ReviewBiosensors2026
Surface-Enhanced Raman Spectroscopy in Breast Cancer Detection: A Bibliometric Review and Landscape of Global Trends.
Review in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surface-Enhanced Raman Spectroscopy (SERS) has emerged as a powerful analytical platform for breast cancer (BC) detection, offering ultrasensitive, multiplexed, and label-free molecular recognition. However, despite the rapid expansion of the field, no prior study has combined quantitative bibliometric mapping with a cluster-validated technical and translational synthesis, limiting a comprehensive understanding of the field's structure, evolution and clinical projection. In this review, a PRISMA-guided bibliometric analysis was conducted; 199 articles on SERS-based BC detection (2016-2025) were retrieved from SCOPUS, Web of Science and Google Scholar, mapping publication trends, keyword co-occurrence networks (VOSviewer), and Multiple Correspondence Analysis (MCA) with hierarchical clustering on principal components. The results reveal sustained growth in scientific output, led by Asia, North America, and Europe. The 20 most-cited articles (271 citations maximum) showed a shift from substrate optimization toward AI-assisted liquid biopsy platforms. MCA identified five clusters, corroborated by the co-occurrence network: (1) nanostructured platforms for diagnosis; (2) biofunctionalization strategies; (3) liquid biopsy approaches targeting exosomes, circulating tumor cells, and alternative biofluids; (4) diagnostic interpretation based on chemometrics, machine learning (ML) and artificial intelligence (AI); and (5) translational achievements in preclinical and clinical studies. Each cluster was anchored by a technical sub-analysis of its landmark studies, an integration largely absent from prior SERS reviews. This framework clarifies the field's trajectory and positions SERS as a key technology for non-invasive, personalized diagnostics in precision oncology.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.