Deep Learning-Based Pretreatment Cardiovascular Risk Stratification in Women with Breast Cancer

Background: Cardiovascular disease is a leading non-cancer cause of morbidity and mortality among breast cancer (BC) survivors. Existing cardiovascular risk tools are not tailored to cancer populations and often rely on cardiology investigations or treatment details unavailable at the initial oncology visit, limiting their use for early referral decisions. Methods: We conducted a registry-based cohort study including 17,051 women diagnosed with BC in stage I-III or ductal carcinoma in situ in the Stockholm-Gotland region (2008-2019). Using only pre-treatment information routinely available to oncologists, such as demographics, cancer characteristics, planned cancer treatment, baseline comorbidities, medications, and healthcare utilization, we trained and validated a deep learning-based competing-risk model to predict 1-year major adverse cardiovascular events (MACE) risk, accounting for non-cardiovascular death as a competing outcome. Model performance was evaluated using a 3-fold CV and time-dependent concordance indices. Fine-Gray subdistribution hazard models were used to aid interpretability. Results: The model demonstrated strong and stable discrimination across validation folds, with a median c-index of 0.84 for 1-year MACE prediction and 0.90 for the competing risk. Key contributors to predictive performance included age at BC diagnosis, prior cardiovascular disease, healthcare utilization patterns, cancer stage, and specific medication profiles. Several predictors with modest marginal hazard ratios in the Fine-Gray model contributed substantially through nonlinear effects and interactions. Conclusions: Our model with a deep learning-based competing-risk structure and using only pre-treatment, oncology-accessible data enables accurate short-term cardiovascular risk stratification in women with BC and may support targeted cardio-oncology referral prior to initiation of systemic therapy.