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

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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.

Original languageEnglish
Pages (from-to)2216-2224
Number of pages9
JournalEuropean Journal of Cancer
Issue number12
Publication statusPublished - 2010



  • Breast cancer
  • Pathological complete response
  • Predictive factors
  • Predictive model
  • Primary therapy

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

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