In September 2022, a relevant search was conducted in the Web of Science Core Collection (WOScc) database, and the time span of the search dates was set from 2012 to September 2022. The search strategy was: ((((((((((((((((TS=(glioblastoma*)) OR TS=(“glioblastoma multiform*”)) OR TS=(“malignant glioma”)) OR TS=(“brain cancer”)) OR TS=(gliosarcoma)) OR TS=(spongioblastoma)) OR TS=(astrocytoma)) OR TS=(astrocytomas)) OR TS=(“astrocytic tumors”)) OR TS=(“astrocytic glioma”)) OR TS=(“astrocyte tumors”)) OR TS=(“astrocytic glioma”)) OR TS=(oligodendroglioma*)) OR TS=(“oligodendroglial tumors”)) OR TS=(GBM)) OR TS=(LGG)) AND (((TS=(Radiomics)) OR TS=(radiogenomics)) OR TS=(“imaging omics”)). Literature inclusion criteria: (1) glioma radiomics studies were the subject; (2) the type of literature included articles and reviews; (3) the language of the literature was English. Literature exclusion criteria: (1) publications were conference abstracts, news, and case studies. (2) The study topic was not glioma. Two reviewers assessed and screened all retrieved publications, and any disagreements were resolved through discussion until consensus was reached. Flow chart of the literature screening process ( Figure 1 ).

CiteSpace software (version 6.1.3) and R software (version 4.1.3) were mainly used for bibliometric and visual analysis of publications related to the field of glioma radiomics research, and Microsoft Excel 2019 was used for data management and trend analysis of publications. CiteSpace software is a tool for visual analysis of literature developed by Prof. Chaomei Chen for analysis of indicators such as country/region, author, institution, journal, reference, and keywords (20). In addition, CiteSpace is used for keyword burst analysis and visualizes it to predict trends in the field. In the visual mapping, the node size represents the frequency of occurrence, with larger nodes representing higher frequency of occurrence. Connections between nodes represent collaboration or co-occurrence relationships. Betweenness centrality is an important parameter in CiteSpace, generally centrality ≥ 0.1 is considered as a more important node, marked by a purple circle in the visual mapping, which mainly measures the value of the node playing a bridging role in the overall network structure. The “bibliometrix” package in the R software was used for the visual analysis of the publications’ source journals.

A total of 587 publications related to the field of glioma radiomics research from 2012 to September 2022 were searched in WOScc, and 484 were finally obtained according to the search criteria, including 393 articles (81.20%) and 91 reviews (18.80%). The number of relevant publications increased year by year, and Figure 2 shows the trend of publications, including the annual and cumulative number of publications. The annual number of relevant publications was within 10 from 2012-2015, and the number of publications steadily increased from 2016-2020, but the annual number of publications was within 100, and the number of publications exceeded 100 in 2021. Further, we conducted a Pearson correlation analysis to test the correlation between publications and citations by Pearson correlation coefficient, and a p-value < 0.05 was considered as a significant correlation. The results showed a high positive correlation between publications and citations (r=0.97, p<0.01).

A total of 484 publications from 40 countries/regions and 258 institutions were obtained. The highest number of publications was from the USA (171, 35.33%) and China (170, 35.12%), followed by SOUTH KOREA (45, 9.30%) and GERMANY (41, 8.47%) ( Table 1 ). Figure 3A shows the visual mapping of collaborative relationships between countries/regions. USA collaborates most closely with other countries/regions, followed by ENGLAND, SPAIN. The top 10 countries/regions with the highest centrality of publications are USA (0.64), followed by ENGLAND (0.24), GERMANY (0.15) and CHINA (0.13). The research institution with the most publications was Chinese Academy of Sciences (Chinese Acad Sci) (24), followed by University of Pennsylvania (Univ Penn) (22) and Fudan University (Fudan Univ) (21). Among the top 10 institutions in terms of publications, University of California, San Francisco (Univ Calif San Francisco) (0.16) had the highest centrality, followed by Stanford University(Stanford Univ)(0.13) and University of Pennsylvania(Univ Penn)(0.12) ( Table 1 ). Figure 3B shows a visual mapping of the research institutions in CiteSpace. A node represents an institution, with larger nodes indicating more publications and higher centrality marked by purple circles.

A total of 354 researchers were involved in the publication of relevant literature. WANG Y (27) had the highest number of publications, followed by LI Y (22), WANG J (20), and among the top 10 authors LIU Z (0.1) had the highest centrality ( Table 2 ). Figure 4A shows the visual mapping of author collaboration network, each circle represents an author, the larger the circle the more publications, the line between the circles represents the connection between authors, and the thicker the line the closer the collaboration. In 1973, Small, an American intelligence scientist, first introduced the concept of co-citation as a method to measure the degree of relationship between documents (21). In 1981, White and Griffith extended the co-citation of literature to the author level and developed the method of author co-citation analysis (ACA) (22). In addition, there are journal co-citations. Co-cited authors are two or more authors who are cited in one or more articles at the same time, and these two or more authors form a co-citation relationship. Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most co-cited authors, followed by LAMBIN P (171), GILLIES RJ (165). Among the top 10 co-cited authors LAMBIN (0.23) had the highest centrality ( Table 2 ). Figure 4B shows a visual mapping between the co-cited authors.

The “bibliometrix” package in R software (version 4.1.3) was used to visually analysis the source journals of the relevant publications. FRONTIERS IN ONCOLOGY (42) was the most published journal, followed by EUROPEAN RADIOLOGY (32) and CANCERS (31). Figure 5A shows a visual mapping of the top 20 academic journals in terms of number of articles published. Among the top 10 academic journals, the highest impact factor is RADIOLOGY (29.146), followed by NEURO-ONCOLOGY (13.029) ( Table S1 ). The most cited of the 516 co-cited journals was NEURO-ONCOLOGY (412), followed by RADIOLOGY (402) and AM J NEURORADIOL (345) ( Table 3 ). Among the top 10 co-cited journals, NEW ENGL J MED (176.709) had the highest impact factor and CLIN CANCER RES (0.27) had the highest centrality, indicating the high status of these journals in this research area. Figure 5B shows the visual mapping of co-cited journals, with larger circles representing higher frequency of co-citations and purple circles indicating higher centrality.

Chen C et al. (23) introduced a new visual analysis method, dual-map overlay, which is used to analyze, compare and contrast the characteristics of publications portfolios. The new method introduces a novel dual-map thematic overlay design on global science. Each publication portfolio can be superimposed as a one-layer of dual-map overlays over 2 related, but different global science maps: one for the citing journal and the other for the cited journal. The color paths in Figure S1 shows the citation relationships, where the three green paths, respectively, indicate that literature published in dentistry, dermatology, surgery journals is frequently cited by Molecular, Biology, Genetics. Literature from Medicine, Biology, clinical journals is often cited in health, nursing, medicine journals. Literature from neurology, sports, ophthalmology journals is often cited in psychology, education, social journals.

Of the 558 co-cited references retrieved, the top 10 most frequently cited references are listed in Table 4 . Figure 6 shows a visual mapping of the co-cited references. Gillies RJ et al. (24) reported the most frequently cited article “Radiomics: Images Are More than Pictures, They Are Data”. Describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of cancer patients. Second, van Griethuysen JJM et al. (25) report on the “Computational Radiomics System to Decode the Radiographic Phenotype”, which reports on their team’s development of PyRadiomics, a flexible open-source platform capable of extracting a large number of engineering features from medical images. PyRadiomics is implemented in Python and can be used standalone or with 3D Slicer. And discusses the workflow and architecture of PyRadiomics and demonstrates its application to lung lesions characterization. In addition, according to the titles of the top 10 co-cited references, it is possible to understand that their topics are mainly about the prediction of survival in glioma patients by radiomics, and the study of molecular subtypes.

Reference burst analysis is beneficial for researchers to understand the literature in a field that has received focused attention during a certain period of time. Based on the strongest citation bursts, Figure S2 shows that the first citation burst started in 2011, with the top 25 references having a citation burst intensity of 4.64-19.18. Among them, the strongest and longest lasting citation was published by Aerts HJ et al. (26) in the journal “Nat Commun”, entitled “Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach”, reported the prognostic power of a large number of radiomic features, many of which had not previously been identified as significant before, in an independent dataset of patients with lung and head and neck cancers. Radiogenomics analysis revealed that prognostic radiomic features capturing intra-tumor heterogeneity were associated with underlying gene expression patterns. These findings may have clinical implications as imaging is routinely used in clinical practice, providing an unprecedented opportunity for low-cost improved decision support for cancer treatment.

Keywords are words or phrases selected to reflect the concept of the subject of a paper for the purpose of literature indexing and searching. The analysis of keywords enables to summarize the research themes in a specific field and explore hot spots and research directions. The top 10 keywords that appear frequently in the studies related to glioma imaging histology displayed in Table S2 include glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), feature (79), central nervous system (66), IDH (57), and radiomics (55), indicating that these areas are the current research hotspots in glioma radiomics. In the keyword co-occurrence virtual mapping ( Figure 7A ), there are 344 nodes and 2211 connected lines. Each node corresponds to a keyword, and the larger the node the more frequently the keyword appears. The number of connecting lines between nodes and the distance between nodes reflect the closeness of the keywords.

The clustering analysis of keywords based on keyword co-occurrence analysis can reflect the hot directions of this research area. Figure 7B shows the visual mapping of keyword clustering, which mainly includes high-grade glioma treatment response monitoring, glioblastoma patient survival prediction, programmed death-ligand, preoperative differentiation, non-invasive genotype prediction, brain tumor, molecular characteristics, machine learning model development, illuminating radiogenomic characteristics, scalar regression analysis, using radiomics, anatomic localization, artificial intelligence, predicting early recurrence, genomic mapping, regional genetic heterogeneity 16 clusters.

Publication volume is a more intuitive reflection of the research fervor and development rate of this research field within a certain period of time, which is important for analysis of research dynamics and prediction of development trends. Figure 2 shows that the annual and cumulative number of publications related to glioma radiomics are both on the rise. 2012-2015, the annual number of related publications was within 10, and the number of publications steadily increased from 2016 to 2020, but the annual number of publications was within 100, and the number of publications exceeded 100 in 2021, indicating that this field is receiving more and more attention from researchers and the research intensity is keeps rising.

The volume and centrality of publications by country/region and institution provides an objective reflection of the level of scientific research in the relevant research area and the value of acting as a bridge in the entire network structure. The highest number of publications is in USA (171, 35.33%) and China (170, 35.12%), with USA working most closely with other countries. In addition, the country/region with the highest centrality among the top 10 countries/regions in terms of publication volume is USA (0.64), followed by ENGLAND (0.24), GERMANY (0.15) and China (0.13), which shows that USA has contributed greatly to the scientific development in this field. The research institution with the highest number of publications is Chinese Acad Sci (24), and among the top 10 institutions in terms of publications, Univ Calif San Francisco (0.16) has the highest centrality. In the future, exchange and cooperation between countries/regions need to be strengthened to promote the development of this research area.

A total of 354 researchers were involved in the publication of related literature. WANG Y (27) had the most publications, followed by LI Y (22), WANG J (20), and among the top 10 authors LIU Z (0.1) had the highest centrality. Wang Y et al. (27) build a radiomic prediction model of LGG-related epilepsy types based on MRI data. The results showed that MRI-based radiomic analysis could predict the type of LGG-related epilepsy and thus provide individualized treatment for patients with LGG-related epilepsy. Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. Among the top 10 co-cited authors LAMBIN (0.23) had the highest centrality. Louis DN et al. reported “2016 World Health Organization classification of central nervous system tumors: summary”, proposed a concept for the structure of CNS tumor diagnosis in the molecular era. Building on the 2016 update of the fourth edition and the work of the CNS Consortium for Molecular and Practical Approaches to Tumor Taxonomy, the 2021 fifth edition introduces significant changes that advance the role of molecular diagnostics in the classification of CNS tumors (2). It is evident that Louis DN and his colleagues have made a great contribution to the continuous updating of the tumor classification of CNS. Kickingereder P et al. (28) developed a framework that relies on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumor burden. The discovery of ANN enables objective and automated assessment of tumor response in neuro-oncology at high throughput and could eventually serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation in clinical trials, as well as application to automated high-throughput imaging biomarker discovery and extension to other diseases. In addition, Kickingereder P et al. (29) explored the correlation between multiparametric, multiregional MRI features and key molecular features in patients with newly diagnosed glioblastoma. Found associations between established MRI features and molecular features, although their strength is not sufficient to generate machine learning classification models for reliable and clinically meaningful prediction of molecular features in patients with glioblastoma. In addition, the role of integrating radiomics into a multi-layered decision-making framework with key molecular and clinical features to improve disease stratification and potentially advance personalized treatment of glioblastoma patients is emphasized (11).

FRONTIERS IN ONCOLOGY (42) was the most published journal, followed by EUROPEAN RADIOLOGY (32) and CANCERS (31). Among the top ten academic journals, RADIOLOGY (29.146) has the highest impact factor, followed by NEURO-ONCOLOGY (13.029). The impact factor of journals is widely accepted and recognized internationally, and is an important indicator for evaluating the academic influence of journals. The most cited of the 516 co-cited journals was NEURO-ONCOLOGY (412), followed by RADIOLOGY (402) and AM J NEURORADIOL (345), and among the top 10 co-cited journals, NEW ENGL J MED (176.709) had the highest impact factor and CLIN CANCER RES (0.27) had the highest centrality. This indicates that the above journals have high influence and status in this research area.

The analysis of co-cited references can reveal the research themes clustered in the field. Information on the top 10 co-cited references was mined, which can be considered as the classic literature in the field. Based on the top 10 co-cited references it can be understood that the research themes in this field are mainly about the prediction of survival of glioma patients by radiomics, and molecular subtypes. The reference with the greatest burst intensity and longest duration was reported by Aerts HJ et al. (26), in an independent dataset of patients with lung and head and neck cancer, a large number of radiogenomic features with prognostic power, many of which had not previously been identified as significant.

Generally, keywords in the literature are the core summary of research content. Most scholars use keywords in the literature of a specific field to analyze the research themes and hotspots in different periods and reveal the trajectory of changes in the research content and research focus of a specific field. We listed the top ten keywords in this research field, and performed keyword clustering analysis based on keyword co-occurrence, which finally formed 16 clusters to identify the current research hotspots and possible future development trends of radiomics in glioma. The main contents are as follows: