Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework

Abstract

Abstract: Background and purpose: Machine learning (ML) is applied for outcome prediction and treatment support. This study aims to develop different ML models to predict risk of axillary lymph node metastasis (LNM) in breast invasive micropapillary carcinoma (IMPC) and to explore the risk factors of LNM. Methods: From the Surveillance, Epidemiology, and End Results (SEER) database and the records of our hospital, a total of 1547 patients diagnosed with breast IMPC were incorporated in this study. The ML model is built and the external validation is carried out. SHapley Additive exPlanations (SHAP) framework was applied to explain the optimal model; multivariable analysis was performed with logistic regression (LR); and nomograms were constructed according to the results of LR analysis. Results: Age and tumor size were correlated with LNM in both cohorts. The luminal subtype is the most common in patients, with the tumor size