Development of a prediction model for breast cancer based on the national cancer registry in Taiwan

BackgroundThis study aimed to develop a prognostic model to predict the breast cancer-specific survival and overall survival for breast cancer patients in Asia and to demonstrate a significant difference in clinical outcomes between Asian and non-Asian patients.MethodsWe developed our prognostic models by applying a multivariate Cox proportional hazards model to Taiwan Cancer Registry (TCR) data. A data-splitting strategy was used for internal validation, and a multivariable fractional polynomial approach was adopted for prognostic continuous variables. Subjects who were Asian, black, or white in the US-based Surveillance, Epidemiology, and End Results (SEER) database were analyzed for external validation. Model discrimination and calibration were evaluated in both internal and external datasets.ResultsIn the internal validation, both training data and testing data calibrated well and generated good area under the ROC curves (AUC; 0.865 in training data and 0.846 in testing data). In the external validation, although the AUC values were larger than 0.85 in all populations, a lack of model calibration in non-Asian groups revealed that racial differences had a significant impact on the prediction of breast cancer mortality. For the calibration of breast cancer-specific mortality, P values < 0.001 at 1 year and 0.018 at 4 years in whites, and P values ≤ 0.001 at 1 and 2 years and 0.032 at 3 years in blacks, indicated that there were significant differences (P value < 0.05) between the predicted mortality and the observed mortality. Our model generally underestimated the mortality of the black population. In the white population, our model underestimated mortality at 1 year and overestimated it at 4 years. And in the Asian population, all P values > 0.05, indicating predicted mortality and actual mortality at 1 to 4 years were consistent.ConclusionsWe developed and validated a pioneering prognostic model that especially benefits breast cancer patients in Asia. This study can serve as an important reference for breast cancer prediction in the future.