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Nomogram for Predicting Breast Cancer-Specific Mortality of Elderly Women with Breast Cancer

  • Lu, Xunxi1, 2
  • Li, Xiaoguang1
  • Ling, Hong1, 2
  • Gong, Yue1, 2
  • Guo, Linwei1, 2
  • He, Min1, 2
  • Sun, Hefen1, 2
  • Hu, Xin1, 2
  • 1 Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, P.R. China
  • 2 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, P.R. China
Published Article
Medical Science Monitor
"International Scientific Information, Inc."
Publication Date
Sep 13, 2020
DOI: 10.12659/MSM.925210
PMID: 32920589
PMCID: PMC7510685
PubMed Central


Background The objectives of this study were to evaluate the cumulative incidence of breast cancer-specific death (BCSD) and other cause-specific death in elderly patients with breast cancer (BC) and to develop an individualized nomogram for estimating BCSD. Material/Methods Data were retrieved from the Surveillance, Epidemiology, and End Results program. A total of 25 241 patients older than 65 years with stage I–III BC diagnosed between 2004 and 2008 was included in the study cohort. We used the cumulative incidence function (CIF) to describe the cause-specific mortality and Gray’s test to compare the differences in CIF among the groups. Fine and Gray’s proportional subdistribution hazard model was applied to validate the independent prognostic factors, upon which the competing-risks nomogram and web-based calculator was built. The performance of the nomogram was assessed with the C-indexes and calibration plot diagrams. Results After data screening, 25 241 cases were included for statistical analysis. In the training cohort, the 5-, 8-, and 10-year cumulative incidence of BCSD was 5.7, 8.1, and 9.1%, respectively. Ten independent prognostic factors associated with BCSD were identified. The C-index of the nomogram was 0.818 (0.804–0.831) in the training cohort and 0.808 (0.783–0.833) in the validation cohort. Calibration plot diagrams showed near-ideal consistency between the predicted probabilities and actual observations. Conclusions We built a reliable dynamic nomogram for predicting BCSD in elderly patients, and this individualized predictive tool is favorable for risk classification and complex personalized treatment decision making in clinical practice.

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