A nomogram based on the expression of Ki-67, steroid hormone receptors status and number of chemotherapy courses to predict pathological complete remission after preoperative chemotherapy for breast cancer.

Background: Tools able to predict pathological complete response (pCR) to preoperative chemotherapy might improve treatment outcome.

Patients And Methods: Data from 783 patients with invasive ductal carcinoma treated with preoperative chemotherapy and operated at the European Institute of Oncology were used to develop a nomogram using logistic regression model based on both categorical (clinical T and N, HER2/neu, grade and primary therapy) and continuous variables (age, oestrogen receptor (ER), progesterone receptor (PgR), Ki-67 expression and number of chemotherapy courses). The performance of the resulting nomogram was internally evaluated through bootstrapping methods. Finally the model was externally validated on a patient set treated in other institutions and subsequently operated at the EIO.

Results: At multivariable analysis the probability of pCR was directly associated with Ki-67 expression (OR for 10% increase in the percentage of positive cells, 1.15, 95% confidence interval (CI), 1.03, 1.29) and number of chemotherapy courses (OR for one cycle increase, 1.31, 95% CI, 1.12, 1.53) and inversely associated with ER and PgR expression (ORs for 10% increase in the percentage of positive cells, 0.86, 95% CI 0.79, 0.93 and 0.82, 95% CI 0.69, 0.99, respectively). The nomogram for pCR based on these variables had good discrimination in training as well in validation set (AUC, 0.78 and 0.77).

Conclusion: The use of a nomogram based on the number of preoperative courses, degree of Ki-67 and steroid hormone receptors expression may be useful for predicting the probability of pCR and for the design of the proper therapeutic algorithm in locally advanced breast cancer.