Validation of Breast Cancer Survival Prediction Model with SEER Database

Abstract

Objective: The accurate estimation of outcome in postoperative breast cancer patients is an essential component of the individualized treatment, decision-making, and patient counseling processes. The disease outcome and prognosis of breast cancer patients may vary according to geographic and ethnic factors. To clarify this topic, we created a new prognostic and predictive model for breast cancer patients, based on clinical and pathological variables. Study design and setting: Clinical and pathological data were collected from 1587 patients with breast cancer who underwent surgical intervention. A survival prediction model was used to allow the analysis of the optimal combination of variables. The area under the receiver operating characteristic (ROC) curve, as applied to an independent validation data set, was used as the measure of accuracy. Results were assessed by comparing the area under the ROC curve with the SEER database. Results: Our predictive model of survival predicted disease outcome for individual patients with breast cancer. The comparison between our predictive model and SEER databases showed that our model underestimated outcome in the SEER cohort and that the SEER model overestimated outcome in our breast cancer patients. Conclusion: Our model may present an alternative as personalized prognostic tool for breast cancer patients. Decision regarding the survival prediction should take every consideration about regional and racial factors into account.