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.
A risk score to predict disease-free survival in patients not achieving a pathological complete remission after preoperative chemotherapy for breast cancer / M. Colleoni, V. Bagnardi, N. Rotmensz, S. Dellapasqua, G. Viale, G. Pruneri, P. Veronesi, R. Torrisi, A. Luini, M. Intra, V. Galimberti, E. Montagna, A. Goldhirsch. - In: ANNALS OF ONCOLOGY. - ISSN 0923-7534. - 20:7(2009), pp. 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
G. Viale;G. Pruneri;P. Veronesi;
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.
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
