Mapping the research landscape of photoacoustic imaging in breast cancer: a text-based bibliometric analysis

Photoacoustic imaging (PAI) is a noninvasive functional imaging modality capable of assessing tissue oxygenation and other tumor-related biological characteristics, and it has been increasingly explored in both preclinical and clinical breast cancer research. However, bibliometric evidence regarding the developmental trajectory and knowledge structure of this field remains limited. To address this gap, this study reviews PAI literature for breast cancer assessment and summarizes the research status and emerging hotspots.

Publications on PAI in breast cancer were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection, with a data cutoff of December 31, 2025. After screening and exclusion of non-English publications, review articles, meeting abstracts, and other ineligible document types, eligible research articles were included for bibliometric analysis. VOSviewer and the bibliometrix R package were used to analyze publication trends, geographic and institutional distributions, author collaboration, journal characteristics, and keyword evolution.

A total of 214 research articles were included. Annual publication output increased progressively between 2004 and 2025, reaching a peak of 27 articles in 2025. China and the United States were the dominant contributors, jointly accounting for 71% of all publications; China ranked first in publication volume (n=89), whereas the United States showed the highest total citations (n=4,053). Jinan University was the most productive institution (n=17). Journal of Biomedical Optics was the leading journal in publication output (n=18) and also had the highest total citations (n=879). Among authors, Manohar, Srirang was the leading contributor in both publication output (n=20) and total citations (n=1,229). Keyword analysis showed that, beyond core terms, "ultrasound", "molecular imaging", and "photothermal therapy" were major recurring themes, whereas "radiomics", "prediction", and "deep learning" emerged as recent research frontiers.

Research on PAI in breast cancer has expanded steadily over the past two decades, with the field evolving from technology-driven system development toward clinically oriented applications. Current hotspots indicate a growing emphasis on artificial intelligence-assisted diagnosis and predictive modeling. Future work should prioritize multicenter validation, cross-platform standardization, and deeper integration of PAI with multimodal imaging and intelligent analytical methods to facilitate broader clinical translation.