Advancing the understanding of tumor microenvironment through medical imaging based on bibliometric analysis

Introduction

The tumor microenvironment (TME) includes tumor cells and their surrounding immune cells, stromal cells, adipocytes, and other cells, as well as non-cellular components such as blood vessels, extracellular matrix, cytokines, proteolytic enzymes, and inflammatory factors (1-3). The TME is a critical factor in cancer progression, influencing various processes such as tumor growth, invasion, metastasis, immune response, and metabolic regulation (4). At present, specific components of TME, such as tumor-associated macrophages, tumor-associated fibroblasts, tumor-associated neutrophils, inflammatory components, etc., have become research hotspots as therapeutic targets for cancer treatment (4-6). Researchers have used various advanced technical means to explore further these components’ role in tumor progression and their implications for therapy. The main methods and techniques used include gene expression profiling, whole-genome sequencing, immunohistochemical analysis, and single-cell analysis (7). However, these techniques are to extract tumor tissue for in vitro detection, which cannot fully characterize the TME, and it is not easy to achieve real-time monitoring (8,9). These limitations highlight the necessity of in vivo monitoring methods in the TME.

As a non-invasive tool, medical imaging can reflect the macroscopic anatomical structure of tumors and provide a detailed analysis of their microscopic heterogeneity and the functional state of their surrounding environment. Consequently, medical imaging has been used to monitor changes in the TME and its associated components (10). Particularly, radiomics and artificial intelligence methodologies have bestowed research endeavors with significant advantages (11). For instance, Sun et al. employed radiomics to assess the tumor immune microenvironment, achieving results of heightened accuracy (12). Several systematic reviews and meta-analyses have been carried out to consolidate advancements in integrating TME research with medical imaging (13-15). Nonetheless, these approaches fall short of intuitively reflecting research trends and identifying emerging hotspots within the domain (16). Therefore, conducting a comprehensive and in-depth quantitative analysis to systematically sort out and summarize the progress to reveal the latest research hotspots is particularly important.

Bibliometric analysis employs mathematical and statistical methods to summarize scientific activity, systematically revealing key issues and developments in areas of interest by analyzing literature databases (17,18). CiteSpace and VOSviewer are visual analysis software that can process large datasets to display the development of research domains, including academic productivity, geographical distribution, and cooperative network structures. They are widely used across various academic and practical fields (17,19). Therefore, this paper aims to systematically analyze the research trends in the intersection of TME through medical imaging from 2008 to 2024 by bibliometric methods. We present this article in accordance with the BIBLIO reporting checklist (20) (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0308/rc).

Methods

Data source and screening

The literature search in this study was conducted from the Web of Science Core Collection (WoSCC). The search strategy was [TS=(“Medical Imag*” OR “Diagnostic Imag*” OR “Quantitative Imag*” OR “Radiological” OR “imagiologic” OR “Radiology” OR “Radiography” OR “Radiomi*” OR “X-ray*” OR “Roentgen Ray*” OR “X Radiation*” OR “CT” OR “MRI” OR “PET” OR “SPECT” OR “Magnetic Resonance Imaging” OR “Computed Tomography” OR “Compute Tomography” OR “Positron Emission Tomography” OR “Ultrasound” OR “Ultrasonography” OR “Echotomography” OR “Echography”) AND TS=(“Tumor Microenvironment*” OR “Cancer Microenvironment*” OR “TME” OR “Tumor Immune Microenvironment*” OR “Cancer Immune Microenvironment*”)] (Appendix 1). Inclusion criteria of literature: (I) clinical application and preclinical study of medical imaging in the diagnosis and monitoring of TME; (II) written in English; (III) articles and reviews. The exclusion criteria were: (I) conference abstracts, newspapers, news, books, and book chapters; (II) technical studies such as synthesis and development of probes.

The search period expired on April 5, 2026, and the full records and cited references of all publications were downloaded in Plain Text format on the same day to avoid the significant bias arising from rapid database renewal. The download included detailed information on abstracts, annual publication and citation counts, countries, institutions, authors, journals, funding agencies, research areas, keywords, and references. The impact factor (IF) of journals was derived from the Journal Citation Reports (JCR) 2025 and journal websites.

As of April 5, 2026, 5,119 articles were retrieved from the WoSCC. We excluded 139 non-English writing and other types of document types. To reduce subjective bias, all retrieved articles were reviewed by two independent readers for titles, abstracts. When there were any disagreements, a consensus was reached by discussing with the third reviewer, who had seven years of radiology experience. After screening according to the inclusion criteria, 3,724 articles were excluded. The literature searching and retrieval process is shown in Figure 1.

Figure 1 Flowchart of the research design.

Statistical analysis

CiteSpace software was produced by Professor Chaomei Chen from Drexel University (21). The full records downloaded from WoSCC were imported into CiteSpace (version 7.0.R0, Advanced) for processing. CiteSpace was used to verify that the type is correct and non-repetitive, and then used for subsequent statistical analysis. This study used CiteSpace to visualize co-occurrence analysis of institutions/keywords, clustering graph of keywords, and timeline diagram of keywords. The centrality reflects the importance of each node in the network (22). CiteSpace was also used to detect the burst of keywords to analyze the development trends and research hotspots. In addition, a dual-map analysis was also performed by CiteSpace, which could show the citation trend and knowledge flow of academic journals (23). During keyword analysis, minimum spanning tree and pruning sliced networks were used for sliced networks, while all other parameters remained at their default settings.

The VOSviewer software was developed by the Centre for Science and Technology Studies at Leiden University (24). VOSviewer (version 1.6.20) was used to generate visualization maps for authors, citations and references. Collectively, in these visualization maps, each node on the map represents an element, and the links between the nodes represent the relationships between these elements. The nodes and lines are colored differently according to different clusters or based on their corresponding average appearing year (25). VOSviewer was also used in this study to analyze the number of publications by each author and the collaborative relationships between countries/regions.

The statistical data from the WoSCC, along with the data analysis results from VOSviewer, were imported into R software (version 4.3.3) for subsequent analysis and visualization. R packages “ggplot2” and packages “circlize” were used to visualize and analyze the publication trend, national publication volume, national cooperation relationship, author publication volume and journal publication data.

Results

Publications and trends

A total of 1,256 valid publications were finally included in this study. Among these publications, 1,019 were articles (81.1%) and 237 were reviews (18.9%). These publications had a total of 23,010 citations, with an average of 22.66 citations per publication. The research design flowchart for the study is depicted in Figure 1.

The annual publications output and citations for 2008 to 2026 were shown in Figure 2A. The overall trend of the histogram indicated an increase year by year, rising from 5 in 2008 to 215 in 2025. The number of publications saw a significant increase in 2020, with an increase of 140% compared with the previous year. The annual number of citations showed a steady growth over the past years.

Figure 2 Publication trends by year, country and author from 2008 to 2026 were analyzed. (A) Global annual publications output and citations trends from 2008 to 2026. Blue bars represent the number of publications per year, orange line represents the number of citations per year. (B) Trends in the number of annual publications in the top 10 countries/regions from 2008 to 2026. Different color bars represent the number of publications per year for various countries/regions. (C) Timeline of top 20 authors. The depth of the dot color represents the times cited, and the size represents the number of publications.

The annual number of publications of the top 10 countries/regions accounts for most of the global publications (Figure 2B). The China had the highest total number of publications (n=446, 35.51%), China’s number of publications had grown rapidly since 2020. Krishna Murali C. (National Institutes of Health-USA) was the author with the largest number of articles, with a total of 16 publications from 2012 to 2022 (Table S1). Among the top 10 authors, Bhujwalla Zaver M. from Johns Hopkins University was the earliest and most enduring contributor, with publications spanning from 2008 to 2024 (Figure 2C). Based on the definition of core authors by Price’s law (M=0.747×Nmax=2.996), those who had published more than 3 articles in this field are the core authors, and so the top 20 authors are all core authors (Table S1) (26).

Contribution and cooperation

Based on the relationship of 57 countries/regions, a chord graph was drawn to analyze the national contribution and cooperation (Figure 3A). There was frequent cooperation between the top 10 countries. China and the USA had the closest cooperative relationship (link strength =39). The USA participated in the largest number of collaborations (Total link strength =201) (Table S2). The institutional co-occurrence map (Figure 3B) revealed that the University of California System had the highest number of publications (n=42), with the highest centrality value of 0.17. Detailed data on the top 20 institutions can be found in Table S3. The co-occurrence map of 65 authors showed Song Patrick N. and Sorace Anna G. have the closest cooperative relationship, which is in the dark blue cluster (Figure 3C). Sorace Anna G., from the dark blue cluster, had a maximum total link strength of 57 and has collaborated with 7 authors.

Figure 3 The contribution and cooperation information pertains to countries/regions, institutions, and authors. (A) The visualization of the collaborative relationships between the 57 countries/regions. Each section of the outer ring represents a country, and the thickness of the connection represents the strength of cooperation between countries. (B) The co-occurrence map of institutions. Node size shows document count, line thickness shows cooperation density, and color depth shows document time. The node centrality of the mechanism is shown by the purple circle outside the node (centrality >0.1). (C) Author co-occurrence map. Circle size shows article count, lines show cooperation, and color shows clusters.

Keywords and topics

The keywords co-occurrence map showed that the following words occurred more frequently: “tumor microenvironment”, “tumors”, “positron emission tomography”, “magnetic resonance imaging”, “breast tumors”, “expression”, “cells”, “therapy”, “in vivo”, and “survival” (Figure 4A). For the centrality of keywords, “breast tumors” was the most central keyword with a centrality of 0.15 (Table S4). The LLR clustering algorithm of CiteSpace, used for the clustering analysis of keywords, yielded a modularity of 0.3485 and a mean silhouette of 0.5121, indicating successful clustering results (Figure 4B). The keywords were analyzed and classified into 12 clusters, as shown in Table S5.

Figure 4 Analysis of all keywords and topics in the field. (A) The co-occurrence map of keywords. (B) The clustering graph of keywords. The smaller the label number, the more keywords are included in the cluster. (C) Diagram of the top 25 keywords with the strongest citation bursts. The blue lines show the timeline and the red bars represent burst periods, including start, end years, and duration of keywords.

The top 25 keywords with the strongest citation bursts are presented in Figure 4C. The keyword of strongest burst intensity was “in vivo” (burst strength of 12.51). Among them, “hepatocellular carcinoma”, “open label”, “artificial intelligence”, “non-small cell lung cancer”, and “recurrence” continuously developed with significant bursts of progress until 2026. In addition, “antibody”, “PET/CT”, and “fibroblast activation protein” were also frontier keywords.

Journal analysis

Cancer Research (IF =16.6, average citations =76.85) was the journal with the highest average number of citations and IF (Figure 5A, Table S6). There were three colored primary citation pathways in Figure 5B. From the Table 1, “molecular, biology, genetics” was the type of journals with the highest citations, consistent with the results of co-occurrence references and emergent references analysis.

Figure 5 Analysis of journal in the study. (A) The average number of citations and impact factors of the top 19 journals (≥12). The Y-axis shows the journal name, and the X-axis indicates the number of publications. The node size represents the journal’s IF [2025], and the color represents the average citation per item for the journal in the field. (B) The dual-map overlay of journals. Each curve connects citing journals (left) to cited journals (right) on the base map. The dual-map overlay illustrated the thread link between cited journals and the citing. The journal publication volume affects ellipse size, and the author count affects width. Trajectory color matches the cited region, and thickness shows citation frequency. IF, impact factor.

Citation and cited reference

Table 2 shows the 20 most highly cited articles, among which the one published in Nature has the highest citation count, with 844 citations. The visualization of the citation network map displays 60 items with 79 links, while the co-citation network map showcases 39 items interconnected by 463 links (Figure 6A,6B). A 2018 paper by Sun et al. had the most citations (Table S7).

Figure 6 Analysis of citation and co-citation in the study. (A) The citation network visualization. (B) The co-citation network visualization. The node’s size represents the number of publications, the thickness of the line represents the link strength, and the node’s color represents different clusters.

Discussion

This study used bibliometric analysis to map the knowledge landscape of the TME through medical imaging in multiple dimensions. The number of publications and citations has increased rapidly in recent years, indicating the considerable potential in this field. In terms of keyword analysis, “radiomics”, “artificial intelligence”, “molecular imaging”, and “immunotherapy” have emerged as key research hotspots. These findings can serve as a background for further investigations into this field.

According to the annual publication results of this study (Figure 2), the total number of publications increased over the study period. The fastest growth occurred in 2020, indicating rapid development in this field. This trend was accompanied by the wide application of radiomics. Notably, a 2018 article by Sun et al. on radiomics analysis of the TME was the most frequently cited in the co-citation analysis, significantly influencing radiomics research within the TME field (27). Later, studies provided insights on the TME components and hypoxia using imaging data (28-31). The number of studies and the corresponding citation counts showed a clear positive correlation, indicating that the rapid growth of publications was consistent with the change in citation trends. With the advancement of imaging techniques and research methods, medical imaging will enable a more comprehensive understanding of the TME.

Different countries/regions, institutions, and related authors have jointly promoted the development of this field. It can be seen from Figure 3A that the dominant force of academic production is mainly from the USA and China, which is consistent with the rapid development of advanced medical imaging technology and the high incidence of tumors in the two countries (32). Furthermore, the USA cooperates with other countries more frequently, which supports its large number of documents (33,34). From the co-occurrence diagram of institutions (Figure 3B), the University of California System and the Chinese Academy of Sciences have the most publications and the highest centrality in this research field, respectively. As for the reason, this is consistent with their high academic influence in the field.

Additionally, most institutional collaborations occur within domestic borders, with few formal cross-national partnerships. On the author side, the timeline (Figure 2C) and co-occurrence map (Figure 3C) identify Krishna Murali C., Sorace Anna G., Zheng-Rong Lu, and Mitchell James B. as high-output contributors, having produced a substantial body of literature. These authors have formed stable cooperative teams focused on medical imaging and TME characterization, as reflected by their frequent co‑publications. A small group of researchers has driven most of the work in this field, frequently publishing in collaboration with others. While such close cooperation correlates with high productivity, it also raises concerns about a potential risk of limited topic diversity. Encouraging greater participation from emerging researchers could help address this issue and foster more innovative contributions.

It can be seen from the journal bubble diagram (Figure 5A) that Cancer Research is the journal with the highest average number of citations and the highest IF in this field of study. According to JCR, statistics show that Cancer Research is the second-most frequently cited cancer journal globally, which is consistent with our statistical results in this field (35). Notably, top-tier journals such as Nature also focused on this field: Reinfeld et al. used positron emission tomography (PET) tracers to characterize cell-specific nutrient partitioning in the TME, validating the focus on PET-based TME metabolic imaging and its immunotherapy translational potential (36). Articles published in high-impact journals are generally considered to provide rigorous and influential evidence. Therefore, researchers in this field should prioritize relevant high-quality publications to enhance the credibility and impact of their work.

The analysis of keyword clustering (Figure 4B) shows that “molecular imaging” is an important method in this field. PET and magnetic resonance imaging (MRI) are commonly used techniques in molecular imaging (37). Early MRI has been used to track related cell molecular targets and has been carried out in therapeutic studies (38). For example, MRI contrast agents have been used to visualize the metabolic characteristics of TME in liver cancer models (9). In recent years, the advantages of PET imaging have also emerged. PET enables tracking of relevant cells in the TME and provides quantitative indicators of targeted drugs in different tumor environments, with typical applications including PET imaging of PD-L1, CD8⁺ T cells, and tumor-associated macrophages to reflect immune activation status (9,30-42). In addition, the combination of PET and magnetic resonance (MR) has shown good prospects in tracking tumor growth and cell infiltration (43). The scope of molecular imaging in this field has broadened from assessing the feasibility of imaging TME components using traditional techniques to exploring advanced technologies like PET/MR for tracking tumor changes (28). This expansion reflects the growing applications and considerable potential of molecular imaging in TME research.

From the burst keyword map (Figure 4C), distinct temporal changes in research keywords were observed. Early keywords like “in vivo” and “magnetic resonance” centered on technical improvements in TME imaging, such as enhanced contrast imaging and targeted molecular probe development to improve detection sensitivity. Researchers have explored various methods to enhance in vivo imaging (44,45). More recently, studies have shifted toward applying TME imaging to disease processes and investigating underlying mechanisms, with key topics including “immunotherapy”, “T cells”, and “PD-L1 expression” (46,47). Among these, “therapy”, especially “immunotherapy”, has become a prominent research direction over the past five years. At present, medical imaging plays a crucial role in evaluating therapeutic targets, characterizing the tumor immune microenvironment, and monitoring immunotherapy processes (10,48). Relevant evidence indicates that imaging techniques can assist tumor immunotherapy by detecting TME dynamics (49,50). However, the operability, safety, and overall effectiveness of TME-based treatments remain insufficiently validated (39,51). While medical imaging offers advantages such as real-time monitoring and prognostic evaluation, its application in therapeutic research still faces challenges, particularly in elucidating the underlying mechanisms of action.

Multimodal imaging may represent a key direction to address current limitations (34,46,52). Keyword analysis further confirms a research transition from technical methodologies to clinical applications. The growing interest in immunotherapy underscores its potential, making it a critical area for further investigation (53). While bibliometric analysis provides valuable insights into the current state of research, it has several limitations. First, this study relies solely on the WoSCC, leading to potential gaps in literature coverage due to database constraints and language or format limitations. Second, variations in statistical methods among different bibliometric tools may introduce subtle discrepancies in analysis results. Additionally, bibliometrics primarily evaluates publication quantity and citation relationships but does not directly assess literature quality. Although efforts were made to address this issue during screening, the absence of standardized quality assessment criteria makes it difficult to entirely exclude lower-quality studies. Future research should focus on expanding the analysis to include a broader and higher-quality dataset, allowing for more comprehensive evaluations across various fields.

Conclusions

This study utilized bibliometric methods to analyze research trends on the TME through medical imaging across different countries/regions, authors, journals, and institutions. It also examined keyword patterns and citation networks to identify key research areas. The findings highlighted major research hotspots and future directions, including molecular imaging, multimodal imaging, radiomics, and immunotherapy. This study may provide valuable insights into the current landscape and development trends in the field, offering a useful reference for future research.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0308/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0308/prf

Funding: This work was supported by Medical and Health Science and Technology Project of Zhejiang Province (grant Nos. 2024KY129, 2025KY093, and 2024KY132), Zhejiang Province Traditional Chinese Medicine Science and Technology Project (grant Nos. 2025ZL302, and 2025ZS012) and Research Project of Zhejiang Chinese Medical University (grant No. 2025JKJNTZ16).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0308/coif). X.L. reports funding from the Research Project of Zhejiang Chinese Medical University (grant No. 2025JKJNTZ16). L.W. reports funding from the Medical and Health Science and Technology Project of Zhejiang Province (grant No. 2024KY132) and the Zhejiang Province Traditional Chinese Medicine Science and Technology Project (grant No. 2025ZS012). C.G. reports funding from the Medical and Health Science and Technology Project of Zhejiang Province (grant Nos. 2024KY129, 2025KY093) and the Zhejiang Province Traditional Chinese Medicine Science and Technology Project (grant No. 2025ZL302). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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