Comparison and modification of survival predicting system for breast cancer patients with bone metastases

Abstract

Background: Breast cancer is the most common malignancy in the female. Survival for patients with breast cancer has improved substantially over the past two decades, accompanied by increased patients with skeletal-related events. Since surgery is most commonly needed for complete or pending pathological fractures, an accurate preoperative survival estimation for patients with symptomatic bone metastases is crucial in surgical decision making. Several prognostic models for survival estimation in metastatic cancer patients have been developed in western centers without external validation in Asian patient populations and breast cancer-specific cohorts. Methods: Seven survival prediction models were externally validated by a cohort of metastatic breast cancer patients from an Asian center. The prediction ability and accuracy were valued using receiver operating characteristic analysis and Brier score at different time points. Univariate and multivariate Cox regression was used to identify independent prognostic factors. A multivariable prediction model was further established and validated. Results: In our metastatic breast cancer patients cohort, the PathFx model demonstrated superior accuracy at predicting 3- and 6-month survival while the SSG model showed the highest accuracy at 12-month. None of these models exhibit accurate predictions beyond 12-month. Cox regression further identified five independent prognostic factors. A prognostic scoring system with breast cancer-specific prognostic factors was established. Internal validation showed consistent discrimination and accuracy. Conclusions: Current prognostic models showed inconsistent and limited accuracy in Asian metastatic breast cancer patients, especially for more prolonged estimated survival. A disease-based predicting model with cancer-specific prognostic factors would increase the prediction accuracy and help with surgical decision making.