Bojing Liu

Breast cancer (BC) remains a global health challenge, with 7.8 million new cases annually. Despite treatment and screening advances that have gradually improved outcomes since the 1970s, many patients still do not survive their dissease, creating an urgent need for precision diagnostics to identify high-risk individuals and predict therapeutic responses. The Consortium for AI in Registry-Based Image Epidemiology Research in Breast Cancer (CARE-B) addresses this by combining registry data, histopathology images, and AI to improve BC characterization.CARE-B will establish an internationally unique large (up to 30,000 patients), multimodal multi-site database integrating clinical data, whole slide images (WSIs), and molecular profiles from breast cancer cohorts in Sweden, Denmark, and Scotland. The consortium will develop scalable AI models for cost-effective precision diagnostics, focusing on deep phenotyping of BC subtypes based on routine H&E stained histopathology slides, characterise intra-tumor heterogeneity (ITH), predicting treatment responses and for prognostic stratification.CARE-B will foster collaboration, support junior researchers, and advance epidemiological research and clinical translation. By leveraging AI and registry data, CARE-B aims to significantly impact BC research and clinical diagnostics, creating a foundation for future advances in precision medicine, while also building a research environment that take health-registry research to the next level.