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Annals of Oncology Advance Access originally published online on February 13, 2009
Annals of Oncology 2009 20(7):1178-1184; doi:10.1093/annonc/mdn747
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© The Author 2009. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

breast cancer

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

M. Colleoni1,*, V. Bagnardi2,3,4, N. Rotmensz2, S. Dellapasqua1, G. Viale5, G. Pruneri5, P. Veronesi6, R. Torrisi1, A. Luini6, M. Intra6, V. Galimberti6, E. Montagna1 and A. Goldhirsch1

1 Department of Medicine
2 Division of Epidemiology and Biostatistics, European Institute of Oncology
3 Department of Statistics, University of Milan-Bicocca
4 Frontier Science and Technology Research Foundation, Southern Europe
5 Division of Pathology, European Institute of Oncology and University of Milan School of Medicine
6 Division of Senology, European Institute of Oncology, Milan, Italy

* Correspondence to: Dr M. Colleoni, Department of Medicine, Division of Medical Oncology, European Institute of Oncology, Via Ripamonti 435, 20141 Milan, Italy. Tel: +39-02-57489439; Fax: +39-02-574829212; E-mail: marco.colleoni{at}ieo.it

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.

Key words: breast cancer, disease-free survival, nomogram, predictive factors, preoperative chemotherapy, primary therapy

Received for publication October 29, 2008. Accepted for publication November 17, 2008.


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