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Published on: May 18, 2020
Prognostic model of ER-positive, HER2-negative breast cancer predicted by clinically relevant indicators
Xinming Song1, Pintian Wang1, Ruiling Feng1
1Department of Oncology, The First Affiliated Hospital of Shantou University Medical College, No. 57, Changping Road, Jinping District, Shantou, 515041, Guangdong, China.
Purpose:
To study the clinicopathological variables connected with disease-free survival (DFS) as well as overall survival (OS) in patients who are ER-positive or HER2-negative and to propose nomograms for predicting individual risk.
Methods:
In this investigation, we examined 585 (development cohort) and 291 (external validation) ER-positive, HER2-negative breast cancer patients from January 2010 to January 2014. From January 2010 to December 2014, we retrospectively reviewed and analyzed 291 (external validation) and 585 (development cohort) HER2-negative, ER-positive breast cancer patients. Cox regression analysis, both multivariate and univariate, confirmed the independence indicators for OS and DFS.
Results:
Using cox regression analysis, both multivariate and univariate, the following variables were combined to predict the DFS of development cohort: pathological stage (HR = 1.391; 95% CI = 1.043-1.855; P value = 0.025), luminal parting (HR = 1.836; 95% CI = 1.142-2.952; P value = .012), and clinical stage (HR = 1.879; 95% CI = 1.102-3.203; P value = 0.021). Endocrine therapy (HR = 3.655; 95% CI = 1.084-12.324; P value = 0.037) and clinical stage (HR = 6.792; 95% CI = 1.672-28.345; P value = 0.009) were chosen as predictors of OS. Furthermore, we generated RS-OS and RS-DFS. According to the findings of Kaplan-Meier curves, patients who are classified as having a low risk have considerably longer DFS and OS durations than patients who are classified as having a high risk.
Conclusion:
To generate nomograms that predicted DFS and OS, independent predictors of DFS in ER-positive/HER2-negative breast cancer patients were chosen. The nomograms successfully stratified patients into prognostic categories and worked well in both internal validation and external validation.
