P3-07-36: Predicting Sentinel Lymph Node Metastasis of a Chinese Breast Cancer Population: Assessment of an Existing Nomogram and a New Predictive Nomogram.
Authors: J-Y Chen, J-J Chen, B-L Yang, Z-B Liu, X-Y Huang, G-Y Liu, Q-X Han, W-T Yang, Z-Z Shen, Z-M Shao, J Wu
Journal: Cancer Research (2011)

Abstract Background: The Memorial Sloan-Kettering Cancer Center (MSKCC) developed a nomogram to predict the presence of sentinel lymph node (SLN) metastasis in breast cancer patients. In our study, The MSKCC nomogram performance for prediction of SLN metastases was assessed in Chinese breast cancer population. A new model (Shanghai Cancer Center Nomogram, SCC nomogram ) was developed with clinically relevant variables and possible advantages. Methods: Data were collected from 771 patients with successful SLN biopsy who were treated during March 2005 to June 2010. Touch imprint cytology (TIC) and serial section with H&amp;E staining were performed routinely on each sentinel node. 580 SLN biopsy procedures from March 2005to November 2009were used as training group to validate the MSKCC nomogram and assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The predictive accuracy of MSKCC nomogram was assessed by calculating the area under the receiver-operating characteristic (ROC) curve (AUC). The SCC nomogram was created from the logistic regression model. The new model was subsequently applied to 191 sequential SLN biopsies from January 2010 to June 2010 as the validation group. Results: It was shown that age, tumor size, tumor type, histological grade, lymphovascular invasion and neural invasion was correlated with the probability of SLN metastasis by univariate analysis (P&amp;lt;0.05). By multivariate analysis, tumor size, histological grade and lymphovascular invasion were identified as independent predictors of SLN metastasis. The SCC nomogram was then developed with four variables associated with SLN metastasis: age, tumor size, histological grade and lymphovascular invasion. The new model was accurate and discriminating, with AUC of 0.773 when applied to the validation group, as compared to the MSKCC nomogram with AUC of 0.754 in the modeling group. The trend of actual probability in various decile groups was comparable to the predicted probability. For predicted probability cut-off points of 7% and 15%, the false-negative rates of SCC nomogram were 0% and 8.1%. Conclusion: As far as we know, this is the first study designed to evaluate the MSKCC nomogram and develop a new nomogram in Chinese early breast cancer population. Compared to the MSKCC nomogram, the SCC nomogram was developed with similar AUC but less variables and lower false-negative rates for low-probability subgroups. It could provide a more acceptable clinical accessory in the preoperative discussion with patients, especially in the very low risk of patients. For those patients, the SCC nomogram could be used to safely avoid a SLN procedure, thereby reducing postoperative morbidity, whereas the rate could be as high as 7% in the literature. Although the SCC nomogram that predicts metastasis of breast cancer in the sentinel lymph node performed well in Chinese breast cancer population, it is imperfect. The SCC nomogram was developed and validated in the single instite. The SCC model should be validated in different patient groups before it is demonstrated to be reproducible and would be applied widely. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-07-36.