Optimizing Breast Cancer Screening: Imaging Modalities and Emerging Technologies

Breast cancer screening continues to reduce mortality through earlier detection, yet persistent challenges—including interval cancers, variable interpretive performance, dense breast tissue, inequities in access and outcomes, and uncertainty around risk-tailored strategies—limit the full potential of current programs. At the same time, breast imaging is entering a period of rapid technical and analytical evolution: advances in acquisition hardware, image reconstruction, multimodal workflows, applied artificial intelligence and data science are reshaping how imaging is performed, interpreted, and integrated into clinical decision-making. This Research Topic, “Optimizing Breast Cancer Screening: Imaging Modalities and Emerging Technologies,” brings together work that clarifies how established and novel approaches can improve screening effectiveness, efficiency, and equity across diverse populations and clinical settings.A central aim is to evaluate and refine the role of conventional and advanced imaging modalities across screening paradigms. Topics of interest include full-field digital mammography and digital breast tomosynthesis, ultrasound (handheld and automated), contrast-enhanced mammography, breast MRI (including abbreviated protocols), and emerging or adjunct techniques. We welcome contributions addressing comparative performance (sensitivity, specificity, PPV/NPV), interval cancer reduction, screening frequency and recall optimization, and implementation considerations such as workflow, patient experience, and resource utilization.Equally important is the integration of risk stratification and personalized screening. We encourage studies that connect imaging findings with clinical risk factors, breast density, family history, genetic susceptibility, prior treatment, and longitudinal outcomes to inform individualized screening strategies. Work examining calibration and transportability of risk models, thresholds for supplemental imaging, and consequences of risk-based protocols (including overdiagnosis and false-positive burden) is particularly relevant.The Research Topic also highlights emerging technologies and computational approaches poised to change screening practice. Areas of focus include AI-assisted interpretation, triage and prioritization systems, radiomics and deep learning biomarkers, quality assurance and reader performance tools, and approaches that enhance generalizability across scanners, sites, and populations, including resource-constrained settings. Submissions examining algorithmic bias, fairness, prospective evaluation, and real-world deployment will be especially valued, as will work on privacy-preserving learning, federated strategies, and interoperability within clinical ecosystems. We also welcome work addressing patient compliance with screening recommendations and the affordability and scalability of screening technologies and workflows, particularly in low-income countries.Finally, we seek contributions that strengthen the evidence base for implementation and outcomes, including health services research, cost-effectiveness, program evaluation, and strategies to improve uptake and adherence—particularly in underserved communities. Studies that link technical advances to patient-centered outcomes and measurable reductions in disparities are strongly encouraged.Overall, this collection aims to define practical, evidence-driven pathways to optimize screening—uniting imaging innovation, rigorous evaluation, and equitable implementation to improve early detection and outcomes in breast cancer.Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Sergi Ganau, Michael J Kerin, Angie Fasoula Source: https://www.frontiersin.org/research-topics/79126/optimizing-breast-cancer-screening-imaging-modalities-and-emerging-technologies