Innovations in Breast Cancer Imaging: From Early Detection to Precision Care

Breast cancer originates from abnormal cell growth in breast tissue and is the most common cancer among women worldwide. Medical imaging techniques such as mammography and ultrasound play a crucial role in early detection, significantly improving survival rates. Research continues to focus on developing therapies that not only extend life but also improve patients’ quality of life. Image-guided diagnosis and treatment—including minimally invasive biopsies and targeted therapies—are enhancing both diagnostic accuracy and patient comfort. Recent advances in artificial intelligence, molecular imaging, and advanced techniques such as functional MRI, elastography, photoacoustic imaging, and microwave imaging have pushed breast imaging beyond routine structural assessments, establishing it as a powerful tool for biologically informed, patient-specific care. This Research Topic seeks to bring together the latest innovations that connect cutting-edge imaging technologies with meaningful improvements in breast cancer treatment.

Despite major advances in breast cancer treatment, significant challenges remain, requiring novel approaches for the early detection of aggressive tumors, accurate assessment of therapy effectiveness, and broader access to cutting-edge imaging technologies. Traditional imaging methods often fail to capture the complex biology underlying breast cancer, limiting the ability to personalize clinical decisions. This Research Topic aims to highlight innovative imaging techniques and cross-disciplinary strategies that enhance understanding of tumor biology and improve patient care. We welcome submissions covering a broad range of imaging methods, including mammography, ultrasound, MRI, PET, photoacoustic imaging, microwave imaging and elastography, alongside research integrating artificial intelligence, radiomics, and multi-omics data. Additionally, emphasis is placed on image-guided diagnostic and therapeutic approaches that offer accurate, minimally invasive treatments with improved safety and effectiveness. Studies employing imaging endpoints in early-phase clinical trials are also highly encouraged. By fostering collaboration among radiologists, oncologists, engineers, and computer scientists, this collection seeks to accelerate the translation of advanced breast imaging into routine clinical practice—ultimately enhancing early detection, enabling personalized therapies, and expanding global access to state-of-the-art care.

We invite submissions that highlight cutting-edge advances in breast imaging technology and translational science. Areas of interest include, but are not limited to:
-Artificial intelligence, radiomics, and predictive models for diagnosis and treatment planning.
-Functional and molecular imaging techniques that reveal tumor biology and the tumor environment.
-Emerging imaging modalities such as photoacoustic imaging , PET/MRI, microwave imaging, and contrast-enhanced imaging.
-Image-guided interventions, minimally invasive diagnostic systems, and image-based treatment delivery methods.
-Integration of imaging with multiomics data, digital pathology, and findings from early-phase therapeutic studies.
-Strategies to improve access, equity, and the implementation of advanced imaging across diverse healthcare settings.

We welcome contributions in the form of Original Research, Review Articles, Clinical Trials (particularly Phase I/II), Technology Reports, and Case Studies. Submissions should demonstrate scientific rigor, translational value, and the potential to advance clinical practice.

Keywords: Breast cancer; Medical imaging, Artificial intelligence, Image-guided interventions, Molecular imaging, Radiomics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.