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

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.