Operations research to improve breast cancer screening and diagnosis: Opportunities and challenges

Breast cancer remains a leading cause of cancer mortality worldwide, and early detection through screening is critical for reducing mortality. However, substantial uncertainty remains about optimal initiation and cessation ages, appropriate screening intervals, and the appropriate balance between benefits and harms of screening. This review synthesizes literature from two complementary domains: operations research (OR) models and simulation models in the medical literature. OR studies have focused primarily on optimizing screening timing, frequency, and diagnostic decision-making. These models demonstrate how individual risk factors, adherence patterns, comorbidities, and resource constraints shape optimal policies, and they highlight opportunities for personalization beyond generic guidelines. On the other hand, simulation models have been instrumental in informing national screening guidelines, assessing emerging imaging technologies, and evaluating strategies for specific high-risk populations. By integrating insights from both methodological and policy-oriented modeling, this review identifies major opportunities for advancing breast cancer screening through OR methods, including improved modeling of comorbidities, multi-objective optimization, adherence behavior, equity considerations, multimodal screening technologies, and simulation calibration. Finally, we outline key challenges for future OR work, emphasizing the critical need for robust validation to establish credibility with policymakers, the difficulties in translating complex technical models for clinical adoption, and the extensive data requirements necessary to ensure practical relevance.