Purpose: Unilateral breast cancer (BC) patients scheduled for mastectomy often choose to undergo contralateral prophylactic mastectomy (CPM), despite substantial declines in contralateral breast cancer (CBC) risk in recent decades. Models predicting absolute risk of future CBC can aid informed decision-making about CPM. CBCRisk is an existing CBC absolute risk prediction model trained on unilateral BC patients regardless of whether they had mastectomy. Here we developed CBCRisk-Mastectomy, tailored specifically to BC patients scheduled for mastectomy and considering CPM. Patients and Methods: We used data on BC patients who underwent mastectomy to treat their first BC from two nationally representative sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER) cancer registry. We imputed missing data in the BCSC sample and used conditional logistic regression models, trained on 2,660 BC patients (665 CBC cases) from BCSC, to identify predictors and estimate relative risks (RRs). These were combined with attributable risks and CBC incidence rates estimated from SEER to obtain absolute risk. Cross-validation was used to internally validate CBCRisk-Mastectomy and compare with CBCRisk. Results: CBCRisk-Mastectomy has nine predictors: first BC type, lobular carcinoma in situ status, estrogen receptor status, tumor stage, breast density, age at BC diagnosis, family history of BC, age at first birth, and body mass index. The areas under the curve and their 95% confidence intervals for 5-year predictions for CBCRisk-Mastectomy and CBCRisk were 0.62 (0.59, 0.65) and 0.58 (0.55, 0.61), respectively. Conclusions: CBCRisk-Mastectomy may aid clinicians in counseling BC patients scheduled for mastectomy, enabling improved decision-making regarding CPM.

The authors have declared no competing interest.

This project was partially funded by the National Cancer Institute at the National Institutes of Health (Grant Number R21CA186086-01).

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IRB of the Office of Research and Innovation at the University of Texas at Dallas gave the ethical approval for this work.

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