This evidence implies that SLNB might not be sufficient for the diagnosis of lymph node metastasis in T1 breast cancer patients. The aim of this study was to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer. Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and age, race, and tumour size, primary site, pathological grade, histologic type, and molecular subtype. A nomogram was developed in the training set and validated by a calibration curve with the bootstrapping method and receptor operating characteristic curve analysis. Result: A total of 91,364 T1 breast cancer patients were included in the present study. For patients with T1 breast cancer, age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor status, progesterone receptor status and human epidermal growth factor receptor 2 status were independent predictive factors of positive lymph node metastasis (P&amp;lt;0.001). Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. Based on multivariate logistic regression analysis, we successfully developed a nomogram to predict lymph node metastasis by summing the scores of each variables. The nomogram was further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 (95% CI: 0.722-0.744) and 0.741 (95% CI: 0.731-0.752) in the training and validation sets, respectively. We determined the cut-off value of total points to predict lymph node metastasis according to Youden's index in the training set. Both the training set and validation set were divided into two groups: the low score group (total points≤182) and the high score group (total points&amp;gt;182). We found a significant difference in the probability of lymph node metastasis between the high and low score groups in univariate analysis in both the training set (OR=4.15, 95% CI:3.77-4.57, P&amp;lt;0.001) and the validation set (OR=4.53, 95% CI 4.10-5.00, P&amp;lt;0.001). Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients could be vital for the assessment of their metastatic lymph node status. The nomogram developed here, if further validated in other large T1 patient cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis. Citation Format: Zhao Y-X, Liu Y-R, Jiang Y-Z, Xie S, Shao Z-M. A nomogram predicting lymph node metastasis in T1 breast cancer based on the surveillance, epidemiology, and end results program [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-03-09.