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.