Purpose: To compare PREDICT and CancerMath, two widely used prognostic models for invasive breast cancer, taking into account their clinical utility. Furthermore, it is unclear whether these models could be improved. Experimental Design: A dataset of 5,729 women was used for model development. A Bayesian variable selection algorithm was implemented to stochastically search for important interaction terms among the predictors. The derived models were then compared in three independent datasets ( n = 5,534). We examined calibration, discrimination, and performed decision curve analysis. Results: CancerMath demonstrated worse calibration performance compared with PREDICT in estrogen receptor (ER)–positive and ER-negative tumors. The decline in discrimination performance was –4.27% (–6.39 to –2.03) and –3.21% (–5.9 to –0.48) for ER-positive and ER-negative tumors, respectively. Our new models matched the performance of PREDICT in terms of calibration and discrimination, but offered no improvement. Decision curve analysis showed predictions for all models were clinically useful for treatment decisions made at risk thresholds between 5% and 55% for ER-positive tumors and at thresholds of 15% to 60% for ER-negative tumors. Within these threshold ranges, CancerMath provided the lowest clinical utility among all the models. Conclusions: Survival probabilities from PREDICT offer both improved accuracy and discrimination over CancerMath. Using PREDICT to make treatment decisions offers greater clinical utility than CancerMath over a range of risk thresholds. Our new models performed as well as PREDICT, but no better, suggesting that, in this setting, including further interaction terms offers no predictive benefit. Clin Cancer Res; 24(9); 2110–5. ©2018 AACR .