Abstract PO2-07-03: Retrospective Validation Study of an Artificial Neural Network-Based Preoperative Decision-Support Tool for Noninvasive Lymph Node Staging (NILS) in Women with Primary Breast Cancer (ISRCTN14341750)

Abstract

Abstract Background Surgical sentinel lymph node biopsy (SLNB) is routinely used to reliably stage axillary lymph nodes in early breast cancer (BC). However, SLNB may be associated with postoperative arm morbidities. For most patients with BC undergoing SLNB, the findings are benign, and the procedure is currently questioned. A decision-support tool for the prediction of benign sentinel lymph nodes based on preoperatively available data has been developed using artificial neural network modelling [1,2]. Methods This was a retrospective geographical and temporal validation study of the noninvasive lymph node staging (NILS) model, based on preoperatively available data from 586 women consecutively diagnosed with primary BC at two sites. Ten preoperative clinicopathological characteristics from each patient were entered into the web-based calculator (Table 1), and the probability of benign lymph nodes was predicted. Vascular invasion, the tenth feature of the NILS model, was difficult to determine preoperatively. Therefore, a separate ANN model was developed to impute this feature, using the other nine features of the NILS model as predictors. A user-friendly web implementation of the NILS model was tested in this study. The performance of the NILS model was assessed in terms of discrimination with the area under the receiver operating characteristic curve (AUC). The primary endpoint was axillary nodal status (discrimination, benign [N0] vs. metastatic axillary nodal status [N+]) determined by the NILS model compared to nodal status by definitive pathology. Results The mean age of the women in the cohort was 65 years, and most of them (93%) had luminal cancers (Table 2). Approximately three-fourths of the patients had no metastases in SLNB (N0 74%). The AUC for the predicted probabilities for the whole cohort was 0.6741 (95% confidence interval: 0.6255–0.7227). More than one in four patients (n=151, 26%) were identified as candidates for SLNB omission when applying the predefined cut-off for lymph node negativity from the development cohort (Table 3). Conclusion The performance of the NILS model was satisfactory. In approximately every fourth patient, SLNB could potentially be omitted. Considering the shift from postoperatively to preoperatively available predictors in this validation study, we have demonstrated the robustness of the NILS model. The clinical usability of the web interface will be evaluated before its clinical implementation. Trial registration Registered in the ISRCTN registry with study ID ISRCTN14341750.