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.

Colleoni, M., Bagnardi, V., Rotmensz, N., Dellapasqua, S., Viale, G., Pruneri, G., et al. (2009). A risk score to predict disease-free survival in patients not achieving a pathological complete remission after preoperative chemotherapy for breast cancer. ANNALS OF ONCOLOGY, 20(7), 1178-1184 [10.1093/annonc/mdn747].

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

BAGNARDI, VINCENZO;
2009

Abstract

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.
Articolo in rivista - Articolo scientifico
Breast cancer; Disease-free survival; Nomogram; Predictive factors; Preoperative chemotherapy; Primary therapy;
English
2009
20
7
1178
1184
none
Colleoni, M., Bagnardi, V., Rotmensz, N., Dellapasqua, S., Viale, G., Pruneri, G., et al. (2009). A risk score to predict disease-free survival in patients not achieving a pathological complete remission after preoperative chemotherapy for breast cancer. ANNALS OF ONCOLOGY, 20(7), 1178-1184 [10.1093/annonc/mdn747].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/12873
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