Optimal Time Intervals Between Personal Mammogram Test Decisions
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DOI:
https://doi.org/10.59287/ijanser.650Keywords:
Decision Models, Mammogram Testing, Cancer, Utility Theory, Decision Trees, Bayes TheoremAbstract
Excluding skin cancer, breast cancer is the most common type of cancer in women in the United States, accounting for one in three diagnosed cases. A woman's chance of developing invasive breast cancer in her lifetime is approximately 1 in 8 (12%). While mammography has been effective in early detection, its use has resulted in a minor increase in the number of in situ cancers detected. However, mammography carries potential risks, such as exposure to unnecessary radiation, additional costs, psychological stress, and the possibility of false-positive results. Although the American Cancer Society recommends annual testing, it may be unnecessary for healthy women. In this paper, we aim to find the optimal interval between mammogram tests, balancing the benefits and risks of testing while reducing the false-positive rate and number of tests. To achieve this, we use decision trees, utility theory, and Bayes' theorem to calculate the quality-adjusted life years (QALYs) of patients.
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References
ACS. 2009. Breast cancer facts and figures: 2009-2010. American Cancer Society, Atlanta.
Gail, M., & Rimer, B. (1998). Risk-based recommendations for mammographic screening for women in their forties. Journal of clinical oncology, 16(9), 3105-3114.
Olson, D. L., & Araz, Ö. M. (2023). Multiple Criteria Decision Models in Healthcare. Data Mining and Analytics in Healthcare Management: Applications and Tools, 151-160.
Maillart, L. M., Ivy, J. S., Ransom, S., & Diehl, K. (2008). Assessing dynamic breast cancer screening policies. Operations Research, 56(6), 1411-1427.
Ivy, J. L., Goforth Jr, H. W., Damon, B. M., McCauley, T. R., Parsons, E. C., & Price, T. B. (2002). Early postexercise muscle glycogen recovery is enhanced with a carbohydrate-protein supplement. Journal of applied physiology, 93(4), 1337-1344.
Saunders, RS, Samei, E, 2006, Improving mammographic decision accuracy by incorporating observer ratings with interpretation time, The British Journal of Radiology, 79, S117–S122
Peterman, RM, Anderson JL,1999, Decision Analysis: A Method for Taking Uncertainties into Account in Risk-Based Decision Making, Human and Ecological Risk Assessment: An International Journal, Volume 5, Issue-2
Schaefer, A.J., M.D. Bailey, S.M. Shechter, M.S. Roberts. 2004. Modeling medical treatment using Markov decision processes. Operations Research and Health Care: A Handbook of Methods and Applications 597-616.
Baker JA, Kornguth PJ, Lo JY, Williford ME, Floyd CE Jr. 1995, Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. Radiology.;196(3):817-22.
Baker JA, Kornguth PJ, Lo JY, Floyd CE Jr., 1996, Artificial neural network: improving the quality of breast biopsy recommendations. Radiology. 198(1):131-5.
Floyd CE, JY Lo, AJ Yun, DC Sullivan, PJ, 1994, Kornguth, Prediction of breast cancer malignancy using an artificial neural network., Cancer, United States, , 2944-8.
Sütçü, M., Güner, P., & Ersöz, N. Ş. (2022). Analysis of under-five mortality by diseases in countries with different levels of development: a comparative analysis. The European Research Journal, 1-13.
Brewer, N.T., T. Salz, S.E. Lillie. 2007. Systematic review: the long-term effects of false-positive mammograms. Annals of Internal Medicine 146(7) 502-510.
