A model for individualized risk prediction of contralateral breast cancer

Abstract

Purpose Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral breast cancer (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task. Methods We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the Breast Cancer Risk Assessment Tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named Contralateral Breast Cancer Risk (CBCRisk) is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC. Results We identified eight factors to be significantly associated with CBC–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 with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period. Conclusions By providing individualized CBC risk, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

The association of mammographic density with risk of contralateral breast cancer and change in density with treatment in the WECARE study

“…CBC remains a major concern for women diagnosed with a first breast cancer, but risk is variable and effective risk prediction models are needed. Such models would be essential in making decisions about prophylactic contralateral mastectomy [ 10 ]. Given that annual mammography to screen for new breast primaries in the contralateral breast is recommended for women with a first breast cancer diagnosis [ 5 ], it is reasonable to consider MD for inclusion in CBC prediction models.…”

“…There is some evidence showing that higher MD at the time of a diagnosis of ductal carcinoma in situ (DCIS) is associated with increased CBC risk [ 6 – 8 ]. Two large studies including women with both DCIS and invasive cancer observed higher rates of any second breast primary [ 9 ] or CBC [ 10 ] in women with heterogeneously dense or extremely dense Breast Imaging Reporting and Data System (BI-RADS) categories before diagnosis compared to women with almost entirely fatty breasts. Similar results were observed in women with invasive breast cancer only [ 11 ].…”

Validation of a personalized risk prediction model for contralateral breast cancer

“…Table 1 presents the case-control distributions for the two cohorts together with those of BCSC and SEER data on which CBCRisk was built [10]. The proportion of cases in both the cohorts are smaller than those in BCSC and SEER.…”

“…The women in the JH cohort tend to be associated with higher risks of CBC compared to those in the MDA cohort, thereby leading to an increased value of E for that cohort. For example, the “pure DCIS” and “mixed invasive/DCIS” categories of first BC type are associated with higher risks of CBC compared to the “pure invasive” category [10]; and the JH cohort is 60% “pure DCIS” or “mixed invasive/DCIS,” whereas the MDA cohort is 98% “pure invasive.” Likewise, unknown family history of BC is associated with a higher risk compared to no family history and the JH cohort has 96% unknown family history while the MDA cohort has 83% no family history. Moreover, unknown status of anti-estrogen therapy is associated with a higher risk compared to having the therapy; and the JH cohort has 63% with unknown status whereas in the MDA cohort 70% have anti-estrogen therapy.…”

“…For this task, physicians need a statistical model for risk prediction of CBC based on patient’s personal risk factors. We had earlier developed such a model named CBCRisk for predicting absolute risk of CBC for women diagnosed with invasive BC or ductal carcinoma in situ in one breast [10]. The model was built using US population-based datasets from Breast Cancer Surveillance Consortium (BCSC) [11] and Surveillance, Epidemiology, and End Results (SEER) [12].…”