Prediction of Individualized Risk of Contralateral Breast Cancer




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Women diagnosed with cancer in one breast are increasingly choosing to remove their other unaffected (contralateral) breast through a surgery called contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary because the risk of CBC has, in fact, gone down substantially mainly due to availability of effective therapies for breast cancer (BC), which have a preventative effect on the contralateral breast. Thus, this dramatic rise in the rate of CPMs is a particularly disturbing trend. Research shows that many BC patients tend to substantially overestimate their CBC risk. Although CPM reduces the risk of CBC, there is no convincing evidence that it prolongs survival. The surgery also has a significant number of side effects and can have an adverse effect on a woman’s health and well-being. Thus, there is a pressing need to educate patients effectively on their CBC risk. For this task, physicians need a statistical model for risk prediction of CBC based on patient’s personal risk factors. This dissertation is focused on filling this critical need. Although several risk factors for CBC are well established in the literature, one factor that is relatively less well-studied is mammographic breast density. This factor has come to the attention of the scientific community only recently and, in particular, it has been shown that increased breast density is a strong risk factor for first BC. Thus, it is of interest to study if it is associated with the risk of CBC as well. To this end, first we studied the relationship between breast density and CBC by analyzing data from Breast Cancer Surveillance Consortium (BCSC), which is a large population based source consisting of seven cancer registries across the US. We found that breast density is an independent and significant risk factor for development of CBC. In particular, breast density has a dose dependent effect on the risk of CBC, with increased breast density associated with increased risk. Next, we developed a CBC risk prediction model using data from BCSC and also Surveillance, Epidemiology, and End Results, another large population based source. We explored numerous potential risk factors for inclusion into this model. The final model consists of eight risk factors — age at first BC diagnosis, anti-estrogen therapy, family history of BC, high risk pre-neoplasia, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates of these factors with the relevant hazard rates, our model, named CBCRisk, projects absolute risk of developing CBC over a given period. Finally, we validated CBCRisk on clinical datasets from the MD Anderson Cancer Center and Johns Hopkins University. We computed the relevant calibration and validation measures, and found that the model performs reasonably well for both datasets. With independent validation, CBCRisk can be used confidently in clinical settings in counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.


Winner of the 2018 Best Dissertation prize in the School of Natural Sciences and Mathematics


Breast—Cancer—Surgery, Mastectomy, Cancer—Diagnosis, Cancer—Risk factors, Breast Cancer Surveillance Consortium, SEER Program (National Cancer Institute (U.S.))