Volume 30, Issue 2 (Avicenna Journal of Clinical Medicine-Summer 2023)
Avicenna J Clin Med 2023, 30(2): 81-89 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Ranjbar N, Roshanaei G, Keshtpour Amlashi Z, Poorolajal J, Tapak L. Investigating Factors Affecting the Survival of Breast Cancer Patients Using the Mixture Cure Model. Avicenna J Clin Med 2023; 30 (2) :81-89
URL: http://sjh.umsha.ac.ir/article-1-2719-en.html
URL: http://sjh.umsha.ac.ir/article-1-2719-en.html
Neda Ranjbar1 
, Ghodratollah Roshanaei2 , Zahra Keshtpour Amlashi3 , Jalal Poorolajal4 , Leili Tapak 5
, Ghodratollah Roshanaei2 , Zahra Keshtpour Amlashi3 , Jalal Poorolajal4 , Leili Tapak 5
1- Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
2- Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3- Cancer Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
4- Department of Epidemiology, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
5- Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran ,l.tapak@umsha.ac.ir
2- Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
3- Cancer Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
4- Department of Epidemiology, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
5- Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran ,
Abstract: (975 Views)
Background and Objective: Breast cancer is the second leading cause of death in women. Prognostic factors for cancer using appropriate statistical models with data structure not only help in choosing a more appropriate treatment method in the short term but can also play a significant role in identifying high-risk patients in the long term. This study aimed to determine the factors associated with survival in breast cancer patients using a Mixture Cure model.
Materials and Methods: This retrospective cohort study was conducted from 2011 to 2021. A total of 767 breast cancer patients were referred to the Mahdieh Medical Diagnostic Center in Hamadan, Iran. Clinical and demographic information was collected by contacting the individuals through telephone calls. A Mixture Cure model was used to analyze the collected data. Data analysis was performed using smcure in R.4.0.5 software.
Results: Of the 767 breast cancer patients followed up for 10 years, 58(6.7%) individuals died. The mean (standard deviation) age at diagnosis was 49.23(10.85) years. In univariate analysis, clinical and demographic features, including age, first treatment, marital status, disease recurrence, type of pathology, invasion of lymphatic vessels, and in multivariate analysis, first treatment and disease recurrence had a significant association with patient survival (P<0.05).
Conclusion: According to the findings of this study, first treatment and disease recurrence were identified as factors associated with the survival of breast cancer patients. The impact of first treatment and disease recurrence on survival highlights the importance of early diagnosis, appropriate diagnostic and therapeutic services, and improving the quality of life of patients. On the other hand, the relatively high survival rate in patients referred to the Mahdieh Medical Center may indicate the importance of creating such centers for various diseases.
Materials and Methods: This retrospective cohort study was conducted from 2011 to 2021. A total of 767 breast cancer patients were referred to the Mahdieh Medical Diagnostic Center in Hamadan, Iran. Clinical and demographic information was collected by contacting the individuals through telephone calls. A Mixture Cure model was used to analyze the collected data. Data analysis was performed using smcure in R.4.0.5 software.
Results: Of the 767 breast cancer patients followed up for 10 years, 58(6.7%) individuals died. The mean (standard deviation) age at diagnosis was 49.23(10.85) years. In univariate analysis, clinical and demographic features, including age, first treatment, marital status, disease recurrence, type of pathology, invasion of lymphatic vessels, and in multivariate analysis, first treatment and disease recurrence had a significant association with patient survival (P<0.05).
Conclusion: According to the findings of this study, first treatment and disease recurrence were identified as factors associated with the survival of breast cancer patients. The impact of first treatment and disease recurrence on survival highlights the importance of early diagnosis, appropriate diagnostic and therapeutic services, and improving the quality of life of patients. On the other hand, the relatively high survival rate in patients referred to the Mahdieh Medical Center may indicate the importance of creating such centers for various diseases.
Type of Study: Original |
Subject:
Biostatistics & Epidemiology
Extended Abstract [HTML 28 KB] (54 Download)
References
1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33. PMID: 35020204 DOI: 10.3322/caac.21708
2. Sharifian A, Pourhoseingholi MA, Emadedin M, Nejad MR, Ashtari S, Hajizadeh N, et al. Burden of breast cancer in Iranian women is increasing. Asian Pac J Cancer Prev. 2015;16(12):5049-52. PMID: 26163639 DOI: 10.7314/apjcp.2015.16.12.5049
3. Keshavarzi A, Asadi S, Asadollahi A, Mohammadkhah F, Khani Jeihooni A. Tendency to breast cancer screening among rural women in Southern Iran: a structural equation modeling (SEM) analysis of theory of planned behavior. Breast Cancer (Auckl). 2022;16:1-12. PMID: 36091183 DOI: 10.1177/11782234221121001
4. Naz MS, Simbar M, Fakari FR, Ghasemi V. Effects of model-based interventions on breast cancer screening behavior of women: a systematic review. Asian Pac J Cancer Prev. 2018;19(8):2031-41. PMID: 30139040 DOI: 10.22034/APJCP.2018.19.8.2031
5. Abdyldaev D, Kudaibergenova I, Iliyazova S, Karabaeva I, Uzakbaeva B, Tagaev T. Impact of risk factors on the development of breast cancer in women from Kyrgyzstan. Biomedicine. 2023;43(02):603-9. DOI: 10.51248/.v43i02.2627
6. Ghasemi SS, Zarchi AAK, Alimohamadi Y, Raei M, Sepandi M. Survival analysis and its related factors among patients with breast cancer referred to a military hospital in Tehran. EBNESINA. 2023;25(1):4-12. DOI: 10.22034/25.1.4
7. Karimi A, Moradi Z, Sayehmiri K, Delpisheh A, Sayehmiri F. Investigation of 1 to 10-year survival of breast cancer in Iranian women: A systematic review and meta-analysis. Iran J Obstet Gynecol Infertil. 2016;19(22):17-25. DOI: 10.22038/IJOGI.2016.7706
8. Fallahzadeh H, Pahlavani N. A Study on the Prognostic Factors of. International Breast Cancer Congress; 2012.
9. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. Springer; 2003.
10. Rondeau V, Schaffner E, Corbiere F, Gonzalez JR, Mathoulin-Pelissier S. Cure frailty models for survival data: application to recurrences for breast cancer and to hospital readmissions for colorectal cancer. Stat Methods Med Res. 2013;22(3):243-60. PMID: 21632696 DOI: 10.1177/0962280210395521
11. Arano I, Sugimoto T, Hamasaki T, Ohno Y. Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder. BMC Med Res Methodol. 2010;10(1):1-13. PMID: 20412598 DOI: 10.1186/1471-2288-10-33
12. Othus M, Barlogie B, LeBlanc ML, Crowley JJ. Cure models as a useful statistical tool for analyzing survivalcure models and multiple myeloma. Clin Cancer Res. 2012;18(14):3731-6. PMID: 22675175 DOI: 10.1158/1078-0432.CCR-11-2859
13. Boag JW. Maximum likelihood estimates of the proportion of patients cured by cancer therapy. J R Stat Soc Series B. 1949;11(1):15-53.
14. Berkson J, Gage RP. Survival curve for cancer patients following treatment. J Am Stat Assoc. 1952;47(259):501-15.
15. Felizzi F, Paracha N, Pöhlmann J, Ray J. Mixture cure models in oncology: a tutorial and practical guidance. Pharmacoecon Open. 2021;5(2):143-55. PMID: 33638063 DOI: 10.1007/s41669-021-00260-z
16. Jia X, Sima CS, Brennan MF, Panageas KS. Cure models for the analysis of time‐to‐event data in cancer studies. J Surg Oncol. 2013;108(6):342-7. PMID: 24037620 DOI: 10.1002/jso.23411
17. Lambert PC, Thompson JR, Weston CL, Dickman PW. Estimating and modeling the cure fraction in population-based cancer survival analysis. Bio-statistics. 2007;8(3):576-94. PMID: 17021277 DOI: 10.1093/biostatistics/kxl030
18. Othus M, Bansal A, Koepl L, Wagner S, Ramsey S. Accounting for cured patients in cost-effectiveness analysis. Value Health. 2017;20(4):705-9. PMID: 28408015 DOI: 10.1016/j.jval.2016.04.011
19. Amico M, Van Keilegom I. Cure models in survival analysis. Annu Rev Stat Appl. 2018;5:311-42.
20. Andersson TM, Dickman PW, Eloranta S, Lambert PC. Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. BMC Med Res Methodol. 2011;11(1):1-11. PMID: 21696598 DOI: 10.1186/1471-2288-11-96
21. Jahani S, Hoseini M, Pourhamidi R, Askari M, Moslemi A. Determining the factors affecting long-term and short-term survival of breast cancer patients in rafsanjan using a mixture cure model. J Res Health Sci. 2021;21(2):e00516. PMID: 34465639 DOI: 10.34172/jrhs.2021.51
22. Amico M. Cure models in survival analysis: from modelling to prediction assessment of the cure fraction. (PhD Thesis). 2018.
23. Najafi B, Anvari S, Roshan ZA. Disease free survival among molecular subtypes of early stage breast cancer between 2001 and 2010 in Iran. Asian Pac J Cancer Prev. 2013;14(10):5811-6. PMID: 24289582 DOI: 10.7314/apjcp.2013.14.10.5811
24. Tzikas A-K, Nemes S, Linderholm BK. A comparison between young and old patients with triple-negative breast cancer: biology, survival and metastatic patterns. Breast Cancer Res Treat. 2020;182(3):643-54. PMID: 32524352 DOI: 10.1007/s10549-020-05727-x
25. Makower D, Lin J, Xue X, Sparano JA. Lympho-vascular invasion, race, and the 21-gene recurrence score in early estrogen receptor-positive breast cancer. NPJ Breast Cancer. 2021;7(1):20.
26. Wang K, Nakano K, Naderi N, Bajaj-Elliott M, Mosahebi A. Is the skin microbiota a modifiable risk factor for breast disease?: A systematic review. Breast. 2021;59:279-85. PMID: 34329949 DOI: 10.1016/j.breast.2021.07.014
27. Rhodes DJ, O'Connor MK, Phillips SW, Smith RL, Collins DA. Molecular breast imaging: a new technique using technetium Tc 99m scintimammography to detect small tumors of the breast. Mayo Clin Proc. 2005;80(1):24-30. PMID: 15667025 DOI: 10.1016/S0025-6196(11)62953-4
28. Thomas M KE, Abraham J, Kruse M, editors. Invasive lobular breast cancer: A review of pathogenesis. Semin Oncol. 2019;46(2):121-32. PMID: 31239068 DOI: 10.1053/j.seminoncol.2019.03.002
29. McCart Reed AE, Kalinowski L, Simpson PT, Lakhani SR. Invasive lobular carcinoma of the breast: the increasing importance of this special subtype. Breast Cancer Res. 2021;23(1):1-16. PMID: 33413533 DOI: 10.1186/s13058-020-01384-6
30. Vallacha A, Haider G, Raja W, Kumar D. Quality of breast cancer surgical pathology reports. Asian Pac J Cancer Prev. 2018;19(3):853-5. PMID: 29582645 DOI: 10.22034/APJCP.2018.19.3.853
31. Ding W, Ruan G, Lin Y, Zhu J, Tu C, Li Z. Dynamic changes in marital status and survival in women with breast cancer: a population-based study. Sci Rep. 2021;11(1):1-8. PMID: 33686220 DOI: 10.1038/s41598-021-84996-y
32. Li J, Chen Z, Su K, Zeng J. Clinicopathological classification and traditional prognostic indicators of breast cancer. Int J Clin Exp Pathol. 2015;8(7):8500-5. PMID: 26339424
33. Calhoun BC, Collins LC. Predictive markers in breast cancer: An update on ER and HER2 testing and reporting. Semin Diagn Pathol. 2015;32(5):362-9. PMID: 25770732 DOI: 10.1053/j.semdp.2015.02.011
34. Ghasemi F, Olfatbakhsh A, Rasekhi AA, Haghighat S. Determining the correlated factors of breast cancer recurrence by Poisson Beta-Weibull non-mixture cure model. IJBD. 2020;13(2):8-18. DOI: 10.30699/ijbd.13.2.8
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
Articles Copyright © The Author(s).
Owned by Hamadan University of Medical Sciences
Published by UMSHA Press
Journal Tel: +988138380924
Publisher Tel:+988138380548
Website: http://sjh.umsha.ac.ir
Email: s_ journal[at]umsha.ac.ir
