Bibliometric analysis of breast phantom development and image quality evaluation in breast imaging: Trends,...

Breast cancer is the most common malignancy among women globally, and early detection through mammography is crucial for reducing mortality. High-quality imaging is essential for accurate diagnosis, making breast phantoms key tools for performance evaluation, quality control, and imaging system innovation. Advances in additive manufacturing, multimodal imaging, computational modeling, and artificial intelligence (AI) have transformed phantom design, enabling anatomically realistic and computationally integrated evaluation platforms.

A bibliometric and scientometric analysis was conducted on global research concerning breast phantom development and mammography image quality evaluation between 2015 and 2025. Data were retrieved from major scientific databases and analyzed using bibliometric mapping, network visualization, and thematic interpretation to identify publication trends, leading contributors, hotspots, and gaps.

The review identified strong growth in research output, led by the United States, the Netherlands, China, and Brazil. Hotspots include 3D printing for realistic phantoms, multimodal imaging platforms, optimized acquisition and post-processing algorithms, simulation-based modeling, and AI-driven image quality assessment. Gaps persist in anatomical diversity, standardized evaluation metrics, integration of simulation with physical testing, and equitable participation from low- and middle-income countries.

This study provides the first integrated mapping of breast phantom research, offering a foundation for innovation and global collaboration. Addressing existing gaps will advance imaging system evaluation and clinical relevance.

Developing standardized, anatomically accurate, and accessible phantoms will improve mammography quality control, enhance diagnostic accuracy, and promote equity in breast cancer detection worldwide. Integrating advanced fabrication, AI, and simulation tools can ensure reproducible, high-quality imaging in diverse healthcare settings.