The nomogram prediction scheme may provide a more accurate and personalized survival estimate in elderly breast cancer patients. This prognostic nomogram directly quantifies patient survival risk based on variant prognostic factors without forming risk groups, a strategy that resulted in more favorable Cand AIC indexes than were achieved using the AJCC TNM classification.11 Cancer-specific prognostic factors are different in elderly patients than in younger patients. Our nomogram can also be used to indicate which factors most influence cancer-specific survival, which might give clinicians important insight when treating these patients. 

Our study has several limitations. First, the data for perioperative chemotherapy were not available for all patients in the SEER database. Additionally, the benefits of chemotherapy in elderly breast cancer patients were controversial. Some studies have suggested that elderly patients who undergo adjuvant therapy have worse outcomes, while others endorse its use.12,13 Second, endocrine therapy was also not available in the database. These two factors are critical prognostic factors in patients with breast cancer. However, the absence of data regarding the use of chemotherapy or endocrine therapy did not significantly influence the choice of surgical approach. In addition, the use of chemotherapy or endocrine therapy in breast cancer is based on the stage of the disease and its molecular subtype. Our study was based on a large sample size of ~17000 patients who were recruited during the past 5 years. This may have reduced potential bias in the analysis. In addition, we used the propensity score method to solve the problem of imbalance in baseline characteristics between different treatment groups. The propensity score represents the probability of assigning a patient to a treatment, and it can be calculated using a fitted model. Propensity matching is nonparametric, and the two-step procedure used in causal effect estimation is considered doubly robust in that if either the propensity score matching or the parametric model is correct, the causal estimates should be consistent.14,15 Furthermore, applying a stratified adjusted survival analysis would make the analysis more accurate when comparing the relationship between potential prognosis factors and the 5-year overall survival rate. Third, the usage of trastuzumab was unclear, and this might have affected the results for HER-2-positive patients. Fourth, as a retrospective study of the SEER database, we have to acknowledge that there were some limitations, but this database included a large population. Furthermore, we used propensity score analysis in order to decrease some bias caused by the missing items. In elderly patients, the cancer-specific survival might be a good factor to predict the effects of the treatment strategy. In the nomogram prediction model, every factor was allocated a score, so we omit the stage information and include the distant metastasis information.

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A localized surgical approach might be a better choice in elderly breast cancer patients. However, radiotherapy was needed to improve cancer-specific survival and overall survival in these patients. In addition, we developed a prognostic nomogram that could be used to directly quantify patient risk based on variant prognostic factors without requiring the formation of risk groups, and this approach was more favorable for estimating cancer-specific survival.


We thank Jianguo Lai for his assistance during data analysis. This research was not supported by any specific grant from a funding agency in the public, commercial, or not-for-profit sectors. version of the manuscript.


The authors report no conflicts of interest in this work.

Zhi Wang,1,2,* Zhangjian Zhou,1,* Wenxing Li,1 Wei Wang,3 Xin Xie,1 Jincheng Liu,2 Yongchun Song,1 Chengxue Dang,1 Hao Zhang1 

1Division of Surgical Oncology, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2Division of Surgery, Shaanxi Tuberculosis Hospital, Changan District, Xi’an, Shaanxi, People’s Republic of China; 3Division of Gynaecology and Obstetrics, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China 

*These authors contributed equally to this work 


1. Shachar SS, Hurria A, Muss HB. Breast cancer in women older than 80 years. J Oncol Pract. 2016;12(2):123–132.

2. Weiss A, Noorbaksh A, Tokin C, Chang D, Blair SL. Hormone receptornegative breast cancer: undertreatment of patients over 80. Ann Surg Oncol. 2013;20(10):3274–3278.

3. Bao J, Yu K-D, Jiang Y-Z, Shao Z-M, Di G-H. The effect of laterality and primary tumor site on cancer-specific mortality in breast cancer: a SEER Population-Based Study. PLoS One. 2014;9(4): e94815–e94818.

4. Yancik R, Wesley MN, Ries LA, Havlik RJ, Edwards BK, Yates JW. Effect of age and comorbidity in postmenopausal breast cancer patients aged 55 years and older. JAMA. 2001;285(7):885–892.

5. Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79(387):516–524.

6. Zhang Z. Propensity score method: a non-parametric technique to reduce model dependence. Ann Transl Med. 2017;5(1):7.

7. Cheung SYA, Evans ND, Chappell MJ, Godfrey KR, Smith PJ, Errington RJ. Exploration of the intercellular heterogeneity of topotecan uptake into human breast cancer cells through compartmental modelling. Math Biosci. 2008;213(2):119–134.

8. Balducci L. Treatment of breast cancer in women older than 80 years is a complex task. J Oncol Pract. 2016;12(2):133–134.

9. Hughes KS, Schnaper LA, Bellon JR, et al. Lumpectomy plus tamoxifen with or without irradiation in women age 70 years or older with early breast cancer: long-term follow-up of CALGB 9343. J Clin Oncol. 2013;31(19):2382–2387.

10. Nolan MT, Russell DJ, Marwick TH. Long-term risk of heart failure and myocardial dysfunction after thoracic radiotherapy: a systematic review. Can J Cardiol. 2016;32(7):908–920.

11. Zhou Z, Zhang H, Xu Z, Li W, Dang C, Song Y. Nomogram predicted survival of patients with adenocarcinoma of esophagogastric junction.World J Surg Oncol. 2015;13(1):197.

12. Janssen-Heijnen ML, Houterman S, Lemmens VE, Louwman MW, Maas HA, Coebergh JW. Prognostic impact of increasing age and comorbidity in cancer patients: a population-based approach. Crit Rev Oncol Hematol. 2005;55(3):231–240.

13. Schonberg MA, Marcantonio ER, Li D, Silliman RA, Ngo L, Mccarthy EP. Breast cancer among the oldest old: tumor characteristics, treatment choices, and survival. J Clin Oncol. 2010;28(12):2038–2045.

14. Ho DE, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal. 2007;15(3):199–236.

15. Keller B, Tipton E. Propensity score analysis in R: a software review. J Educ Behav Stat. 2016;41(3):326–348.

Source: Cancer Management and Research.
Originally published September 4, 2018.