OBJECTIVES: To develop a mapping algorithm for a conversion of the EORTC QLQ-C30 and EORTC QLQ BR-23 into the EQ-5D derived utilities in metastatic breast cancer (MBC) patients. METHODS: We enrolled 199 patients with MBC from the four leading Korean hospitals in 2009. EQ-5D utility, cancer-specific (QLQ-C30) and breast cancer-specific quality of life data (QLQ-BR23), and selected clinical and demographic information were collected from the study participants. Ordinary least squares regression models were used to model the EQ-5D using QLQ-C30 and QLQ-BR23 scale scores. To select the best model specification, six different sets of explanatory variables were compared. RESULTS: Regression analysis with the multi-item scale scores of QLQ-C30 was the best-performing model, explaining for 48.7% of the observed EQ-5D variation. Its mean absolute error between the observed and predicted EQ-5D utilities (0.092) and relative prediction error (2.784%) was among the smallest. Also, this mapping model showed the least systematic errors according to disease severity. CONCLUSIONS: The mapping algorithms developed have good predictive validity and therefore they enable researchers to translate cancer-specific health-related quality-of-life measures to the preference-adjusted health status of MBC patients.