Abstract P5-01-08: Steps toward noninvasive lymph node staging (NILS) in clinically node negative patients: Artificial neural network model to preoperatively predict lymphovascular invasion

Abstract

Background: Lymphovascular invasion (LVI) is one of the most important predictors for nodal status in breast cancer patients [1]. Multiple models have been published for prediction of preoperatively disease-free axillary using i.a. LVI [1-2]. However, LVI detection in preoperative core needle biopsy has been reported with a failure rate of 30% [3] and the analysis is not routinely performed in Sweden. Thus, a preoperative model of LVI status would be useful in prediction models for noninvasive lymph node staging (NILS). The purpose of this study was to develop an artificial neural network (ANN) model for LVI prediction using only clinicopathological variables that are routinely available in the preoperative setting. Methods: Data gathered prospectively during 2009-2012 in Lund, Sweden from 761 clinically node negative breast cancer patients were retrospectively extracted. Inclusion criteria were female sex, primary breast cancer and that each patient was scheduled for primary surgery. Patients with metastatic disease, bilateral cancer, tumor size greater than 50 mm, previous ipsilateral breast or axillary surgery, patients omitted of standard axillary staging procedure by SLNB or ALND, and those who had neoadjuvant treatment were excluded. LVI was assessed on surgical breast specimens and was defined as the presence of tumor cells within endothelium-lined vascular channels. Out of the 761 patients in the cohort, 613 patients were documented with LVI status. The LVI full case dataset was split 80/20 for training and validation. The remaining 148 patients were set aside for model testing. Since the test dataset did not contain information on LVI status, it was used to compare the predicted fraction of LVI positive patients to that of the development dataset. Only variables possible to obtain in the preoperative setting were included in the prediction models, comprising age, menopausal status, mode of detection (mammography screening or symptomatic representation), tumor size, multifocality (yes/no), histopathological type, histological grade, ki-67 percentage, estrogen receptor status, progesterone receptor status, and human epidermal growth factor receptor 2 status. An ensemble approach was used, where each ensemble constituted 30 ANNs that were trained and validated using 5-fold cross validation. For every ensemble model, different model parameters, such as L2-regularization and the number of hidden nodes, were tested. Model selection was based on validation AUC. Results: The study cohort included female clinically node negative breast cancer patients scheduled for primary surgery. Data from 613 patients (lymph node stages N0: 67.4%, N1: 26.9%, N2+: 5.7%) were used to develop the model, and 148 patients (N0: 56.8%, N1: 35.8%, N2+: 7.4%) constituted the internal test cohort. Fifteen percentage of the patients in the development dataset were LVI positive. The selected ensemble model achieved a validation AUC of 0.80 (CI 0.75-0.85). This model predicted an LVI positive rate of 16.2% in the test dataset. Conclusion: LVI was predicted with high accuracy using an ANN model based on routine preoperative clinicopathological variables. The result of validation AUC 0.80 (CI 0.75-0.85) indicates a potential for preoperative prediction of LVI, and the model can putatively be useful when applying preoperative nodal prediction models in patients without known LVI status. To confirm these results, verification in an external dataset is needed. Validation of the LVI-model in an independent dataset from the National Breast Cancer Registry will be performed, as well as an evaluation of the usefulness of the LVI-model as an imputation in a nodal prediction model. [1] Dihge, L. et al. BMC Cancer (2019). PMID: 31226956 [2] Bevilacqua, J. L. et al. J Clin Oncol. (2007). PMID: 17664461 [3] Harris, G. C. et al. Am J Surg Pathol. (2003). PMID: 12502923 Citation Format: Malin Hjärtström, Looket Dihge, Pär-Ola Bendahl, Mattias Ohlsson, Lisa Rydén. Steps toward noninvasive lymph node staging (NILS) in clinically node negative patients: Artificial neural network model to preoperatively predict lymphovascular invasion [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-01-08.