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) trial.
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.
  
  
  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).
  
  
  CONCLUSIONS
  The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as OS.
authors.affiliation: Department of Obstetrics and Gynecology, Johannes Gutenberg University, Mainz, Germany. marcus.schmidt@frauen.klinik.uni-mainz.de
authors.forename: M
authors.initials: M
authors.lastname: Schmidt
authors.forename: A
authors.initials: A
authors.lastname: Victor
authors.forename: D
authors.initials: D
authors.lastname: Bratzel
authors.forename: D
authors.initials: D
authors.lastname: Boehm
authors.forename: C
authors.initials: C
authors.lastname: Cotarelo
authors.forename: A
authors.initials: A
authors.lastname: Lebrecht
authors.forename: W
authors.initials: W
authors.lastname: Siggelkow
authors.forename: J G
authors.initials: JG
authors.lastname: Hengstler
authors.forename: A
authors.initials: A
authors.lastname: Elsässer
authors.forename: M
authors.initials: M
authors.lastname: Gehrmann
authors.forename: H-A
authors.initials: HA
authors.lastname: Lehr
authors.forename: H
authors.initials: H
authors.lastname: Koelbl
authors.forename: G
authors.initials: G
authors.lastname: von Minckwitz
authors.forename: N
authors.initials: N
authors.lastname: Harbeck
authors.forename: C
authors.initials: C
authors.lastname: Thomssen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology* / Q000532:radiotherapy / Q000601:surgery; D018572:Disease-Free Survival; D005260:Female; D005500:Follow-Up Studies; D018734:Genes, erbB-2; D006801:Humans; D007150:Immunohistochemistry; D053208:Kaplan-Meier Estimate; D008137:Longitudinal Studies; D008875:Middle Aged; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D009389:Neovascularization, Pathologic; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D011980:Receptors, Progesterone / Q000032:analysis; D012044:Regression Analysis; D012189:Retrospective Studies; D018570:Risk Assessment; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D011980:Receptors, Progesterone