A Dynamic Bayesian Model for Breast Cancer Survival Prediction.

Abstract

Predicting breast cancer survival and targeting patients at high-risk of mortality is of crucial importance. We built a Bayesian Dynamic Cox (BDCox) model for predicting 5-year overall survival in breast cancer patients using data of the SEER Cancer Registry with 12,840 women. Four feature selection methods were used to identify predictors and enhance parsimony: fast backward variable selection, elastic net, Bayesian Model Average (BMA), and clinical expertise. All resulting models and a baseline full model containing all features were internally validated via bootstrapping and externally validated in the Shanghai Breast Cancer Survival Study. BMA outperformed other feature selection methods in both internal and external validations. The BDCox model with 12 predictors had the best performance. Several predictors showed time-varying associations with survival that are in agreement with previous studies. The model developed using BDCox outperformed other prognostic models considered in our study. The internal validation results indicate that the BDCox model is capable of achieving high prediction accuracy (C-statistic: 0.802), and the external validation results showed excellent generalizability of the BDCox model (C-statistic: 0.739). We built a dynamic Bayesian model from the large population-based registry SEER for predicting 5-year breast cancer overall survival. The prediction performance of the BDCox model is significantly better than other survival models.