CBCRisk-Mastectomy: A Risk Prediction Tool to Aid Contralateral Prophylactic Mastectomy Decision Making

Area of Science:

• Oncology
• Biostatistics
• Public Health

Background:

• Unilateral breast cancer (BC) patients often opt for contralateral prophylactic mastectomy (CPM) despite decreasing contralateral breast cancer (CBC) risk.
• Accurate risk prediction models are crucial for informed decisions regarding CPM.

Purpose of the Study:

• To develop and validate CBCRisk-Mastectomy, a novel risk prediction model specifically for BC patients scheduled for mastectomy.
• To improve decision-making for CPM by providing tailored CBC risk assessments.

Main Methods:

• Utilized data from the Breast Cancer Surveillance Consortium (BCSC) and SEER cancer registry.
• Employed conditional logistic regression models trained on 2,660 BC patients to identify CBC risk predictors.
• Combined relative risks with attributable risks and CBC incidence rates for absolute risk calculation.

Main Results:

• CBCRisk-Mastectomy incorporates 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.
• Achieved a higher predictive accuracy (AUC 0.62) compared to the existing CBCRisk model (AUC 0.58) for 5-year CBC risk.
• Internal validation demonstrated the model's effectiveness.

Conclusions:

• CBCRisk-Mastectomy offers a valuable tool for clinicians counseling BC patients undergoing mastectomy.
• The model facilitates informed decision-making regarding contralateral prophylactic mastectomy (CPM).
• Personalized risk assessment can optimize treatment strategies for breast cancer patients.