A C Regierer1, R Wolters2, M-P Ufen3, A Weigel4, I Novopashenny2, C H Köhne3, H Samonigg5, J Eucker4, K Possinger4, M B Wischnewsky2. 1. Department of Oncology and Hematology, Charité - Universitätsmedizin Berlin, Berlin. Electronic address: anne.regierer@charite.de. 2. Department of Mathematics and Computer Science, University Bremen, Bremen. 3. Department of Oncology and Hematology, Klinikum Oldenburg, Oldenburg, Germany. 4. Department of Oncology and Hematology, Charité - Universitätsmedizin Berlin, Berlin. 5. Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.
Abstract
BACKGROUND: The prognosis of metastatic breast cancer (MBC) is extremely heterogeneous. Although patients with MBC will uniformly die to their disease, survival may range from a few months to several years. This underscores the importance of defining prognostic factors to develop risk-adopted treatment strategies. Our aim has been to use simple measures to judge a patient's prognosis when metastatic disease is diagnosed. PATIENTS AND METHODS: We retrospectively analyzed 2269 patients from four clinical cancer registries. The prognostic score was calculated from the regression coefficients found in the Cox regression analysis. Based on the score, patients were classified into high-, intermediate-, and low-risk groups. Bootstrapping and time-dependent receiver operating characteristic curves were used for internal validation. Two independent datasets were used for external validation. RESULTS: Metastatic-free interval, localization of metastases, and hormone receptor status were identified as significant prognostic factors in the multivariate analysis. The three prognostic groups showed highly significant differences regarding overall survival from the time of metastasis [intermediate compared with low risk: hazard ratio (HR) 1.76, 95% confidence interval (CI) 1.36-2.27, P < 0.001; high compared with low risk: HR 3.54, 95% CI 2.81-4.45, P < 0.001). The median overall survival in these three groups were 61, 38, and 22 months, respectively. The external validation showed congruent results. CONCLUSIONS: We developed a prognostic score, based on routine parameters easily accessible in daily clinical care. Although major progress has been made, the optimal therapeutic management of the individual patient is still unknown. Besides elaborative molecular classification of tumors, simple clinical measures such as our model may be helpful to further individualize optimal breast cancer care.
BACKGROUND: The prognosis of metastatic breast cancer (MBC) is extremely heterogeneous. Although patients with MBC will uniformly die to their disease, survival may range from a few months to several years. This underscores the importance of defining prognostic factors to develop risk-adopted treatment strategies. Our aim has been to use simple measures to judge a patient's prognosis when metastatic disease is diagnosed. PATIENTS AND METHODS: We retrospectively analyzed 2269 patients from four clinical cancer registries. The prognostic score was calculated from the regression coefficients found in the Cox regression analysis. Based on the score, patients were classified into high-, intermediate-, and low-risk groups. Bootstrapping and time-dependent receiver operating characteristic curves were used for internal validation. Two independent datasets were used for external validation. RESULTS: Metastatic-free interval, localization of metastases, and hormone receptor status were identified as significant prognostic factors in the multivariate analysis. The three prognostic groups showed highly significant differences regarding overall survival from the time of metastasis [intermediate compared with low risk: hazard ratio (HR) 1.76, 95% confidence interval (CI) 1.36-2.27, P < 0.001; high compared with low risk: HR 3.54, 95% CI 2.81-4.45, P < 0.001). The median overall survival in these three groups were 61, 38, and 22 months, respectively. The external validation showed congruent results. CONCLUSIONS: We developed a prognostic score, based on routine parameters easily accessible in daily clinical care. Although major progress has been made, the optimal therapeutic management of the individual patient is still unknown. Besides elaborative molecular classification of tumors, simple clinical measures such as our model may be helpful to further individualize optimal breast cancer care.
Entities:
Keywords:
metastatic breast cancer; prognostic factors; survival
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