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J Clin Oncol. 2005 Nov 20;23(33):8331-9.

Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.

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  • 1Department of Breast Medical Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77230-1439, USA.



To combine clinical variables associated with pathologic complete response (pCR) and distant metastasis-free survival (DMFS) after preoperative chemotherapy (PC) into a prediction nomogram.


Data from 496 patients treated with anthracycline PC at the Institut Gustave Roussy were used to develop and calibrate a nomogram for pCR based on multivariate logistic regression. This nomogram was tested on two independent cohorts of patients treated at the M.D. Anderson Cancer Center. The first cohort (n = 337) received anthracycline; the second cohort (n = 237) received a combination of paclitaxel and anthracycline PC. A separate nomogram to predict DMFS was developed using Cox proportional hazards regression model.


The pCR nomogram based on clinical stage, estrogen receptor status, histologic grade, and number of preoperative chemotherapy cycles had good discrimination and calibration in the training and the anthracycline-treated validation sets (concordance indices, 0.77, 0.79). In the paclitaxel plus anthracycline group, when the predicted pCR rate was less than 14%, the observed rate was 7.5%; for a predicted rate of > or = 38%, the actual rate was 85%. For a predicted rate between 14% to 38%, the observed rates were 50% with weekly and 27% with 3-weekly paclitaxel. This indicates that patients with intermediate chemotherapy sensitivity benefit the most from the optimized schedule of paclitaxel. Patients unlikely to achieve pCR to anthracylines remain at low probability for pCR, even after inclusion of paclitaxel. The nomogram for DMFS had a concordance index of 0.72 in the validation set and outperformed other prediction tools (P = .02).


Our nomograms predict pCR accurately and can serve as a basis to integrate future molecular markers into a clinical prediction model.

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