Original Article

Evaluating related Factors to the Number of Involved Lymph Nodes in Patients with Breast Cancer Using Zero-Inflated Negative Binomial Regression Model

Abstract

Background and aims: In Iran, breast cancer accounts for 24.4% of all cancers and contributes to 14.2% of cancer-associated mortality in women. A major challenge facing the health system is to examine the health status of patients with breast cancer, which often involves the axillary lymph nodes. The number of involved nodes should be clinically predicted to ascertain postoperative radiotherapy and chemotherapy. The present study employed regression models to investigate the determinants of the number of lymph nodes involved in patients with breast cancer.

Methods: This retrospective study recruited patients diagnosed with breast cancer during 2005-2015 referring to Shafa Hospital affiliated to Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran. The outcome variable was the number of involved lymph nodes. Regression models for count outcomes, were utilized for investigating the related factors to the number of involved lymph nodes in patients with breast cancer.

Results: A sample of 165 patients was eligible for the present study. The Akaike information criterion (AIC) of the zero-inflated negative binomial (ZINB) model was the lowest. The logistic part showed that absence of metastasis significantly increased the chance of node-negative breast cancer (P=0.027). The negative binomial part revealed an increase of 86% in the risk of a greater number of involved nodes in stage III breast cancer compared to stages I and II, suggesting that the patients were at a high risk (P=0.006).

Conclusion: Metastasis status and tumor grade significantly relate to the number of lymph nodes involved in breast cancer. Determining the factors associated with nodal involvement is crucial for the early diagnosis of breast cancer by clinicians.

1. Pelizzari G, Basile D, Zago S, Lisanti C, Bartoletti M, Bortot L, Vitale MG, Fanotto V, Barban S, Cinausero M, Bonotto M. Lactate Dehydrogenase (LDH) Response to First-Line Treatment Predicts Survival in Metastatic Breast Cancer: First Clues for a Cost-Effective and Dynamic Biomarker. Cancers. 2019 Sep;11(9):1243.
2. Presti D, Quaquarini E. The PI3K/AKT/mTOR and CDK4/6 Pathways in Endocrine Resistant HR+/HER2− Metastatic Breast Cancer: Biological Mechanisms and New Treatments. Cancers. 2019 Sep;11(9):1242.
3. Kolah DS, Sajadi A, Radmard AR, KHADEMI H. Five common cancers in Iran.
4. Abass MO, Gismalla MD, Alsheikh AA, Elhassan MM. Axillary lymph node dissection for breast cancer: efficacy and complication in developing countries. Journal of global oncology. 2018 Oct;4:1-8.
5. M. E. Akbari and G. Mohammadi, Women’s Cancers of Iran, Mohsen Publications, Tehran, 2014, (Farsi).
6. CDC Cancer Office, “National Cancer Registry Report 2007-8,” Tech. Rep., Tehran: Ministry of Health, Treatment and Education of Iran, Iran, 2009.
7. Hajian K, Gholizadehpasha A, Bozorgzadeh SH. Association of obesity and central obesity with breast cancer risk in pre-and postmenopausal women. Journal of Babol university of medical sciences. 2013 May 10;15(3):7-15.
8. Hajizadeh N. Incidence rate of breast cancer in iranian women, trend analysis from 2003 to 2009.
9. YektaKooshali MH, Esmaeilpour-Bandboni M, Sharami H, Alipour Z. Survival Rate and Average Age of the Patients with Breast Cancer in Iran: Systematic Review and Meta-Analysis. J Babol Univ Med Sci. 2016;18(8):29-40.
10. Swain PK, Grover G, Chakravorty S, Goel K, Singh V. Estimation of Number of Involved Lymph Nodes in Breast Cancer Patients using Bayesian Regression Approach.2017
11. Dwivedi AK, Dwivedi SN, Deo S, Shukla R, Kopras E. Statistical models for predicting number of involved nodes in breast cancer patients. Health. 2010 Jul;2(7):641.
12. Cui X, Wang N, Zhao Y, Chen S, Li S, Xu M, Chai R. Preoperative prediction of axillary lymph node metastasis in breast cancer using radiomics features of DCE-MRI. Scientific reports. 2019 Feb 19;9(1):1-8.
13. Cameron AC, Trivedi PK. Regression analysis of count data: Cambridge university press; 2013.
14. Mohammadi T, Kheiri S, Sedehi M. The application of zero-inflated count regression models for identifying main factors on the number of blood donor deferral in Shahrekord. J Shahrekord Univ Med Sci. 2016; 18(5): 26-35.
15. Bakhshi E, Yazdanipour MA, Rahgozar M, Ghorbani Z, Deghatipour M. Overall Effects of Risk Factors Associated with Dental Caries Indices Using the Marginalized Zero-Inflated Negative Binomial Model. Caries research. 2019 Jan 1;53(4):1-6.
16. Cantarero Prieto D, Pascual Sáez M, Lera Torres JI. Socioeconomic determinants and health care utilization among elderly people living in Europe: Evidence from the Survey of Health, Ageing and Retirement. 2018.
17. Hilbe JM. Negative binomial regression: Cambridge University Press; 2011.
18. Lambert D. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1-14.
19. Greene WH. Accounting for excess zeros and sample selection in Poisson and negative binomial regression models. 1994.
20. Kamalja KK, Wagh YS. Estimation in zero-inflated Generalized Poisson distribution. Journal of Data Science. 2018 Jan 1;16(1):183-206.
21. Mohammed AA. Predictive factors affecting axillary lymph node involvement in patients with breast cancer in Duhok: Cross-sectional study. Annals of Medicine and Surgery. 2019 Aug 1;44:87-90.
22. Keihanian S, Koochaki N, Pouya M, Zakerihamidi M. Factors Affecting axillary lymph node involvement in patients with breast cancer. Tehran University Medical Journal TUMS Publications. 2019 Nov 10;77(8):484-90.
23. Chakraborty A, Bose CK, Basak J, Sen AN, Mishra R, Mukhopadhyay A. Determinants of lymph node status in women with breast cancer: A hospital based study from eastern India. The Indian journal of medical research. 2016 May;143 Suppl 1):S45.
24. Guern AS, Vinh-Hung V. Statistical distribution of involved axillary lymph nodes in breast cancer. Bulletin du cancer. 2008 Apr 1;95(4):449-55.
25. Kendal WS. Statistical kinematics of axillary nodal metastases in breast carcinoma. Clinical & experimental metastasis. 2005 Apr 1;22(2):177-83.
26. Schaapveld M, Otter R, de Vries EG, Fidler V, Grond JA, van der Graaf WT, de Vogel PL, Willemse PH. Variability in axillary lymph node dissection for breast cancer. Journal of surgical oncology. 2004 Jul 15;87(1):4-12.
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IssueVol 6 No 4 (2020) QRcode
SectionOriginal Article(s)
Published2021-02-24
DOI https://doi.org/10.18502/jbe.v6i4.5679
Keywords
Number of involved lymph nodes breast cancer count outcome

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How to Cite
1.
Younespour S, Maraghi E, Saki Malehi A, Raissizadeh M, Seghatoleslami M, Hosseinzadeh M. Evaluating related Factors to the Number of Involved Lymph Nodes in Patients with Breast Cancer Using Zero-Inflated Negative Binomial Regression Model. jbe. 2021;6(4):259-266.