Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran

  • Behnaz ALAFCHI Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
  • Leili TAPAK 1. Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 2. Modeling of Noncommunicable Diseases Research Center, School of Health, Hamadan University of Medical Sciences, Hamadan, Iran
  • Omid HAMIDI Department of Science, Hamedan University of Technology, Hamedan, Iran
  • Jalal POOROLAJAL 1. Research Center for Health Sciences, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 2.Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
  • Hossein MAHJUB 1. Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran 2. Department of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
Keywords: Breast neoplasms; Survival analysis; Landmarking; Dynamic prediction; Cohort studies

Abstract

Abstract

Background: Breast cancer is the first non-cutaneous malignancy in women and the second cause of death due to cancer all over the world. There are situations where researchers are interested in dynamic prediction of survival of patients where traditional models might fail to achieve this goal. We aimed to use a dynamic prediction model in analyzing survival of breast cancer patients.

Methods:  We used a data set originates from a retrospective cohort (registry-based) study conducted in 2014 in Tehran, Iran, information of 550 patients were available analyzed. A method of landmarking was utilized for dynamic prediction of survival of the patients. The criteria of time-dependent area under the curve and prediction error curve were used to evaluate the performance of the model.

Results: An index of risk score (prognostic index) was calculated according to the available covariates based on Cox proportional hazards. Therefore, hazard of dying for a high-risk patient with breast cancer within the next five years was 2.69 to 3.04 times of that for a low-risk patient. The value of the dynamic C-index was 0.89 using prognostic index as covariate.

Conclusion: Generally, the landmark model showed promising performance in predicting survival or probability of dying for breast cancer patients in this study in a predefined window. Therefore, this model can be used in other studies as a useful model for investigating the survival of breast cancer patients.

 

References

1. Raimondi S, Botteri E, Munzone E et al (2016). Use of beta‐blockers, angiotensin‐converting enzyme inhibitors and angiotensin receptor blockers and breast cancer survival: Systematic review and meta‐analysis. Int J Cancer, 139:212-219.
2. Poorolajal J, Nafissi N, Akbari M et al (2016). Breast Cancer Survival Analysis Based on Immunohistochemistry Subtypes (ER/PR/HER2): a Retrospective Cohort Study. Arch Iran Med, 19:680-686.
3. Assi HA, Khoury KE, Dbouk H et al (2013). Epidemiology and prognosis of breast cancer in young women. J Thorac Dis, 5:S2-S8.
4. Brenner DR, Brockton NT, Kotsopoulos J et al (2016). Breast cancer survival among young women: a review of the role of modifiable lifestyle factors. Cancer Causes Control, 27:459-472.
5. Anders CK, Johnson R, Litton J et al (2009). Breast cancer before age 40 years. Semin Oncol, 36(3):237-49.
6. Ferlay J, Shin HR, Bray F et al (2010). Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer, 127:2893-2917.
7. DeSantis C, Siegel R, Bandi P, Jemal A (2011). Breast cancer statistics, 2011. CA Cancer J Clin, 61:409-418.
8. Cowper DC, Kubal JD, Maynard C, Hynes DM (2002). A primer and comparative review of major US mortality databases. Ann Epidemiol, 12:462-468.
9. Lamont EB, Herndon JE, Weeks JC et al (2006). Measuring disease-free survival and cancer relapse using Medicare claims from CALGB breast cancer trial participants (companion to 9344). J Natl Cancer Inst, 98:1335-1338.
10. Clahsen PC, Van de ,Velde C et al (1996). Improved local control and disease-free survival after perioperative chemotherapy for early-stage breast cancer. A European Organization for Research and Treatment of Cancer Breast Cancer Cooperative Group Study. J Clin Oncol, 14:745-753.
11. Petersen LF, Moravek M, Woodruff TK, Jeruss JS (2018). Oncofertility Options for Young Women With Breast Cancer. In: The Breast (Fifth Edition). Ed(s): Elsevier, pp. 773-777. e3.
12. Alexieva‐Figusch J, Van Putten W, Blankenstein M, Klijn J (1988). The prognostic value and relationships of patient characteristics, estrogen and progestin receptors, and site of relapse in primary breast cancer. Cancer, 61:758-768.
13. Fisch T, Pury P, Probst N et al (2005). Variation in survival after diagnosis of breast cancer in Switzerland. Ann Oncol, 16:1882-1888.
14. Niwińska A, Murawska M, Pogoda K (2010). Breast cancer brain metastases: differences in survival depending on biological subtype, RPA RTOG prognostic class and systemic treatment after whole-brain radiotherapy (WBRT). Ann Oncol, 21:942-948.
15. Diniz RW, Guerra MR, Cintra JRD et al (2016). Disease-free survival in patients with non-metastatic breast cancer. Rev Assoc Med Bras (1992), 62:407-13.
16. Van Houwelingen HC (2007). Dynamic prediction by landmarking in event history analysis. Scand J Statis, 34:70-85.
17. Van Houwelingen HC, Putter H (2008). Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal, 14:447-63.
18. Van Houwelingen H, Putter H (2011). Dynamic prediction in clinical survival analysis. ed. CRC Press.
19. Nicolaie M, Van Houwelingen J, De Witte T et al. (2013). Dynamic prediction by landmarking in competing risks. Stat Med, 32:2031-47.
20. Anderson JR, Cain KC, Gelber RD (1983). Analysis of survival by tumor response. J Clin Oncol, 1:710-719.
21. Kleinbaum DG, Klein M (2010). Survival analysis. ed. Springer.
22. Horita K, Yamaguchi A, Hirose K et al (2001). Prognostic factors affecting disease-free survival rate following surgical resection of primary breast cancer. Eur J Histochem, 45:73-84.
23. Wolberg WH, Street WN, Mangasarian OL (1999). Importance of nuclear morphology in breast cancer prognosis. Clinical Cancer Research, 5:3542-3548.
24. Dawood S, Broglio K, Esteva F et al (2008). Defining prognosis for women with breast cancer and CNS metastases by HER2 status. Ann Oncol, 19:1242-1248.
25. Heitz F, Rochon J, Harter P et al (2010). Cerebral metastases in metastatic breast cancer: disease-specific risk factors and survival. Ann Oncol, 22:1571-1581.
26. Koizumi M, Yoshimoto M, Kasumi F, Iwase T (2010). An open cohort study of bone metastasis incidence following surgery in breast cancer patients. BMC cancer, 10:381.
27. Gijsen M, King P, Perera T et al (2010). HER2 phosphorylation is maintained by a PKB negative feedback loop in response to anti-HER2 herceptin in breast cancer. PLoS Biol, 8:e1000563.
28. Shak S (1999). Overview of the trastuzumab (Herceptin) anti-HER2 monoclonal antibody clinical program in HER2-overexpressing metastatic breast cancer. Herceptin Multinational Investigator Study Group. Semin Oncol, 71-77.
29. Mousavi SM, Mohagheghi MA, Mousavi-Jerrahi A et al (2008). Outcome of breast cancer in Iran: a study of Tehran Cancer Registry data. Asian Pac J cancer prev, 9:275-8.
30. Sadjadi A, Hislop TG, Bajdik C et al (2009). Comparison of breast cancer survival in two populations: Ardabil, Iran and British Columbia, Canada. BMC cancer, 9:381.
Published
2019-12-02
How to Cite
1.
ALAFCHI B, TAPAK L, HAMIDI O, POOROLAJAL J, MAHJUB H. Landmark Prediction of Survival for Breast Cancer Patients: A Case Study in Tehran, Iran. Iran J Public Health. 48(12):2249-2259.
Section
Original Article(s)