Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors.

Abstract

BACKGROUND:

Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice.

OBJECTIVE:

The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables.

METHODS:

Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value.

RESULTS:

The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646.

CONCLUSION:

We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

• Authors: Jian Zhang, Linhai Xiao, Shengyu Pu, Yang Liu, Jianjun He, Ke Wang
• Journal: Annals of surgical oncology (Ann Surg Oncol) Vol. 28 Issue 5 Pg. 2632-2645 (May 2021) ISSN: 1534-4681 [Electronic] United States
• PMID: 33095360 (Publication Type: Journal Article)

Chemical References

• Receptors, Estrogen

Topics

• Breast Neoplasms (drug therapy)
• Humans
• Logistic Models
• Neoadjuvant Therapy
• Nomograms
• Receptors, Estrogen