Breast cancer screening in Asian populations faces significant challenges due to high breast density prevalence, which reduces mammographic sensitivity. Low-dose computed tomography (LDCT) scans acquired for lung cancer screening capture breast tissue and present an opportunity for opportunistic breast cancer risk assessment. This study develops and evaluates deep learning frameworks using multiple instance learning (MIL) for breast cancer risk stratification from LDCT scans combined with clinical features. Two complementary approaches were developed: an individual breast model using attention mechanisms for slice-level interpretability, and a bilateral model employing global pooling to capture asymmetry patterns. Evaluated on 60 patients, the individual model achieved 72.7% accuracy with AUC 0.85, while the bilateral model achieved 75.0% accuracy with AUC 0.87. Both models successfully stratified patients into medium-risk (BI-RADS 3–4) and high-risk (BI-RADS 5–6) categories, providing interpretable outputs through attention maps and asymmetry visualizations. This represents the first comprehensive, proof-of-concept study demonstrating the feasibility of automated breast cancer risk assessment from opportunistic LDCT imaging.