Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence

Abstract

With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become increasingly diverse, yet no consensus has been reached. This study presents a bibliometric and latent Dirichlet allocation (LDA) topic modeling analysis of AR research in LT, encompassing 1399 articles. The United States, Zhejiang University, and the University of California, San Francisco emerged as leading contributors, while Levitsky J and Uemoto SJ were key researchers. The most influential journals included the American Journal of Transplantation, Journal of Hepatology, and Transplantation. The analysis reveals a transition from traditional histological assessments to molecular diagnostics, genetic and epigenetic profiling, and noninvasive biomarkers such as donor-derived cell-free DNA (dd-cfDNA) and microRNAs. Advances in immune checkpoint inhibitors (ICIs), cell-based therapies (Tregs, mesenchymal stem cells (MSCs)), AI-guided immunosuppression, and nanoparticle-mediated drug delivery systems reflect a growing emphasis on precision medicine. In addition, recent exploration of microbiome-based therapies and regenerative medicine, including MSCs and their extracellular vesicles, offers promising new avenues for reducing long-term immunosuppressive drug dependency and enhancing graft survival. These developments not only improve early AR detection and personalized treatment but also reduce toxicity, foster immune tolerance, and expand the scope of individualized therapeutic options. Global collaboration, supported by cutting-edge research and AI-driven decision-making, remains essential for refining AR strategies, improving graft survival, and achieving better long-term patient outcomes.

Keywords: acute rejection; bibliometric analysis; latent Dirichlet allocation; liver transplantation.

Graphical Abstract

Figure 1.

Conceptual design of the current study.

Figure 2.

Number of annual publications and citations related to AR after LT research from 2012 to 2021.

Figure 3.

Distribution of countries or regions and country collaboration of AR after LT research. (a) The global distribution of AR after LT research. The size of the circle represents the number of total documents in different countries; the width of the lines between different countries indicate the strength of their cooperation. (b) The total number of citations for publications from different countries. The size of the circle represents total number of citations in each country. The width of the lines between different countries indicates the strength of their cooperation. (c) Spatial distribution map of countries. The size of the node reflects the frequency of publications, and the links indicate collaborative relationships. The color of the nodes and lines represents different years. The outermost purple circle denotes the country with a significant role in the AR after LT field.

Figure 4.

Co-authorship network map of institutions and authors in AR after LT research. (a) Collaborations among the primary institutions in AR after LT. Each dot or circle represents an institution, links denote communication and interactions between institutions, and the width of the lines represents the strength of their cooperation. (b) Cooperative relationships among the top 50 institutions. The size of the circle represents the number of citations of different institutions; the lines between nodes indicate the strength of cooperation, that is, the thicker the lines, the stronger the cooperation. (c) The overlay visualization map of authors co-authorship (top 100). Node size represents the number of articles published by each author. The color of the nodes and lines indicates the average publication year, indicated by the color gradient in the lower right corner. Lines between nodes denote strength of cooperation, with thicker lines indicating stronger collaboration. (d) The overlay visualization map of author co-citation (top 100). The size of a circle is proportional to the total number of citations of the author. The color of the nodes and lines signifies the average publication year, based on the color gradient in the lower right corner. Lines between nodes indicate the strength of the co-citation link.

Figure 5.

Analysis of journals and cited journals. (a) Annual publication volume of the top 20 journals. The size of the circle represents the total number of documents about AR after LT in different journals; the color of the circle represents the citations of the journals. (b) The dual-map overlay of journals. The citing journals were on the left, the cited journals on the right, with colored path representing the citation relationship between them.

Figure 6.

Historical direct citation network in AR after LT research. The links among documents represent the citation relationships in the dataset.

Figure 7.

Co-citation network of references and timeline view. (a) Co-occurrence of references in AR after LT research. The size of each node represents the frequency of cited references, while color indicates the year of the first citation. Clusters of references are identified using the log-likelihood ratio algorithm, with each cluster named based on the title of citing article within it. (b) Timeline view of references. The horizontal line signifies the cluster type. Circular nodes represent cited documents, and links between nodes denote co-citation relationships. Cluster IDs are arranged in sequence on the right side of the figure.

Figure 8.

Overlay map of keywords in AR caused by immunotherapy for LT for liver cancer research. Note. In the visualization, each circle represents an identified keyword, where the size corresponds to the frequency occurrences. The thickness of the link between circles represents the strength of connections among the keywords. Circle colors denote the average year of keyword occurrences, as indicated by the legend in the lower right corner.

Figure 9.

Overlay map of keywords in AR after LT research. Note. In the visualization, each circle represents an identified keyword, where the size corresponds to the frequency occurrences. The thickness of the link between circles represents the strength of connections among the keywords. Circle colors denote the average year of keyword occurrences, as indicated by the legend in the lower right corner.

Figure 10.

Research topics related to AR after LT over time. (a) Word cloud. (b) Accumulated occurrences. (c) Annual occurrences.