Date of Award
Doctor of Philosophy (PhD)
Computational Analysis and Modeling
Breast cancer is a leading cause of premature mortality among women in the United States. Breast cancer screening tests can help with detecting breast cancer in early stages and thereby reducing the breast cancer mortality risk. However, due to the imperfect nature of screening tests, there is always some associated overdiagnosis, false positives, and false negatives risks. Therefore, to improve breast cancer preventive care, we defined the focus of this dissertation on modeling breast cancer screening decisions.
Breast cancer overdiagnosis is the first issue that is addressed in this dissertation. Although overdiagnosis is known to be the major risk inherent in mammography screening; currently there is no way to distinguish between overdiagnosed cancers and the ones that would cause problems over a patient’s lifetime. Overdiagnosis risk significantly depends on a patient’s compliance with screening recommendations. In Chapter 2, we use a stochastic framework to perform a harm-benefit analysis to compare the overdiagnosis risk with the benefits that breast cancer screening provides. In addition, we estimate the lifetime mortality risk of breast cancer while considering the overdiagnosis risk and the uncertainty in a patient’s adherence behavior. Our results show that, although overdiagnosis rate is relatively high in breast cancer screening, the benefits of breast cancer mammography screening outweigh the overdiagnosis risk.
The second issue that is addressed in this dissertation is false negative results caused by density of breast tissue. Breast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws, require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (e.g., ultrasound). Since the enactment of the laws, there have been controversial debates over i) their implementations due to the potential radiologists bias in breast density classification of mammogram images and ii) the necessity of supplemental screenings for all patients with high breast density. Breast density is a dynamic risk factor. Therefore, in the third chapter, we apply a hidden Markov model (HMM) on a sparse unbalanced longitudinal data to quantify the yearly progression of breast density based on Breast Imaging Reporting and Data System (BI-RADs) classifications.
In Chapter 4, we use the results from previous chapter to investigate the effectiveness of supplemental screening and the impact of radiologists’ bias on patients’ outcomes under the breast density notification law. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime and the expected number of supplemental tests as patient’s outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists’ performances are shown to be comparable with the performance of a perfect radiologist.
Molani, Sevda, "" (2020). Dissertation. 893.