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Published in: BMC Women's Health 1/2024

Open Access 01-12-2024 | Breast Cancer | Research

Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach

Authors: Chalachew Gashu, Aragaw Eshetie Aguade

Published in: BMC Women's Health | Issue 1/2024

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Abstract

Background

Despite the significant weight of difficulty, Ethiopia's survival rate and mortality predictors have not yet been identified. Finding out what influences outpatient breast cancer patients' survival time was the major goal of this study.

Methods

A retrospective study was conducted on outpatients with breast cancer. In order to accomplish the goal, 382 outpatients with breast cancer were included in the study using information obtained from the medical records of patients registered at the University of Gondar referral hospital in Gondar, Ethiopia, between May 15, 2016, and May 15, 2020. In order to compare survival functions, Kaplan-Meier plots and the log-rank test were used. The Cox-PH model and Bayesian parametric survival models were then used to examine the survival time of breast cancer outpatients. The use of integrated layered Laplace approximation techniques has been made.

Results

The study included 382 outpatients with breast cancer in total, and 148 (38.7%) patients died. 42 months was the estimated median patient survival time. The Bayesian Weibull accelerated failure time model was determined to be suitable using model selection criteria. Stage, grade 2, 3, and 4, co-morbid, histological type, FIGO stage, chemotherapy, metastatic number 1, 2, and >=3, and tumour size all have a sizable impact on the survival time of outpatients with breast cancer, according to the results of this model. The breast cancer outpatient survival time was correctly predicted by the Bayesian Weibull accelerated failure time model.

Conclusions

Compared to high- and middle-income countries, the overall survival rate was lower. Notable variables influencing the length of survival following a breast cancer diagnosis were weight loss, invasive medullar histology, comorbid disease, a large tumour size, an increase in metastases, an increase in the International Federation of Gynaecologists and Obstetricians stage, an increase in grade, lymphatic vascular space invasion, positive regional nodes, and late stages of cancer. The authors advise that it is preferable to increase the number of early screening programmes and treatment centres for breast cancer and to work with the public media to raise knowledge of the disease's prevention, screening, and treatment choices.
Literature
1.
go back to reference Abate S, Yilma Z, Assefa M, Tigeneh W. Trends of breast cancer in Ethiopia. Int J Cancer Res Mol Mech. 2016;2(1):1. Abate S, Yilma Z, Assefa M, Tigeneh W. Trends of breast cancer in Ethiopia. Int J Cancer Res Mol Mech. 2016;2(1):1.
2.
go back to reference Tiruneh M, Tesfaw A, Tesfa D. Survival and predictors of mortality among breast cancer patients in Northwest Ethiopia: a retrospective cohort study. Cancer Manag Res. 2021;9225-34. Tiruneh M, Tesfaw A, Tesfa D. Survival and predictors of mortality among breast cancer patients in Northwest Ethiopia: a retrospective cohort study. Cancer Manag Res. 2021;9225-34.
3.
go back to reference Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C. GLOBOCAN 2012 v1. 0, Cancer Incidence and Mortality Worldwide. France: International Agency for Research on Cancer; 2013. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C. GLOBOCAN 2012 v1. 0, Cancer Incidence and Mortality Worldwide. France: International Agency for Research on Cancer; 2013.
4.
go back to reference Organization WH. Noncommunicable diseases country profiles 2018. 2018. Organization WH. Noncommunicable diseases country profiles 2018. 2018.
5.
go back to reference Abate S, Yilma Z, Assefa M, Tigeneh W. Trends of breast cancer in Ethiopia. Int J Cancer Res Mol Mech. 2016;2(1):1. Abate S, Yilma Z, Assefa M, Tigeneh W. Trends of breast cancer in Ethiopia. Int J Cancer Res Mol Mech. 2016;2(1):1.
6.
go back to reference Arora D, Hasan S, Male E, Pruszynski J, Ord C, Rao A. Prognostic factors affecting outcomes in triple negative breast cancer. Int J Radiat Oncol Biol Phys. 2015;93(3):E33.CrossRef Arora D, Hasan S, Male E, Pruszynski J, Ord C, Rao A. Prognostic factors affecting outcomes in triple negative breast cancer. Int J Radiat Oncol Biol Phys. 2015;93(3):E33.CrossRef
7.
go back to reference Leivonen MK, Kalima TV. Prognostic factors associated with survival after breast cancer recurrence. Acta Oncol (Madr). 1991;30(5):583–6.CrossRef Leivonen MK, Kalima TV. Prognostic factors associated with survival after breast cancer recurrence. Acta Oncol (Madr). 1991;30(5):583–6.CrossRef
8.
go back to reference Kantelhardt EJ, Zerche P, Mathewos A, Trocchi P, Addissie A, Aynalem A, et al. Breast cancer survival in Ethiopia: a cohort study of 1,070 women. Int J Cancer. 2014;135(3):702–9.CrossRefPubMed Kantelhardt EJ, Zerche P, Mathewos A, Trocchi P, Addissie A, Aynalem A, et al. Breast cancer survival in Ethiopia: a cohort study of 1,070 women. Int J Cancer. 2014;135(3):702–9.CrossRefPubMed
9.
go back to reference Kotepui M, Chupeerach C. Age distribution of breast cancer from a Thailand population-based cancer registry. Asian Pacific J Cancer Prev. 2013;14(6):3815–7.CrossRef Kotepui M, Chupeerach C. Age distribution of breast cancer from a Thailand population-based cancer registry. Asian Pacific J Cancer Prev. 2013;14(6):3815–7.CrossRef
10.
go back to reference Velie EM, Schairer C, Flood A, He J-P, Khattree R, Schatzkin A. Empirically derived dietary patterns and risk of postmenopausal breast cancer in a large prospective cohort study. Am J Clin Nutr. 2005;82(6):1308–19.CrossRefPubMed Velie EM, Schairer C, Flood A, He J-P, Khattree R, Schatzkin A. Empirically derived dietary patterns and risk of postmenopausal breast cancer in a large prospective cohort study. Am J Clin Nutr. 2005;82(6):1308–19.CrossRefPubMed
11.
go back to reference Chan DSM, Abar L, Cariolou M, Nanu N, Greenwood DC, Bandera EV, et al. World Cancer Research Fund International: Continuous Update Project—systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes Control. 2019;30:1183–200.CrossRefPubMed Chan DSM, Abar L, Cariolou M, Nanu N, Greenwood DC, Bandera EV, et al. World Cancer Research Fund International: Continuous Update Project—systematic literature review and meta-analysis of observational cohort studies on physical activity, sedentary behavior, adiposity, and weight change and breast cancer risk. Cancer Causes Control. 2019;30:1183–200.CrossRefPubMed
12.
go back to reference Memirie ST, Habtemariam MK, Asefa M, Deressa BT, Abayneh G, Tsegaye B, et al. Estimates of cancer incidence in Ethiopia in 2015 using population-based registry data. J Glob Oncol. 2018;4:1–11.PubMed Memirie ST, Habtemariam MK, Asefa M, Deressa BT, Abayneh G, Tsegaye B, et al. Estimates of cancer incidence in Ethiopia in 2015 using population-based registry data. J Glob Oncol. 2018;4:1–11.PubMed
13.
go back to reference Al-Foheidi M, Al-Mansour MM, Ibrahim EM. Breast cancer screening: review of benefits and harms, and recommendations for developing and low-income countries. Med Oncol. 2013;30:1–15.CrossRef Al-Foheidi M, Al-Mansour MM, Ibrahim EM. Breast cancer screening: review of benefits and harms, and recommendations for developing and low-income countries. Med Oncol. 2013;30:1–15.CrossRef
14.
go back to reference Legesse B, Gedif T. Knowledge on breast cancer and its prevention among women household heads in Northern Ethiopia. Open J Prev Med. 2014;4(01):32-40. Legesse B, Gedif T. Knowledge on breast cancer and its prevention among women household heads in Northern Ethiopia. Open J Prev Med. 2014;4(01):32-40.
15.
go back to reference Yazdani A, Dorri S, Atashi A, Shirafkan H, Zabolinezhad H. Bone metastasis prognostic factors in breast cancer. Breast Cancer Basic Clin Res. 2019;13:1178223419830978.CrossRef Yazdani A, Dorri S, Atashi A, Shirafkan H, Zabolinezhad H. Bone metastasis prognostic factors in breast cancer. Breast Cancer Basic Clin Res. 2019;13:1178223419830978.CrossRef
16.
17.
go back to reference Gashu C, Tasfa B, Alemu C, Kassa Y. Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach. BMC Womens Health. 2023;23(1):1–14.CrossRef Gashu C, Tasfa B, Alemu C, Kassa Y. Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach. BMC Womens Health. 2023;23(1):1–14.CrossRef
18.
go back to reference Khanal SP, Sreenivas V, Acharya SK. Accelerated failure time models: an application in the survival of acute liver failure patients in India. Int J Sci Res. 2014;3(6):161–6. Khanal SP, Sreenivas V, Acharya SK. Accelerated failure time models: an application in the survival of acute liver failure patients in India. Int J Sci Res. 2014;3(6):161–6.
19.
go back to reference Qi J. Comparison of proportional hazards and accelerated failure time models. 2009. Qi J. Comparison of proportional hazards and accelerated failure time models. 2009.
21.
go back to reference Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Stat Comput. 2014;24(6):997–1016.MathSciNetCrossRef Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Stat Comput. 2014;24(6):997–1016.MathSciNetCrossRef
22.
go back to reference Berger JO. Statistical decision theory and Bayesian analysis. Springer Science & Business Media; 2013. Berger JO. Statistical decision theory and Bayesian analysis. Springer Science & Business Media; 2013.
23.
go back to reference Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat. 1998;7(4):434–55.MathSciNet Brooks SP, Gelman A. General methods for monitoring convergence of iterative simulations. J Comput Graph Stat. 1998;7(4):434–55.MathSciNet
24.
go back to reference Rue H, Riebler A, Sørbye SH, Illian JB, Simpson DP, Lindgren FK. Bayesian computing with INLA: a review. Annu Rev Stat Its Appl. 2017;4:395–421.CrossRef Rue H, Riebler A, Sørbye SH, Illian JB, Simpson DP, Lindgren FK. Bayesian computing with INLA: a review. Annu Rev Stat Its Appl. 2017;4:395–421.CrossRef
25.
go back to reference Belayneh T, Adefris M, Andargie G. Previous early antenatal service utilization improves timely booking: cross-sectional study at university of Gondar hospital, northwest Ethiopia. J Pregnancy. 2014;2014. Belayneh T, Adefris M, Andargie G. Previous early antenatal service utilization improves timely booking: cross-sectional study at university of Gondar hospital, northwest Ethiopia. J Pregnancy. 2014;2014.
27.
28.
go back to reference Aalen O, Borgan O, Gjessing H. Survival and event history analysis: a process point of view. Springer Science & Business Media; 2008. Aalen O, Borgan O, Gjessing H. Survival and event history analysis: a process point of view. Springer Science & Business Media; 2008.
29.
go back to reference Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. Vol. 1230. Springer; 2003. Klein JP, Moeschberger ML. Survival analysis: techniques for censored and truncated data. Vol. 1230. Springer; 2003.
30.
go back to reference Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):719–48.PubMed Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):719–48.PubMed
32.
go back to reference Depaoli S. The impact of inaccurate “informative” priors for growth parameters in Bayesian growth mixture modeling. Struct Equ Model A Multidiscip J. 2014;21(2):239–52.MathSciNetCrossRef Depaoli S. The impact of inaccurate “informative” priors for growth parameters in Bayesian growth mixture modeling. Struct Equ Model A Multidiscip J. 2014;21(2):239–52.MathSciNetCrossRef
33.
go back to reference Ibrahim JG, Chen M-H, Sinha D, Ibrahim JG, Chen MH. Bayesian survival analysis. Vol. 2. Springer; 2001. Ibrahim JG, Chen M-H, Sinha D, Ibrahim JG, Chen MH. Bayesian survival analysis. Vol. 2. Springer; 2001.
34.
go back to reference Ganjali M, Baghfalaki T. Bayesian analysis of unemployment duration data in the presence of right and interval censoring. J Reliab Stat Stud. 2012;17–32. Ganjali M, Baghfalaki T. Bayesian analysis of unemployment duration data in the presence of right and interval censoring. J Reliab Stat Stud. 2012;17–32.
35.
go back to reference Akerkar R, Martino S, Rue H. Implementing approximate Bayesian inference for survival analysis using integrated nested Laplace approximations. Prepr Stat Nor Univ Sci Technol. 2010;1:1–38. Akerkar R, Martino S, Rue H. Implementing approximate Bayesian inference for survival analysis using integrated nested Laplace approximations. Prepr Stat Nor Univ Sci Technol. 2010;1:1–38.
36.
go back to reference Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Vol. 13. John Wiley & Sons; 2004. Spiegelhalter DJ, Abrams KR, Myles JP. Bayesian approaches to clinical trials and health-care evaluation. Vol. 13. John Wiley & Sons; 2004.
37.
go back to reference Watanabe S, Opper M. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11(12). Watanabe S, Opper M. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res. 2010;11(12).
39.
go back to reference Areri HA, Shibabaw W, Mulugeta T, Asmare Y, Yirga T. Survival status and predictors of mortality among breast cancer patients in adult oncology unit at black lion specialized hospital, addis ababa, ethiopia, 2018. View Publ Site. 2019; Areri HA, Shibabaw W, Mulugeta T, Asmare Y, Yirga T. Survival status and predictors of mortality among breast cancer patients in adult oncology unit at black lion specialized hospital, addis ababa, ethiopia, 2018. View Publ Site. 2019;
40.
go back to reference Azubuike SO, Muirhead C, Hayes L, McNally R. Rising global burden of breast cancer: the case of sub-Saharan Africa (with emphasis on Nigeria) and implications for regional development: a review. World J Surg Oncol. 2018;16(1):1–13.CrossRef Azubuike SO, Muirhead C, Hayes L, McNally R. Rising global burden of breast cancer: the case of sub-Saharan Africa (with emphasis on Nigeria) and implications for regional development: a review. World J Surg Oncol. 2018;16(1):1–13.CrossRef
41.
go back to reference Witteveen A, Nane GF, Vliegen IMH, Siesling S, IJzerman MJ. Comparison of logistic regression and bayesian networks for risk prediction of breast cancer recurrence. Med Decis Mak an Int J Soc Med Decis Mak. 2018;38(7):822–33.CrossRef Witteveen A, Nane GF, Vliegen IMH, Siesling S, IJzerman MJ. Comparison of logistic regression and bayesian networks for risk prediction of breast cancer recurrence. Med Decis Mak an Int J Soc Med Decis Mak. 2018;38(7):822–33.CrossRef
42.
go back to reference Avc? E. Bayesian survival analysis: comparison of survival probability of hormone receptor status for breast cancer data. Int J Data Anal Tech Strateg. 2017;9(1):63–74. Avc? E. Bayesian survival analysis: comparison of survival probability of hormone receptor status for breast cancer data. Int J Data Anal Tech Strateg. 2017;9(1):63–74.
43.
go back to reference Teng J, Zhang H, Liu W, Shu X-O, Ye F. A Dynamic Bayesian Model for Breast Cancer Survival Prediction. IEEE J Biomed Heal Inform. 2022;26(11):5716–27.CrossRef Teng J, Zhang H, Liu W, Shu X-O, Ye F. A Dynamic Bayesian Model for Breast Cancer Survival Prediction. IEEE J Biomed Heal Inform. 2022;26(11):5716–27.CrossRef
44.
go back to reference Teng J, Abdygametova A, Du J, Ma B, Zhou R, Shyr Y, et al. Bayesian inference of lymph node ratio estimation and survival prognosis for breast cancer patients. IEEE J Biomed Heal Inform. 2020;24(2):354–64.CrossRef Teng J, Abdygametova A, Du J, Ma B, Zhou R, Shyr Y, et al. Bayesian inference of lymph node ratio estimation and survival prognosis for breast cancer patients. IEEE J Biomed Heal Inform. 2020;24(2):354–64.CrossRef
45.
go back to reference Misganaw M, Zeleke H, Mulugeta H, Assefa B. Mortality rate and predictors among patients with breast cancer at a referral hospital in northwest Ethiopia: a retrospective follow-up study. PLoS One. 2023;18(1):e0279656.CrossRefPubMedPubMedCentral Misganaw M, Zeleke H, Mulugeta H, Assefa B. Mortality rate and predictors among patients with breast cancer at a referral hospital in northwest Ethiopia: a retrospective follow-up study. PLoS One. 2023;18(1):e0279656.CrossRefPubMedPubMedCentral
46.
go back to reference Yesuf T. Survival and Associated Factors among Cervical Cancer Patients in Black Lion Hospital, Addis Ababa, Ethiopia, 2008-2012, a Retrospective Longitudinal Study (Doctoral dissertation, Addis Ababa University). Yesuf T. Survival and Associated Factors among Cervical Cancer Patients in Black Lion Hospital, Addis Ababa, Ethiopia, 2008-2012, a Retrospective Longitudinal Study (Doctoral dissertation, Addis Ababa University).
47.
48.
go back to reference Donkor A, Lathlean J, Wiafe S, Vanderpuye V, Fenlon D, Yarney J, et al. Factors contributing to late presentation of breast cancer in Africa: a systematic literature review. Arch Med. 2015;8(2.2):1–10. Donkor A, Lathlean J, Wiafe S, Vanderpuye V, Fenlon D, Yarney J, et al. Factors contributing to late presentation of breast cancer in Africa: a systematic literature review. Arch Med. 2015;8(2.2):1–10.
49.
go back to reference Maskarinec G, Pagano I, Lurie G, Bantum E, Gotay CC, Issell BF. Factors affecting survival among women with breast cancer in Hawaii. J Women’s Heal. 2011;20(2):231–7.CrossRef Maskarinec G, Pagano I, Lurie G, Bantum E, Gotay CC, Issell BF. Factors affecting survival among women with breast cancer in Hawaii. J Women’s Heal. 2011;20(2):231–7.CrossRef
50.
go back to reference Dawood S, Ueno NT, Valero V, Woodward WA, Buchholz TA, Hortobagyi GN, et al. Identifying factors that impact survival among women with inflammatory breast cancer. Ann Oncol. 2012;23(4):870–5.CrossRefPubMed Dawood S, Ueno NT, Valero V, Woodward WA, Buchholz TA, Hortobagyi GN, et al. Identifying factors that impact survival among women with inflammatory breast cancer. Ann Oncol. 2012;23(4):870–5.CrossRefPubMed
51.
go back to reference Makanjuola SBL, Popoola AO, Oludara MA. Radiation therapy: a major factor in the five-year survival analysis of women with breast cancer in Lagos Nigeria. Radiother Oncol. 2014;111(2):321–6.CrossRefPubMed Makanjuola SBL, Popoola AO, Oludara MA. Radiation therapy: a major factor in the five-year survival analysis of women with breast cancer in Lagos Nigeria. Radiother Oncol. 2014;111(2):321–6.CrossRefPubMed
52.
go back to reference Gakwaya A, Kigula-Mugambe JB, Kavuma A, Luwaga A, Fualal J, Jombwe J, et al. Cancer of the breast: 5-year survival in a tertiary hospital in Uganda. Br J Cancer. 2008;99(1):63–7.CrossRefPubMedPubMedCentral Gakwaya A, Kigula-Mugambe JB, Kavuma A, Luwaga A, Fualal J, Jombwe J, et al. Cancer of the breast: 5-year survival in a tertiary hospital in Uganda. Br J Cancer. 2008;99(1):63–7.CrossRefPubMedPubMedCentral
53.
go back to reference Shita A, Yalew AW, Seife E, Afework T, Tesfaw A, Gufue ZH, Rabe F, Taylor L, Kantelhardt EJ, Getachew S. Survival and predictors of breast cancer mortality in South Ethiopia: A retrospective cohort study. Plos one. 2023;18(3):e0282746. Shita A, Yalew AW, Seife E, Afework T, Tesfaw A, Gufue ZH, Rabe F, Taylor L, Kantelhardt EJ, Getachew S. Survival and predictors of breast cancer mortality in South Ethiopia: A retrospective cohort study. Plos one. 2023;18(3):e0282746.
54.
go back to reference August J. Days of the week: Months of the year: Marketing: Nature: Gdd. 2000;8(2):1–4. August J. Days of the week: Months of the year: Marketing: Nature: Gdd. 2000;8(2):1–4.
55.
go back to reference Zang L, Chen Q, Zhang X, Zhong X, Chen J, Fang Y, et al. Nomogram Predicting Overall Survival in Patients with FIGO II to III Squamous Cell Cervical Carcinoma Under Radical Radiotherapy: a Retrospective Analysis Based on 2018 FIGO Staging. Cancer Manag Res. 2021;13:9391.CrossRefPubMedPubMedCentral Zang L, Chen Q, Zhang X, Zhong X, Chen J, Fang Y, et al. Nomogram Predicting Overall Survival in Patients with FIGO II to III Squamous Cell Cervical Carcinoma Under Radical Radiotherapy: a Retrospective Analysis Based on 2018 FIGO Staging. Cancer Manag Res. 2021;13:9391.CrossRefPubMedPubMedCentral
56.
go back to reference Martino S, Akerkar R, Rue H. Approximate Bayesian inference for survival models. Scand J Stat. 2011;38(3):514–28.MathSciNetCrossRef Martino S, Akerkar R, Rue H. Approximate Bayesian inference for survival models. Scand J Stat. 2011;38(3):514–28.MathSciNetCrossRef
Metadata
Title
Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach
Authors
Chalachew Gashu
Aragaw Eshetie Aguade
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Women's Health / Issue 1/2024
Electronic ISSN: 1472-6874
DOI
https://doi.org/10.1186/s12905-024-02954-y

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