International Journal of Scientific & Technology Research

IJSTR@Facebook IJSTR@Twitter IJSTR@Linkedin
Home About Us Scope Editorial Board Blog/Latest News Contact Us

IJSTR >> Volume 6 - Issue 9, September 2017 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Prediction Model For Risk Of Breast Cancer Considering Interaction Between The Risk Factors

[Full Text]



Nabila Al Balushi



Interaction, Predictive model, AIC, GLM, Likelihood, Correlation, risk



This paper focuses on expansion of Barlow’s predictive model in which a large data set of 2,392,998 eligible screening mammograms taken from Breast Cancer Surveillance Consortium which was previously used by Barlow in 2006 to predict a diagnosis of breast cancer in women through including interaction of exploratory variables. 12 explanatory variables that are assumed to influence the risk of developing breast cancer in women and they are :age, breast density, menopause status, race, Hispanic, BMI ,number of first degree relatives with breast cancer, previous breast procedure, age at first birth, surgical menopause, results of last mammogram, and current hormone therapy. Forward selection method was used to select the best predictive model including significant interaction terms. The results showed 33 interactions were included in the new model through forward selection procedure improved the predictive model. However, only 10 interaction terms were found to be significant across all levels of the risk factors. Also, the updated predictive model was found to better than the main effect model, as the AIC value decreased.



[1]. Anon, (2017). [online] Available at: http://www.cancerresearchuk.org/cancer-info/prod_consump/groups/cr_common/@nre/@sta/documents/generalcontent/018070.pdf [Accessed 22 Jun. 2017].

[2]. Barlow, W. E., White, E., Ballard-Barbash, R., Vacek, P. M., Titus-Ernsto , L., Carney, P. A., Tice, J. A., Buist, D. S. M., Geller, B. M., Rosenberg, R., Yankaskas, B. C., and Kerlikowske, K. (2006). Prospective breast cancer risk prediction model for women undergoing screening mammography. JNCI Journal of the National Cancer Institute, 98(17):1204{1214}.

[3]. Bellcross, C. (2009). Approaches to applying breast cancer risk prediction models in clinical practice. Community Oncology, 6(8), pp.373-382.

[4]. Bondy, M. and Newman, L. (2006). Assessing Breast Cancer Risk: Evolution of the Gail Model. JNCI Journal of the National Cancer Institute, 98(17), pp.1172-1173.

[5]. Breast Cancer Care. (2017). Press pack: Facts and Statistics 2015. [online] Available at: https://www.breastcancercare.org.uk/about-us/media/press-pack-breast-cancer-awareness-month/facts-statistics [Accessed 22 Jun. 2017].

[6]. Burnham, K. and Anderson, D. (2010). Model selection and multimodel inference. New York, NY [u.a.]: Springer.

[7]. Chalabi, M. (2017). Breast cancer: worldwide and UK trends. [online] the Guardian. Available at: https://www.theguardian.com/news/datablog/2013/may/14/breast-cancer-worldwide-uk [Accessed 22 Jun. 2017].

[8]. Nhs.uk. (2017). Breast cancer (female) - Diagnosis - NHS Choices. [online] Available at: http://www.nhs.uk/Conditions/Cancer-of-the-breast-female/Pages/Diagnosis.aspx [Accessed 22 Jun. 2017].

[9]. Salkind, N. (2007). Encyclopedia of measurement and statistics. Thousand Oaks [u.a.]: SAGE.

[10]. Surakasula, A., Nagarjunapu, G. C., & Raghavaiah, K. V. (2014). A comparative study of pre- and post-menopausal breast cancer: Risk factors, presentation, characteristics and management. Journal of Research in Pharmacy Practice, 3(1), 12–18. http://doi.org/10.4103/2279-042X.132704

[11]. Usersfsu.edu. (2017). Logistic Regression. [online] Available at:http://userwww.sfsu.edu/efc/classes/biol710/logistic/logisticreg.htm [Accessed 20 Jul. 2017].

[12]. Zuur, A., Hilbe, J. and Ieno, E. (2015). A beginner's guide to GLM and GLMM with R. Newburgh: Highland Statistics.