A study on breast cancer diagnostic decisions: Impact of resource constraints and patient preferences on optimal decisions

Breast cancer is the most frequently diagnosed cancer and leading cause of cancer mortality among women. Early diagnosis through screening mammography is the most effective means of reducing breast cancer deaths. Although mammography is effective, actual diagnosis requires additional imaging and/or biopsy based on a suspicious finding detected on a screening mammogram. Therefore, when presented with the findings on a mammogram, a radiologist manages the disease through three actions: routine screening; short-term follow-up; or biopsy. Failures in the post-mammography management decisions are not rare and may cause additional morbidity and anxiety without influencing mortality and may result in unnecessary health care expenditures. An analytical approach to optimal diagnostic decisions while considering budgetary restrictions and the role of risk preferences could shed light on better management of the disease and could inform the guidelines.;We study three aspects of breast cancer diagnosis problem: cost, risk, and aging. First, we demonstrate how budgetary constraints may alter a radiologist's diagnostic decisions in the pursuit of optimal breast cancer diagnosis as measured by quality adjusted life years, i.e., given mammography features, demographic factors, and a limited budget, what is the optimal course of action; routine screening, short-term follow-up or biopsy? Decision models representing the clinical situations where treatment options entail risk of morbidity or mortality should consider the variations in risk preferences of individuals. Second, we investigate the role of risk preferences in post-mammography diagnostic decisions. In particular, we maximize the total expected utility of a patient, where utility in the Von Neuman-Morgenstern sense is defined over quality-adjusted survival duration. Breast cancer can be of either invasive or non-invasive type. These diseases entail different treatment and management strategies at different ages. Therefore pre-biopsy differentiation could help reduce unnecessary biopsies. Differentiation of invasive carcinoma from ductal carcinoma in situ (DCIS) on mammography and determining most predictive variables for different age groups would be helpful to facilitate clinical decision-making. Third, we use statistical modeling to determine whether there are age-related predictive differences when discriminating invasive cancer from DCIS.