Topics of interest include, but are not restricted to:

• Image Quality and Radiation Dose in Breast Imaging
  Submissions are invited that investigate methodologies and technologies for optimizing the relationship between image quality and radiation exposure. Research may focus on quantitative image quality metrics, dose-reduction strategies, and approaches that maintain or enhance diagnostic performance while safeguarding patient safety.
• Advances in imaging detectors and imaging systems for breast imaging
  Authors are encouraged to report innovations in detector technologies and breast imaging systems, including photon-counting architectures and advanced system designs for x-ray and other modalities. Evaluations of technical performance and clinical implications are particularly welcome.
• New breast imaging modalities, multimodality breast imaging
  This topic invites contributions on emerging modalities such as optoacoustic and other functional imaging, as well as multimodality approaches integrating x-ray, MRI, ultrasound, and other techniques. Submissions should address technical foundations, clinical utility, and integrative strategies.
• Computer-aided detection, diagnosis, and prognosis
  Authors are encouraged to present research on computational tools supporting detection, diagnosis, and prognostic assessment. Emphasis includes performance evaluation, robustness, generalizability, and workflow integration.
• Risk-stratified breast cancer screening
  Papers exploring personalized screening models informed by imaging-derived risk factors. Topics may include adaptive intervals, modality selection, and validation across diverse populations.
• Breast image perception and human interpretation issues
  Contributions may address perceptual and cognitive factors influencing radiologists’ interpretation accuracy, including diagnostic errors, variability, training interventions, workload effects, and decision-support tools.
• Advances in breast image formation problems
  Research on theoretical and computational challenges including scatter correction, beam hardening, limited-angle artifacts, reconstruction, physics-based models, and hybrid physics–AI methods.
• Image processing, segmentation, and analysis in breast imaging
  Topics include image enhancement, lesion segmentation, tissue characterization, and quantitative analysis using classical or machine learning approaches, with emphasis on validation.
• Radiomics in breast imaging
  Submissions on feature extraction, reproducibility, harmonization, feature reduction, predictive modelling, and translational applications in diagnosis, prognosis, or treatment response.
• Machine learning, AI, and digital twins in breast imaging
  Research on AI for detection, classification, risk estimation, decision support, and patient-specific digital twins for personalized modelling and treatment optimization.
• Interpretability, domain shifts and adaptation, trustworthiness in breast imaging AI
  Work on transparency, robustness, calibration, uncertainty quantification, domain adaptation, bias mitigation, and trustworthy clinical deployment.
• Multi-omics, integration of breast imaging and non-imaging biomarkers
  Research integrating imaging with genomic, proteomic, clinical, or other biomarkers, focusing on multimodal fusion, heterogeneity challenges, and personalized medicine.
• Anthropomorphic phantoms
  Developments in realistic breast phantoms (digital or physical) for QA, calibration, optimization, lesion modelling, functional phantoms, and experimental evaluation.
• Virtual clinical trials in breast imaging, simulation of breast imaging technology
  Computational platforms simulating anatomy, imaging physics, acquisition pipelines, and interpretation for technology development, evaluation, and regulatory science.
• Breast imaging informatics
  Informatics systems for imaging data management, interoperability, analytics, DICOM standards, PACS integration, data governance, and AI infrastructure.