Research has shown that neoadjuvant immunotherapy may provide more significant clinical benefits to cancer patients undergoing surgery than adjuvant therapy. This study examines the development of neoadjuvant immunotherapy research using bibliometric analysis. As of 12 February 2023, articles on neoadjuvant immunotherapy in the Web of Science Core Collection were collected. Co-authorship and keyword co-occurrence analyses and visualizations were performed using VOSviewer, while CiteSpace was used to identify bursting keywords and references. The study analyzed a total of 1222 neoadjuvant immunotherapy publications. The top contributors to this field were the United States, China, and Italy, and the journal with the most publications was Frontiers in Oncology. Francesco Montorsi had the highest H-index. The most common keywords were ‘immunotherapy’ and ‘neoadjuvant therapy’. The study conducted a bibliometric analysis of over 20 years of neoadjuvant immunotherapy research, identifying the countries, institutions, authors, journals, and publications involved in this field. The findings provide a comprehensive overview of neoadjuvant immunotherapy research.

Various immunotherapy has been greatly applied to comprehensive treatment of malignant cancer under different degrees of tumor burden. Scientific researchers have gained considerable progress in the relationship between immunotherapy and tumor burden in recent years. This review aimed to explore the prospect and developing trends in the field of tumor burden and immunotherapy from a bibliometric perspective. Articles about tumor burden and immunotherapy were collected from the Web of Science Core Collection (WoSCC) (retrieved on 3 January 2023). The R package ‘Bibliometrix’ analyzed the primary bibliometric features and created a three-filed plot to display the relationship between institutions, countries, and keywords. VOSviewer was used for co-authorship analysis, co-occurrence analysis, and their visualization. And CiteSpace calculated the citation burst references and keywords. A total of 1030 publications were retrieved from 35 years of scientific researches. The United States (US) and China published the most articles. The most productive journals were Cancer Immunology Immunotherapy and Journal for ImmunoTherapy of Cancer. The top one institution of the highest output was University of Texas MD Anderson Cancer Center. The hot keywords of strong citation burst strength in recent years were ‘nivolumab’, ‘tumor microenvironment’, and ‘immune checkpoint inhibitor’. The most popular tumor type is melanoma. This bibliometric analysis mapped a basic knowledge structure. The field of tumor burden and immunotherapy is entering a rapid growing stage and keeping it value for future research.

Background and Objective: The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation. Methods: A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts, review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis. Key Content and Findings: The review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI’s impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain. Conclusions: In conclusion, this review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.

Background: Despite providing clinical benefit, immune checkpoint inhibitors (ICIs) can cause immune-related adverse events (irAEs) in a number of patients. This study explored the development pattern in irAEs research from a bibliometric perspective.Methods: We obtained articles and reviews related to irAEs from the Web of Science Core Collection (WoSCC) (retrieved on September 13, 2022). Using the R package “Bibliometrix”, the main bibliometric features were calculated, and a three-filed plot was generated to show the relationship between authors, institutions, and topics. VOSviewer was used for co-authorship and keyword co-occurrence analysis and visualization. CiteSpace was used to detect burst references and keywords.Results: A total of 3995 publications on irAEs were included. The United States (US), Japan, and China had the highest publications. The Journal for ImmunoTherapy of Cancer had the highest number of publications. In addition to “immune-related adverse events”, “immune checkpoint inhibitors”, “immunotherapy”, and “nivolumab” were the most frequently used keywords.Conclusions: A bibliometric analysis of 17 years of irAEs research was conducted to map a basic knowledge structure including countries, institutions, authors, journals, and publications. The findings provided a comprehensive perspective on the broad future of this research area.

In recent years, liquid biopsy has shown great potential for improving cancer diagnosis and treatment. This study aimed to explore the trends and prospects in liquid biopsy for cancer from a bibliometric perspective. Reviews and articles on liquid biopsy and cancer were collected from the Web of Science Core Collection (WoSCC). Key bibliometric characteristics were analyzed using CiteSpace. Co-occurrence analysis of keywords and co-citation analysis of references was performed. A total of 6331 publications from 11 years of scientific research were retrieved. Ninety-five countries and 7004 institutions in liquid biopsy and cancer contributed. The United States (US) and China published the most articles. The institution with the most published articles was the University of Texas MD Anderson Cancer Center. The most published journals were Cancer and Frontiers in Oncology. "Bettegowda (2014)" was the most cited reference with the highest burst strength in the last decade. Cluster analysis revealed that the recent hot topics were "circulating tumor cells," "cancer," and "exosomes." This bibliometric analysis maps the basic knowledge structure of the field of liquid biopsy for cancer. The field is entering a phase of rapid development. The hot spots identified in this study deserve further investigation.