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Document type:
Comparative Study; Journal Article; Randomized Controlled Trial
Author(s):
Schmidt, M; Victor, A; Bratzel, D; Boehm, D; Cotarelo, C; Lebrecht, A; Siggelkow, W; Hengstler, JG; Elsässer, A; Gehrmann, M; Lehr, HA; Koelbl, H; von Minckwitz, G; Harbeck, N; Thomssen, C
Title:
Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer--comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3
Abstract:
BACKGROUND: Defining risk categories in breast cancer is of considerable clinical significance. We have developed a novel risk classification algorithm and compared its prognostic utility to the Web-based tool Adjuvant! and to the St Gallen risk classification. PATIENTS AND METHODS: After a median follow-up of 10 years, we retrospectively analyzed 410 consecutive node-negative breast cancer patients who had not received adjuvant systemic therapy. High risk was defined by any of the following criteria: (i) age <35 years, (ii) grade 3, (iii) human epithelial growth factor receptor-2 positivity, (iv) vascular invasion, (v) progesterone receptor negativity, (vi) grade 2 tumors >2 cm. All patients were also characterized using Adjuvant! and the St Gallen 2007 risk categories. We analyzed disease-free survival (DFS) and overall survival (OS). RESULTS: The Node-Negative-Breast Cancer-3 (NNBC-3) algorithm enlarged the low-risk group to 37% as compared with Adjuvant! (17%) and St Gallen (18%), respectively. In multivariate analysis, both Adjuvant! [P = 0.027, hazard ratio (HR) 3.81, 96% confidence interval (CI) 1.16-12.47] and the NNBC-3 risk classification (P = 0.049, HR 1.95, 95% CI 1.00-3.81) significantly predicted OS, but only the NNBC-3 algorithm retained its prognostic significance in multivariate analysis for DFS (P < 0.0005). CONCLUSION: The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as OS.
Journal title abbreviation:
Ann Oncol
Year:
2009
Journal volume:
20
Journal issue:
2
Pages contribution:
258-64
Language:
eng
Fulltext / DOI:
doi:10.1093/annonc/mdn590
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/18824499
Print-ISSN:
0923-7534
TUM Institution:
Frauenklinik und Poliklinik
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