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 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. References 1. Dihge L, et al. The implementation of NILS: A web-based artificial neural network decision support tool for noninvasive lymph node staging in breast cancer. Front Oncol. 2023;13:1102254. https://doi.org:10.3389/fonc.2023.1102254. 2. Skarping I, et al. The implementation of a noninvasive lymph node staging (NILS) preoperative prediction model is cost effective in primary breast cancer. Breast Cancer Res Treat. 2022;194:577–86. https://doi.org:10.1007/s10549-022-06636-x. Table 1. The ten preoperatively available included variables in the noninvasive lymph node staging (NILS) model Table 2. Patient and tumor characteristics at baseline *When missing data on mammography, features from ultrasound was entered into the NILS web interface. Abbreviations: CNB: core needle biopsy; HER2: human epidermal receptor 2 Table 3. Performance measures of the noninvasive lymph node staging (NILS) model, including potential sentinel lymph node biopsy (SLNB) reduction rate for the current model. *Equivalent to the maximum FNR of 10% reflecting accepted FNR of the SLNB procedure **The SLNB reduction rate was calculated as follows = (TN + FN)/(TN + FN + TP + FP). Abbreviations: TP: true positive; TN: true negative; FP: false positive; FN: false negative; FNR: false negative rate; SLNB: sentinel lymph node biopsy Citation Format: Ida Skarping, Julia Ellbrant, Looket Dihge, Mattias Ohlsson, Linnea Huss, Pär-Ola Bendahl, Lisa Rydén. 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]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-07-03.