Nomogram Development for Assessing Oncotype DX Recurrence Scores in Breast Cancer: A Chinese Population Study.

BACKGROUND

Breast cancer (BC) is the most prevalent cancer among women worldwide, with increasing incidence rates, particularly in China. Given the high costs of Oncotype DX (ODX) testing, which predicts recurrence scores (RSs) on the basis of gene expression, developing a nomogram utilizing clinicopathological variables may provide an accessible alternative for risk stratification.

METHODS

We conducted a retrospective analysis of 703 estrogen receptor (ER)-positive, HER2-negative T1-3N0M0 BC patients who underwent ODX testing at Qilu Hospital. A nomogram was developed using multivariate logistic regression to predict low and high RSs in the group. Model performance was validated by receiver operating characteristic curve, calibration curve, and decision curve analysis.

RESULTS

Multivariate analysis revealed that older age, lower histologic grade, a higher ER expression level, a higher proportion of cells expressing progesterone receptor, and a lower proportion of cells expressing Ki-67 were significantly associated with a patient being in the low-risk subgroup. A nomogram was then developed using these variables to predict the RS, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] = 0.772-0.850) in the development group and 0.794 (95% CI = 0.737-0.851) in the validation group. Calibration and decision curve analyses further confirmed the nomogram's clinical utility. Moreover, a comparison between the TAILORx-nomogram and our nomogram was conducted, which proved that our nomogram has better predictive accuracy and reliability in Chinese BC patients.

CONCLUSION

We present the first nomogram for predicting the RS in Chinese patients with BC on the basis of clinicopathological factors. This model could aid in identifying patients who may not need ODX testing and serve as a cost-effective alternative for those unable to access ODX, thereby optimizing treatment decisions and enhancing patient management in resource-limited settings.