A bibliometric analysis of 2000–2024 on interventional therapy for liver cancer and tumor immune microenvironment

Introduction

Liver cancer is a common malignant tumor and is the most common cause of cancer-related deaths worldwide (1). The incidence of liver cancer is high in developing regions/countries, with major risk factors including chronic hepatitis B or C infection, liver cirrhosis, alcohol abuse, long-term exposure to certain chemicals, familial genetic factors, and obesity (2,3). Hepatitis virus infection is the leading preventable cause of liver cancer (4). The clinical manifestations of early-stage liver cancer are not obvious, and most patients are already at the middle or late stage when they visit the clinic and cannot be treated radically by surgery. Currently, the main treatment modalities for intermediate and advanced liver cancer are atezolizumab + bevacizumab or durvalumab + tremelimumab and interventional therapy (5-7), with the latter being the mainstay of treatment for intermediate and advanced liver cancer. Improved patient prognosis through intervention and its combination therapy (8) and transarterial chemoembolization (TACE) has exhibited survival efficacy in patients with intermediate stage hepatocellular carcinoma (HCC) (9). In addition, the BCLC Prognostic Prediction and Treatment Recommendation Strategy suggests that TACE may be used for follow-up when other recommended therapies are not feasible or when treatment is unsuccessful in the early stages of HCC (10). Although the clinical applicability of TACE will vary slightly depending on the staging system, TACE is still proven to be effective (9); however, its therapeutic efficacy in some patients is still limited. Therefore, the treatment of HCC remains a major challenge worldwide, there is no effective treatment, and the high recurrence rate and poor prognosis of patients with HCC remain intractable problems. In the current era of molecular medicine, molecular drugs and the tumor immune microenvironment (TIME) have been gradually emphasized, and molecular-targeted drugs combined with interventional therapy provide fresh hope for HCC treatment. Their combination and research on interventional therapy modalities for liver cancer may lead to novel breakthroughs in liver cancer treatment.

The TIME consists of different cell populations of the immune system and their interacting immune cells and immunomodulatory factors in the tumor ecology and is known for its critical role in carcinogenesis, cancer progression, and response to therapy. TIME includes different types of immune cells, such as T-lymphocytes, B-lymphocytes, natural killer cells, macrophages, and dendritic cells (11). These cells and their associated cytokines play a key role in recognizing and attacking tumor cells, but in some cases, the tumor cells may escape the immune system (12). Therefore, TIME plays a crucial role in tumor growth, proliferation, and treatment response.

The tumor microenvironment (TME) of HCC is composed of immune cells and stromal cells. These cells interact with each other to form an immunosuppressive microenvironment, which leads to the inhibition of anti-tumor immune responses and contributes to treatment resistance. As the understanding of tumor biology has deepened, the emergence of new systemic therapies—especially immune checkpoint inhibitors (ICIs) and multi-target kinase inhibitors (TKIs)—has fundamentally revolutionized the treatment paradigm and prognosis for patients with advanced HCC (13). The combination of ICIs and vascular endothelial growth factor (VEGF) inhibitors is currently positioned as a first-line therapy for advanced HCC (14). Immunotherapy can improve the TIME in patients with HCC, thereby promoting an immune response. However, the benefits of this treatment are limited and can lead to immune-related adverse events (15). Therefore, the combination of immunotherapy with other therapeutic modalities has become a novel research hotspot (16). Currently, the TIME has not been well studied, and its use in TACE for liver cancer remains unclear.

Bibliometrics is the study of scholarly literature using quantitative methods and statistical principles to analyze, measure, and assess the output and impact of scientific research (17). This discipline helps scholars understand and evaluate the quantity, quality, impact, and trends of research publications. In this study, we used R software, VOSviewer, and CiteSpace to analyze the relevant literature on interventional therapy and TIME in liver cancer from 2000 to 2024, explore the changes and trends in their hotspots, and uncover potential research hotspots for future research. We present this article in accordance with the BIBLIO reporting checklist (available at https://cco.amegroups.com/article/view/10.21037/cco-25-83/rc).

Methods

Data collection

The data used in this study were retrieved and downloaded on December 17, 2024 from Web of Science (WOS). WOS Core Collection was chosen as the data source for this paper. Meanwhile, in order to ensure that the retrieved data were comprehensive and accurate, the indexes were selected as SCI-EXPANDED, SSCI, and the search strategy was a combination of the following keywords and terms: ts=((“liver cancer” OR “hepatocarcinoma” OR “hepatoma” OR “hepatic carcinoma” OR “HCC” OR “hepatocellular carcinoma” OR “liver neoplasms”) AND (“immune microenvironment” OR “tumour microenvironment” OR “tumour immune microenvironment” OR “immune” OR “desmocyte” OR “growth factor” OR “thymus derived cell” OR “cellular infiltration” OR “adipocyte” OR “cytokines” OR “extracellular matrix” OR “macrophage”) AND (“interventional therapy” OR “interventional treatment” OR “interventional” OR “TACE” OR “HAIC” OR “TAI” OR “HAI” OR “PEI” OR “PAI” OR “TARE” OR “DEB-TACE”)). In addition, only English publications between January 1, 2000 and December 18, 2024 were considered, and the document type was selected as Articles.The retrieval results were de-duplicated, and after deleting the documents that were not relevant to the present study by three authors, we observed 277 papers (without duplicates) (Figure 1). The results of this study are summarized in the following table. The retrieved papers were saved in plain text format and exported as complete records along with the references they were cited in.

Figure 1 The research flow chart of this study. WOSCC, Web of Science Core Collection.

Data analysis

In this study, the R Bibliometrix (version 4.3.2), VOSviewer (version 1.6.20), and CiteSpace (version 6.1.R6) were utilized for visualizing and analyzing the data and mapping scientific knowledge. Data extraction and analysis management were carried out to ensure the accuracy and reliability of the data.

The R Bibliometrix calculates descending maps and maps strategic coordinates according to the Law of Bradford and also constructs a global distribution network. Impact factors for journals are derived from the Journal Citation Reports 2024.

In this study, we use VOSviewer to accomplish the following analyses: country and institution analysis, journal and co-citation journal analysis, author and co-citation author analysis, reference analysis, and keyword co-occurrence analysis. In the map generated by VOSviewer, a node represents an item, such as country, institution, author or keyword. The size and color of the nodes represent the number and classification of the items, respectively. The lines between the nodes represent the collaboration or co-citation relationship between these items; the thicker the line, the greater the degree of collaboration or co-citation.

We used CiteSpace for country and institution analysis, journal and co-cited journal analysis, author and co-cited author analysis, reference analysis, clustering of keywords, timeline graphs, and bimap overlays of journals, and analyzed references as well as keywords using citation bursts and keyword bursts.

In addition, we quantitatively analyzed the publications using Excel.

Results

Overall analysis of publications

According to our search, 277 studies concerning the TIME in liver cancer interventions have been published since the year 2000. To analyze the increase in annual publication volume, the entire period was divided into three distinct phases: the first period (2000–2010), the second period (2011–2018), and the third period (2019–2024). As illustrated in Figure 2, the number of papers published during the first period was fewer than five annually, indicating minimal activity in TIME research concerning liver cancer interventions during this decade. The second period witnessed a substantial increase in publications compared to the first, with an average of 8.5 papers per year, marking the initial phase of focused research in TIME in liver cancer interventional therapy. The third period experienced a significant escalation in publication output, averaging 32.3 papers per year. Notably, the number of papers published in 2024 was almost double that of 2022. This surge indicates that research on TIME in liver cancer interventional therapy has definitively entered its development stage, characterized by a continuous annual increase in published literature.

Figure 2 Annual publication volume in the field of tumor immune microenvironment in interventional therapy liver cancer.

Regions/countries and institutions

The publications collected for this study originated from 483 institutions across 21 regions/countries, primarily located in Asia and North America (Figure 3A). China led the publication volume with n=217 studies, followed by the USA (n=20), Germany (n=14), Japan (n=13), and South Korea (n=13). Publications from China, the USA, and Germany accounted for a significant proportion of the total body of work. Of the top 10 highest-ranked regions/countries, two exhibited centralities greater than 0.1: China (0.59) and the USA (0.26). These high centrality scores strongly suggest that these nations play a crucial and dominant role in the international research landscape of this area. We then filtered and visualized the regions/countries, selecting only those with two or more publications, to construct a collaborative network based on the publication volume and collaborative relationships within each nation (Figure 3B,3C). This analysis revealed close collaboration among various regions/countries, notably including China, South Korea, Japan, and the US. Subsequently, institutions were filtered and visualized for analysis, with only those possessing four or more publications selected to construct the collaborative networks (Figure 3D,3E). As Figure 3D illustrates, six distinct clusters were formed through close collaboration among the representative institutions within each cluster. Among these, Fudan University (n=26) and Huazhong University of Science and Technology (n=21) were the two institutions with the highest publication counts, and all of the top five institutions by publication number were located in China. Furthermore, the figure demonstrates that Sun Yat-sen University actively collaborates with Southern Medical University, Fourth Military Medical University, and Southeast University.

Figure 3 The information of regions/countries and institutions in the field of tumor immune microenvironment in interventional therapy of liver cancer. (A) A world map depicting the contribution of each country based on publication counts. (B) The regions/countries cooperation network generated by VOSviewer. (C) The regions/countries cooperation network generated by Citespace. (D) The cooperation network of institutions generated by VOSviewer. (E) The cooperation network of institutions generated by Citespace.

Authors and co-cited authors

For the author analysis, the top ten researchers each contributed more than four publications, with Zheng, Chuansheng having the highest number of publications (n=7) (Table 1). We visualized and analyzed the authors, selecting only those with three or more publications, to construct a collaborative network (Figure 4A,4B). VOSviewer classified these authors into different clusters, revealing very strong interconnections among multiple authors. For example, Zheng, Chuansheng demonstrates close collaboration with Ren, Yanqiao, and Liu, Yiming works closely with Zhang, Hongsen.

Figure 4 The information of authors in the field of tumor immune microenvironment in interventional therapy of liver cancer. (A) The network visualization diagram of authors’ co-authorship analysis generated by VOSviewer. (B) The network visualization diagram of authors’ co-authorship analysis generated by CiteSpace. (C) The network visualization diagram of authors’ co-citation analysis generated by VOSviewer. (D) The network visualization diagram of authors’ co-citation analysis generated by CiteSpace.

Out of the 711 co-cited authors, only one surpassed 100 co-citations, with Llovet receiving the highest count (n=119) (Table 1). The co-cited authors were then filtered, selecting only those with 20 or more co-citations, to plot a collaborative network (Figure 4C,4D). This analysis identifies active collaborations among key co-cited authors, such as Llovet’s collaboration with Kudo and Finn. Notably, Llovet has collaborated with most of the co-cited authors in this network. Llovet, a professor of hepatic oncology at the University of Barcelona, has dedicated his career to investigating the pathogenesis and treatment of liver cancer. This is strongly supported by his having the highest number of citations and collaborating with most of the co-cited authors, suggesting his profound influence and expertise in the study of the TIME within the context of interventional treatment for liver cancer.

Journals and co-cited journals

A total of 131 journals have published papers on TIME in the field of liver cancer interventions. The Journal of Hepatocellular Carcinoma has the highest number of publications (n=10), followed by Frontiers in Immunology (n=9), Oncology Letters (n=9), and World Journal of Gastroenterology (n=8). Among the top 10 journals, Frontiers in Immunology had the highest impact factor (IF =5.7), followed by Cancers (IF =4.55) (Table 2). We then filtered the journals, selecting only those with two or more publications, to map the collaborative networks (Figure 5A). This analysis demonstrates that Frontiers in Immunology maintains an active citation relationship with Frontiers in Oncology and Journal of Hepatocellular Carcinoma. As shown in Table 2, among the top ten co-cited journals, five had more than 200 citations, with Hepatology (n=462) having the highest number, followed by the Journal of Hepatology (n=400). The journal with the highest IF was The Lancet (IF =98.4), followed by The New England Journal of Medicine (IF =96.2). We filtered the co-cited journals, selecting only those with 50 or more co-citations, to map the collaborative network (Figure 5B). As depicted in Figure 5B, an active citation relationship exists among Hepatology, Journal of Hepatology, and Journal of Clinical Oncology. Furthermore, arranging the data in descending order according to Bradford’s law illustrates that Frontiers in Immunology, the Journal of Hepatocellular Carcinoma, and Oncology Letters extensively publish related studies, making them highly valuable references for research in this domain (Figure 5C).

Figure 5 The information of journals in the field of tumor immune microenvironment in interventional therapy of liver cancer. (A) The citation map of journals by VOSviewer. (B) The network visualization diagram of journal co-citation analysis generated by VOSviewer. (C) The citation map of journals by CiteSpace.

Figure 6, the double-map overlay of journals, illustrates the distribution of research topics. Clusters of citing journals are displayed on the left, while clusters of cited journals are on the right. The labels represent the disciplines covered by the journals, and the colored paths indicate citation relationships. The two most prominent paths, highlighted in green, suggest that research published in journals specializing in molecular, biological, and genetic fields, as well as those in health, nursing, and medical fields, is frequently cited by drug, medical, and clinical journals. Additionally, the yellow citation path indicates that research published in molecular, biology, and genetics journals is also frequently cited within drug, medical, and clinical journals.

Figure 6 A dual-map overlap of journals in the field of tumor immune microenvironment in interventional therapy of liver cancer.

Co-cited references with citation bursts

Co-cited references are those that appear together in the reference lists of other documents. The top 10 co-cited references are presented in Table 3, of which five were cited more than 30 times. We then filtered the co-cited references, selecting only those with a co-citation count of 15 or more, to map the collaborative network (Figure 7A,7B).

Figure 7 The information of cited references in the field of tumor immune microenvironment in interventional therapy of liver cancer. (A) The network visualization diagram of co-cited references analysis generated by VOSviewer. (B) The network visualization diagram of co-cited references analysis generated by CiteSpace. (C) CiteSpace visualization map of top 20 references with the strongest citation bursts.

Citation bursts signify references that have been frequently cited by scholars in a particular field over a specific period. In this study, we calculated the top 25 references exhibiting strong citation bursts using CiteSpace (Figure 7C). The blue line indicates the overall timeline, and the red segments superimposed on it denote the detected bursts, including the start year, end year, and duration of the burst. The study with the strongest citation burst was “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries” (18) (strength =6.93), with the burst occurring from 2022 to 2024. The second strongest burst was associated with “Management of hepatocellular carcinoma: an update” (strength =6.38), with its citation burst occurring from 2012 to 2016. Overall, these 25 publications had burst strengths ranging from 1.96–6.93 and an endurance of 1–6 years. These findings identify representative and influential studies concerning liver cancer, interventional therapy, and TIME.

Keywords and keyword bursts

A keyword co-occurrence analysis is useful for rapidly capturing research hotspots within a given field. We filtered keywords with an occurrence frequency of 5 or more, and the resulting keyword co-occurrence network diagram (Figure 8A) demonstrates that a larger node indicates a higher keyword frequency, while a thicker connection between nodes signifies a greater frequency of the two keywords appearing simultaneously. Aside from “hepatocellular carcinoma” and “cancer”, the keywords with the highest frequency in this study included “arterial chemoembolization”, “sorafenib”, and “vascular endothelial growth factor”. This finding indicates the central focus of this research area.

Figure 8 The information of keywords in the field of tumor immune microenvironment in interventional therapy of liver cancer. (A) The network visualization diagram of keywords by VOSviewer. (B) CiteSpace visualization map of top 20 keywords with the strongest citation bursts. (C) CiteSpace visualization map of keywords analysis timeline viewer related in the field of tumor immune microenvironment in interventional therapy of liver cancer. (D) The keywords thematic map generated by R Bibliometrix.

Emergent keyword detection was performed using a keyword co-citation network. The top 25 keywords exhibiting the strongest citation bursts are shown in Figure 8B. Notably, “vascular endothelial growth factor” (strength =7.76) displayed the strongest burst strength, followed by “expression” (strength =4.42), “immune checkpoint inhibitor” (strength =3.8), “safety” (strength =3.73), and “tumor microenvironment” (strength =3.16). Based on the temporal onset of these bursts, we can observe that “hypoxia”, “increased expression”, and “vascular endothelial growth factor” appeared earlier and were the subjects of initial studies. Conversely, “immune checkpoint inhibitors”, “safety”, “tumor microenvironment”, and “immunotherapy” are currently at the forefront of research and are experiencing a substantial surge in interest.

The timeline graph illustrates the dynamic evolutionary path of research hotspots as represented by the keywords, exploring the temporal characteristics of the research areas reflected by the clusters and the rise and fall of popular keyword research. The number of studies within the same cluster highlights the richness and importance of the research results in that cluster area. CiteSpace was used to plot the timeline graphs of the keywords (Figure 8C). The time dimension effectively visualizes the stage-specific hotspots and the future direction of research. In Figure 8C, it may be noted that the keywords included in studies published in 2001 are mainly “hepatocellular carcinoma” and “embolization”, while the keywords appearing in studies from 2020–2023 are predominantly “immune checkpoint inhibitor”, “tumor microenvironment”, and “PD-L1 inhibitors”.

The strategic coordinate chart is a two-dimensional plot constructed with density on the vertical axis and centrality on the horizontal axis. Based on these two metrics, the cartogram is divided into four quadrants: the first quadrant represents motor themes (core themes with high maturity); the second quadrant represents developed and isolated themes (isolated themes with high maturity); the third quadrant represents emerging or disappearing themes (new or soon-to-disappear themes); and the fourth quadrant represents basic and transversal themes (basic themes with low maturity, which may become a research hotspot or a trend for future development).

From the strategic coordinate diagram (Figure 8D), we can observe the following distribution: the first quadrant includes “increased expression”, “hepatitis-c”, and “agent”, among others; the second quadrant contains “hif-1-alpha”, “responses”, and “activated monocytes”, among others. The third quadrant includes “hepatitis-b” and “gene,” among others. The fourth quadrant includes “IL-6”, “efficacy”, “transarterial chemotherapy”, “embolization”, and “vascular endothelial growth factor”, among others.

Discussion

General statement

In this study, we used CiteSpace and VOSviewer to analyze the literature on liver cancer, interventional therapies, and TIME and quantify the findings and progress. A quantitative analysis was performed to obtain basic information on the annual publication volume, country, author, institution, and journal. From 2000 to date, the total number of published studies in this field is 277. The annual publication volume from 2000 to 2010 was less than five, indicating that no research has been conducted on TIME in the field of liver cancer interventional therapy, and there is a lack of relevant research bases. From 2011 to 2018, research in this field gradually developed, with an average of 8.5 annual publications. From 2019 to 2024, the number of annual publications increased significantly, with an average of 32.3 articles published per year. In recent years, the number of related studies in this field has increased notably, indicating that research on TIME in liver cancer interventional therapy is developing rapidly, and related research is attracting increasing scholarly attention and participation.

Statistical analysis of the volume of publications by country and institution makes it possible to identify key regions/countries and research institutions with greater influence in the field and determine their collaborative relationships. China is the leading country in this field, with 90% of the top 10 institutions coming from China. We observed strong associations among China, Korea, Japan, and the US. There are also close ties among research institutions such as Sun Yat-sen University, Southern Medical University, Fourth Military Medical University, and Southeast University. Although there are some collaborative relationships between regions/countries, the extent of cooperation between institutions is not ideal; in the long run, such collaborative relationships will hinder the development of research in this field. Therefore, we strongly recommend that institutions between regions/countries strengthen their ties and widely cooperate and exchange ideas to jointly promote the development of research in the field of TIME in the interventional therapy of liver cancer.

Analyzing the distribution of literature sources helps identify the core journals published in the literature related to interventional therapy and TIME in liver cancer and assists scholars with establishing scientific results. Among the top ten co-cited journals, nine had an IF >10, with LANCET (n=98.4) having the highest IF. This indicates that many high-quality and high-impact journals are focusing on the relevant research. These data will help future researchers to select journals when submitting manuscripts related to interventional therapy and the immune microenvironment of liver cancer. Figure 6 shows that research in molecular, biological, and genetic as well as health, nursing, and medical journals is frequently cited in drug, medical, and clinical journals. This suggests that research on the TIME in the field of liver cancer interventional therapy is mainly focused on basic research and clinical use.

Hot topics and frontiers

The high burst signal of a reference indicates a high intensity and time interval of interest (19). Based on the top 25 references with strong citation outbreaks, we found that the exploration of treatments with better results for HCC is currently a major topic in the field of TIME in liver cancer interventions.

In addition to citation explosions, keywords can help us quickly capture the distribution and evolution of hotspots in the field of TIME in liver cancer interventional therapy. Based on the analysis of keyword timelines, strategic coordinates, and outbreak graphs, we found that TIME in the field of liver cancer interventional therapy is mainly concentrated in the following aspects.

Exploring novel interventional therapies

In recent years, a number of experiments have combined TACE with systemic drugs with antiangiogenic effects (such as sorafenib and olantinib), and although these therapeutic strategies are rational, they have not yielded a clinical benefit when compared with treatment with TACE alone (20,21). Meanwhile, several studies in the field of advanced liver cancer have hypothesized that combination therapy has potential synergistic effects, such as the joint use of VEGF inhibitors and TKIs to enhance the immune system, which theoretically provides the basis for yielding better results of interventional therapy combined with immunotherapy compared with the traditional interventional therapy (22-25).

Influence of TIME on the efficacy of interventional therapy and its combination therapy

As shown in Figure 8A, TME has also become a research hotspot in recent years, and keywords such as hypoxia-inducible factor 1-alpha (HIF-1α), C-reactive protein, and T-cells also appeared in Figure 8D in the first and fourth quadrants, which suggests that the on TIME is a trend and hotspot for future research.

We can flank the TME with some indicators such as the inflammation score, lymphocyte subpopulations, or cytokines. These indicators are considered biomarkers of the relationship between the TME and immune response and are also used to predict recurrence, disease progression, and survival of patients with HCC as well as response to TACE (26,27). Investigating the link between the TME, biomarkers, and response to TACE is critical for identifying early TACE refractoriness or failure and thereby providing more appropriate treatments for patients.

Prediction of TACE response using imaging

In recent years, many studies have also constructed prediction models using imaging results to form array maps combined with clinical indications and all of them have achieved favorable results (28). This predictive model, constructed from imaging results combined with clinical indications, can be used for pre-treatment prediction of TACE refractoriness and has some clinical utility to provide better guidance for decision-making for further TACE treatment in patients with HCC.

Foresight

In recent years, the concept of tertiary-like lymphoid structures (TLS) has been proposed as an aggregate of immune cells in peripheral tissues, including the liver, that form in response to chronic inflammation in the context of autoimmune diseases, infections, and malignancies (29) and as a specialized site for presenting antigens to initiate an immune response (30), which possesses potential as a prognostic indicator and therapeutic biomarker. Meanwhile, the eligibility criteria for TACE have been widely discussed, and further research on the subclassification of BCLC-B could help patients choose more effective treatment options, which may also be a direction for future research in this field.

The surge in research on the TME has led to the critical realization that the solid tumor stroma (the physical barrier) and the suppressive cellular and molecular components within the TME (the chemical barrier) are key determinants of anti-tumor immunity resistance, collectively termed the immunosuppressive barrier. Although TACE provides substantial clinical benefit and remains the primary treatment for intermediate- to advanced-stage HCC, studies indicate that the severe ischemia and hypoxia induced by TACE trigger tumor cells to compensate by secreting high levels of pro-angiogenic factors, such as VEGF (8). VEGF not only promotes neo-angiogenesis, leading to tumor recurrence, but also orchestrates the formation of an immunosuppressive microenvironment. Concurrently, the inflammation resulting from TACE exacerbates the deposition of tumor stroma, thereby reinforcing the physical barrier (31). This potential formation of a TACE-reinforced immunosuppressive barrier is precisely why monotherapy with TACE often yields limited efficacy and high recurrence rates. Consequently, the focus of clinical research is now shifting toward establishing combination therapies that can effectively breach this TACE-potentiated immunosuppressive barrier to achieve superior therapeutic outcomes.

Advantages and limitations

This study has several unique strengths. First, we are the first to use bibliometrics to systematically analyze TIME in the field of liver cancer interventions, which can provide a comprehensive guide for scholars concerned with related research. Second, we simultaneously used three bibliometric tools (VOSviewer, CiteSpace, and R package bibliometrix), which are widely used in the field of bibliometrics; therefore, our data analysis is likely to be objective and credible. Through this bibliometric method, we gained insights into the current research status and developmental trends of TIME in the field of liver cancer interventional therapy using a knowledge map. However, this study has several limitations. First, the primary limitation is the reliance on a single database, the WOS Core Collection. This exclusion of other major databases (e.g., Scopus, PubMed) and regional sources (e.g., CNKI) may introduce bias by underrepresenting clinical data or regionally focused research. Second, the exclusive selection of English-only publications likely overlooks high-impact research from non-English speaking regions (especially East Asia), which biases the collaboration network and institutional analysis towards Western/English-dominant institutions. Third, as an inherent methodological constraint of all bibliometric studies, the results are highly dependent on the initial search strategy and keywords. The rapid evolution of terminology in various fields may have resulted in the omission of some emerging technical terms (such as tertiary lymphoid structures and immune suppression barriers), which may have created thematic or temporal gaps in the analysis. While these limitations prevent a claim of absolute comprehensiveness, they do not invalidate the established major trends and foundational knowledge structure of the field as presented. Future research should leverage multi-database aggregation and non-English literature analysis to provide a more holistic and globally representative perspective.

In conclusion, our study provides a valuable, grounded basis for understanding the research topics, hotspots, and major developmental trajectories in TIME in the field of liver cancer interventions.

Conclusions

Overall, TIME has important research value and application prospects in liver cancer interventional therapy and further experimental studies should be conducted in the future. Notably, TLS with more detailed BCLC staging may be the direction for breakthroughs in liver cancer treatment.

Acknowledgments

The authors thank editage editorial team (cactusglobal.com) for language editing service.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://cco.amegroups.com/article/view/10.21037/cco-25-83/rc

Peer Review File: Available at https://cco.amegroups.com/article/view/10.21037/cco-25-83/prf

Funding: The study was supported by the Jiangxi Clinical Research Center for Cancer (No. 20223BCG74010), The Key Project of Science and Technology Innovation of The Provincial Health Commission in 2023 (No. 2023ZD005), Jiangxi Provincial Key Laboratory of Health Cancer Transfer (Jiangxi Provincial Cancer Hospital), National Natural Science Foundation of China (Nos. 81760731, 82260467), Project of the Jiangxi Provincial Department of Science and Technology (Nos. 20202BABL206093, 20224BAB206061, 20224BAB206118), Science and Technology Plan Project of Jiangxi Provincial Health Commission (No. 202310875), The Open Fund for Scientific Research of Jiangxi Cancer Hospital (Nos. 2021EYS01, 2021K05, 2021J12, KFJJ2023YB20, KFJJ2023YB14), and The Science and Technology Innovation Team Project of Nanchang Medical College (No. NYTD202209).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cco.amegroups.com/article/view/10.21037/cco-25-83/coif). The 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/.

References

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34. [Crossref] [PubMed]
2. Fujiwara N, Friedman SL, Goossens N, et al. Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine. J Hepatol 2018;68:526-49. [Crossref] [PubMed]
3. Massoud O, Charlton M. Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis and Hepatocellular Carcinoma. Clin Liver Dis 2018;22:201-11. [Crossref] [PubMed]
4. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
5. Gordan JD, Kennedy EB, Abou-Alfa GK, et al. Systemic Therapy for Advanced Hepatocellular Carcinoma: ASCO Guideline Update. J Clin Oncol 2024;42:1830-50. [Crossref] [PubMed]
6. Sangro B, Kudo M, Erinjeri JP, et al. Durvalumab with or without bevacizumab with transarterial chemoembolisation in hepatocellular carcinoma (EMERALD-1): a multiregional, randomised, double-blind, placebo-controlled, phase 3 study. Lancet 2025;405:216-32. [Crossref] [PubMed]
7. Yang H, Liu Y, Zhang N, et al. Therapeutic advances in hepatocellular carcinoma: an update from the 2024 ASCO annual meeting. Front Oncol 2024;14:1453412. [Crossref] [PubMed]
8. Llovet JM, De Baere T, Kulik L, et al. Locoregional therapies in the era of molecular and immune treatments for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2021;18:293-313. [Crossref] [PubMed]
9. Forner A, Reig M, Bruix J. Hepatocellular carcinoma. Lancet 2018;391:1301-14. [Crossref] [PubMed]
10. Reig M, Forner A, Rimola J, et al. BCLC strategy for prognosis prediction and treatment recommendation: The 2022 update. J Hepatol 2022;76:681-93. [Crossref] [PubMed]
11. Donne R, Lujambio A. The liver cancer immune microenvironment: Therapeutic implications for hepatocellular carcinoma. Hepatology 2023;77:1773-96. [Crossref] [PubMed]
12. Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity 2019;51:27-41. [Crossref] [PubMed]
13. Rizzo A, Ricci AD. Challenges and Future Trends of Hepatocellular Carcinoma Immunotherapy. Int J Mol Sci 2022;23:11363. [Crossref] [PubMed]
14. Dai S, Chen Y, Cai W, et al. Combination immunotherapy in hepatocellular carcinoma: synergies among immune checkpoints, TKIs, and chemotherapy. J Hematol Oncol 2025;18:85. [Crossref] [PubMed]
15. Vitale E, Rizzo A, Maistrello L, et al. Sex differences in adverse events among cancer patients receiving immune checkpoint inhibitors: the MOUSEION-07 systematic review and meta-analysis. Sci Rep 2024;14:28309. [Crossref] [PubMed]
16. Rizzo A, Ricci AD, Brandi G. Trans-Arterial Chemoembolization Plus Systemic Treatments for Hepatocellular Carcinoma: An Update. J Pers Med 2022;12:1788. [Crossref] [PubMed]
17. Kokol P, Blažun Vošner H, Završnik J. Application of bibliometrics in medicine: a historical bibliometrics analysis. Health Info Libr J 2021;38:125-38. [Crossref] [PubMed]
18. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
19. Kleinberg J. Bursty and Hierarchical Structure in Streams. Data Mining and Knowledge Discovery 2003;7:373-97. [Crossref]
20. Lencioni R, Llovet JM, Han G, et al. Sorafenib or placebo plus TACE with doxorubicin-eluting beads for intermediate stage HCC: The SPACE trial. J Hepatol 2016;64:1090-8. [Crossref] [PubMed]
21. Meyer T, Fox R, Ma YT, et al. Sorafenib in combination with transarterial chemoembolisation in patients with unresectable hepatocellular carcinoma (TACE 2): a randomised placebo-controlled, double-blind, phase 3 trial. Lancet Gastroenterol Hepatol 2017;2:565-75. [Crossref] [PubMed]
22. Kalbasi A, Ribas A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat Rev Immunol 2020;20:25-39. [Crossref] [PubMed]
23. Petrillo M, Patella F, Pesapane F, et al. Hypoxia and tumor angiogenesis in the era of hepatocellular carcinoma transarterial loco-regional treatments. Future Oncol 2018;14:2957-67. [Crossref] [PubMed]
24. Sharma P, Allison JP. The future of immune checkpoint therapy. Science 2015;348:56-61. [Crossref] [PubMed]
25. Yang J, Yan J, Liu B. Targeting VEGF/VEGFR to Modulate Antitumor Immunity. Front Immunol 2018;9:978. [Crossref] [PubMed]
26. McMillan DC. Systemic inflammation, nutritional status and survival in patients with cancer. Curr Opin Clin Nutr Metab Care 2009;12:223-6. [Crossref] [PubMed]
27. Xiao WK, Chen D, Li SQ, et al. Prognostic significance of neutrophil-lymphocyte ratio in hepatocellular carcinoma: a meta-analysis. BMC Cancer 2014;14:117. [Crossref] [PubMed]
28. Niu XK, He XF. Development of a computed tomography-based radiomics nomogram for prediction of transarterial chemoembolization refractoriness in hepatocellular carcinoma. World J Gastroenterol 2021;27:189-207. [Crossref] [PubMed]
29. Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science 2022;375:eabf9419. [Crossref] [PubMed]
30. Sautès-Fridman C, Petitprez F, Calderaro J, et al. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer 2019;19:307-25. [Crossref] [PubMed]
31. Rhee H, Kim H, Park YN. Clinico-Radio-Pathological and Molecular Features of Hepatocellular Carcinomas with Keratin 19 Expression. Liver Cancer 2020;9:663-81. [Crossref] [PubMed]