A risk score to predict disease-free survival in patients not achieving a pathological complete remission after preoperative chemotherapy for breast cancer

Ann Oncol. 2009 Jul;20(7):1178-84. doi: 10.1093/annonc/mdn747. Epub 2009 Feb 13.


Background: We aimed to predict disease-free survival (DFS) in patients who failed to achieve a pathologic complete remission (pCR) after preoperative chemotherapy (PC).

Patients and methods: Data from 577 patients treated with PC and operated at the European Institute of Oncology (EIO) were used to develop a nomogram using Cox proportional hazards regression model based on both categorical (pT, positive nodes, human epidermal growth factor receptor 2 (HER2) status, vascular invasion) and continuous histological variables (estrogen receptors and Ki-67 expression) at surgery. The nomogram was tested on a second patient cohort (343 patients) treated in other institutions and subsequently operated at the EIO.

Results: The nomogram for DFS based on both categorical and continuous variables had good discrimination in the training and the validation sets (concordance indices 0.73, 0.67).

Conclusion: The use of a nomogram based on the degree of selected histopathological variables can predict DFS and might help in the adjuvant therapeutic algorithm design.

Publication types

  • Validation Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use*
  • Breast Neoplasms / drug therapy*
  • Breast Neoplasms / pathology
  • Cohort Studies
  • Disease-Free Survival*
  • Female
  • Humans
  • Immunohistochemistry
  • Kaplan-Meier Estimate
  • Middle Aged
  • Nomograms*
  • Predictive Value of Tests
  • Preoperative Care
  • Proportional Hazards Models
  • Remission Induction
  • Risk
  • Treatment Outcome
  • Young Adult