Development and validation of novel machine learning-based prognostic models and propensity score matching for comparison of surgical approaches in mucinous breast cancer.

Abstract

Mucinous breast cancer (MBC) is a rare subtype of breast cancer with specific clinicopathologic and molecular features. Despite MBC patients generally having a favorable survival prognosis, there is a notable absence of clinically accurate predictive models. Patients diagnosed with MBC from the SEER database spanning 2010 to 2020 were included for analysis. Cox regression analysis was conducted to identify independent prognostic factors. Ten machine learning algorithms were utilized to develop prognostic models, which were further validated using MBC patients from two Chinese hospitals. Cox analysis and propensity score matching were applied to evaluate survival differences between MBC patients undergoing mastectomy and breast-conserving surgery (BCS). We determined that the XGBoost models were the optimal models for predicting overall survival (OS) and breast cancer-specific survival (BCSS) in MBC patients with the most accurate performance (AUC=0.833-0.948). Moreover, the XGBoost models still demonstrated robust performance in the external test set (AUC=0.856-0.911). Patients treated with BCS exhibited superior OS compared to those undergoing mastectomy (p < 0.001, HR: 0.60, 95% CI: 0.47-0.77). However, no significant difference was observed in the risk of breast cancer-related mortality. We have successfully developed 6 optimal prognostic models utilizing the XGBoost algorithm to accurately predict the survival of MBC patients. We also developed an interactive web application to facilitate the utilization of our models by clinicians or researchers. Notably, we observed a significant improvement in OS for patients undergoing BCS.