Worldwide research landscape of radiomics in lung cancer: A scientometric study

Worldwide research landscape of radiomics in lung cancer: A scientometric study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Worldwide research landscape of radiomics in lung cancer: A scientometric study Jiangbo He, Chaoyuan Liu, Fang Ma, Yiguang Zhou, Xianling Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6409960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The main cause of cancer-related deaths around the world is lung cancer. Therefore, the diagnosis and treatment of lung cancer make up the majority of clinical research focused on cancer. In recent years, there have been significant advancements in the application of radiomics in lung cancer. However, there are no studies on global research trends in the application of radiomics in lung cancer. To address this gap, this study investigates the current state of research and key application areas of radiomics in lung cancer, while predicting future research directions. Methods On 21 October, 2024, we identified 2057 papers on the application of radiomics in lung cancer from the Web of Science database Core Collection database. In order to examine and graph trends and proportions of publications by country, GraphPad Prism software was used. CiteSpace and VOSviewer were used to visualize and analyze the papers published from 1 January 2010 to 21 October 2024. Results The collection included 2057 papers published from 2010 to 2024, of which most were articles (1734, 84.30%) and a few were reviews (323, 15.70%), contributed by 9539 authors from 61 countries/regions. There was an upward trend in both the number of publications per year and the total number of citations. China, accounting for 47.79% with 983 papers, and the USA, accounting for 25.86% with 532 papers, have made notable contributions in this domain. General Electric was the most productive institution (n = 86). Lambin (n = 919 citations) was the most co-cited author, whereas Aerts, Hugo J. W. L., was placed first among the top ten authors. The most published journal was Frontiers in Oncology (178 publications; IF 2023, 3.5; Q2). It is important for different countries and institutions to strengthen their cooperation in the future. Radiomics, features, images, CT, and survival were the most commonly used keywords. The analysis of references and keywords shows that the research hotspot of lung cancer radiomics is more inclined towards clinical applications. In the future, radiomics was mainly used for the classification, diagnosis, detection, and prediction of lung cancer, especially in immunotherapy. Conclusion In summary, the bibliometric analysis comprehensively and quantitatively presents the research status, research hotspots, and development trends of radiomics applied in lung cancer. The application of radiomics to lung cancer is a highly promising research area based on our results. Multicenter studies are a trend in the development of lung cancer radiomics, and we advocate strengthening cooperation between countries/regions, institutions, and authors to break down academic barriers. The research hotspot of lung cancer radiomics is more inclined towards clinical applications, including screening, diagnosis, and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend. Furthermore, deep learning can provide strong technical support for radiomics. Multimodal learning for information fusion is another crucial development trend; we should pay more attention to multi-omics integration in the future. Radiomics Lung cancer Deep learning Bibliometric analysis VOSviewer CiteSpace Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Lung cancer ranks among the most prevalent cancers and is responsible for the highest number of cancer-related deaths globally. The Global Cancer Statistics 2022 report indicates that lung cancer was the most prevalent cancer in the United States in 2022, with nearly 2.5 million new cases, accounting for one-eighth of all new cancer cases globally ( 1 ). Additionally, the most common cause of cancer-related death is lung cancer, which accounts for 18.7% of cancer-related deaths ( 1 ). In both men and women, lung cancer ranks first in terms of incidence and mortality rates, according to the National Cancer Center of China. It is common for lung cancer to show no symptoms when it is in its early stages, leading to progression or tumor metastasis by the time of diagnosis, which contributes to its poor mean 5-year survival rate of 20–30% ( 2 ). In order to improve the survival rate of lung cancer (LC) patients, timely diagnosis and accurate prediction are crucial. The earliest application of radiomics can be traced back to a study aimed to explore the image-based biomarkers for solid mass pulmonary tumors ( 3 ). And Lambin et al ( 4 ) first introduced the concept of radiomics in 2012. Large amounts of medical data are often difficult to filter and interpret, and the rapid development of artificial intelligence in recent years has provided unique opportunities for the processing of medical data. Radiomics is the bridge between medical imaging and medicine, which extracts features related to tumor and the microenvironment from medical imaging data, ultimately transformed into exploitable high-dimensional data ( 5 ). Ralf Floca et al ( 6 ) have recently summarized the previous research of radiomics and proposed a consensus radiomics workflow definition proposal with high agreement comprising seven phases: study design, data acquisition, data management, image processing, feature extraction, modeling and reporting. Bibliometric analysis employs quantitative mathematical and statistical techniques to study the yearly distribution across various countries, institutions, journals, authors, keywords, and references. Numerous fields have utilized bibliometric analysis. To enhance the execution of radiomics research on lung cancer, research status, hotspots, research frontiers, and research trends were analyzed through a bibliometric study using the Web of Science Core Collection (WOSCC) database, which will provide researchers and institutions with directions for subsequent research and references for the application of radiomics in clinical decision-making for LC patients. 2 Methods 2.1 Database and systematic search strategy From the Web of Science Core Collection (WoSCC) database, we obtained the papers related to radiomics in lung cancer. There are other databases available, such as PubMed, Scopus, and Google Scholar. However, the WoSCC database offers several advantages over the others. The Web of Science Core Collection (WoSCC) database is the world's largest scientific citation database, it covers multiple disciplines and can simultaneously retrieve numerous sub-disciplines, which is very helpful for precise searches. The WoSCC database provides detailed retrieval and paper analysis features, including citation counts, citation reports, and H-index, and is generally considered to have more authoritative data than other sources. In addition, the WoSCC database is frequently used in bibliometric analysis and is regarded as one of the most prominent databases. As a result, the WoSCC database was chosen for our literature search. To reduce biases from database updates, the literature search was performed in a single day on 21 October 2024. The search formula is ((((((((((((((((TS=(Lung Neoplasms)) OR TS=(Pulmonary Neoplasms)) OR TS=(Neoplasms, Lung)) OR TS=(Lung Neoplasm)) OR TS=(Neoplasm, Lung)) OR TS=(Neoplasms, Pulmonary)) OR TS=(Neoplasm, Pulmonary)) OR TS=(Pulmonary Neoplasm)) OR TS=(Lung Cancer)) OR TS=(Cancer, Lung)) OR TS=(Cancers, Lung)) OR TS=(Lung Cancers)) OR TS=(Pulmonary Cancer)) OR TS=(Cancer, Pulmonary)) OR TS=(Pulmonary Cancers)) OR TS=(Cancer of the Lung)) OR TS=(Cancer of Lung) AND ((TS=(Radiomics)) OR TS=(Radiomic*)) OR TS=(imageomics). 2.2 Analysis tools The publication trends and proportions of national and international publications were analyzed and plotted using GraphPad Prism version 8.0.2. In addition, in order to analyze the data and visualize the science mapping, CiteSpace (6.2.4R (64 bit) Advanced Edition) and VOSviewer (1.6.18 Edition) were used. In 2009, Waltman et al developed VOSviewer, a free Java-based program, that allowed researchers to create and view bibliometric maps, especially large bibliometric maps easily readable. Professor Chen created the CiteSpace software, and our research used this tool to analyze the dual-map overlays of the journals, the timeline view of references, keyword co-occurrences, and clustering of keywords. 3 Results 3.1 Database search In our study, we retrieved 2057 publications between 1 January 2010 and 21 October 2024, including 1734 articles (84.30%) and 323 reviews (15.70%). 9539 authors contributed to these publications from 2263 institutions across 61 countries. The 2057 relevant documents were exported as plain text files and Excel tables for bibliometric evaluation and visual analysis. 3.2 The general global trends The first article in this field was published in 2010, and Fig. 2 displayed the volume and trends of publications from 2010 to 2024. As we mentioned before, radiomics emerged as a formal concept in 2012. Before 2015, radiomics in lung cancer had not received much attention. From 2010 to 2024, it has shown a continuous growth trend. In general, the development stage can be divided into three stages: the early stage (2010–2014), the growth stage (2015–2019), and the prosperity stage (2020–2024). The increasing trend in the number of publications reflects research on lung cancer using radiomics is developing and gaining interest. We predict that the annual publication volume in 2024 will continue to grow and exceed the annual publication volume in 2023. With the maturity of artificial intelligence, the application of radiomics in lung cancer will become increasingly widespread. 3.3 Analysis of countries/regions and institutions A total of 2,263 institutions across 61 nations/regions participated in the study of radiomics in lung cancer, according to the literature that was retrieved. Figure 3a displayed the top 10 countries published the most publications per year between 2010 and 2024. Almost half of the 983 publications were published by China (47.79%), making it the top contributor. A total of 983 publications were published by China, followed by 532 publications from the USA, 189 publications from Italy, 133 publications from the Netherlands, and 107 publications from France. The United States had the most citations overall (42203), followed by Netherlands (24473), China (15891), Canada (11329) and Italy (8235). The United States had 42,203 citations (Table 1 ) among the top 10 published nations, which is much more than any other nation/region, and it ranks third overall with a citation-to-publication ratio of 79.33, indicating that the quality of its published papers is generally high. China's publication volume (983 publications) ranks first among countries, while its citation count (15,891 times) ranks third, and it had the lowest citation-to-publication ratio (16.17). This result shows that we need to pay more attention to the cutting-edge development in this field, publish more high-quality articles, and further corroborate the importance of our study. The networks of country cooperation are illustrated in Fig. 3c. China and the United States, the world's two largest producers, have extensive cooperative relationships. China's cooperation with Japan, the Netherlands and South Korea is close, while a closer relationship exists between the United States and Italy, Canada, and the United Kingdom. The development of radiomics in lung cancer was significantly influenced by the United States, which had the highest centrality (0.41). We can also find that international cooperation can promote the innovation and development of the application of radiomics in lung cancer. Table 1 Table of country published literature Rank Country/region Article counts centrality Percentage (%) Citation Citation per publication 1 CHINA 983 0.15 47.79% 15891 16.17 2 USA 532 0.41 25.86% 42203 79.33 3 ITALY 189 0.25 9.19% 8235 43.57 4 NETHERLANDS 133 0.09 6.47% 24473 184.01 5 FRANCE 107 0.02 5.20% 6006 56.13 6 GERMANY 98 0.09 4.76% 4399 44.89 7 CANADA 94 0.11 4.57% 11329 120.52 8 ENGLAND 87 0.24 4.23% 6283 72.22 9 SOUTH KOREA 77 0.01 3.74% 1907 24.77 10 JAPAN 72 0.01 3.50% 1277 17.74 Table 2 lists the top ten institutions by publication volume that published relevant papers on radiomics in lung cancer. A total of 2263 institutions published relevant papers on radiomics in lung cancer in the past few years. Among them, five were from the United States, four were from China, and the remaining one was from France. General Electric merged as the most prolific (86 papers, 988 citations, and 11.49 citations per paper), followed by Maastricht University (81 papers, 22573 citations, 278.68 per paper), Fudan University (78 papers, 977 citations, 12.53 per paper), Shandong First Medical University (77 papers, 961 citations, 12.48 per paper) and Harvard University (75 papers, 24172 citations, 322.29 per paper). The networks of institutional cooperation are illustrated in Fig. 3d. Through the visualization analysis, we found that domestic and foreign institutions preferred to cooperate with their own domestic units. In order to break down academic barriers, it is crucial to strengthen cooperation between domestic and international institutions. Table 2 Table of Institutional Published Literature Rank Institution Country Number of studies Total citations Average citation 1 General Electric USA 86 988 11.49 2 Maastricht University USA 81 22573 278.68 3 Fudan University China 78 977 12.53 4 Shandong First Medical University & Shandong Academy of Medical Sciences China 77 961 12.48 5 Harvard University USA 75 24172 322.29 6 Chinese Academy of Sciences China 73 2607 35.71 7 H Lee Moffitt Cancer Center & Research Institute USA 64 16966 265.09 8 University of Texas System USA 62 4005 64.60 9 Institut National de la Sante et de la Recherche Médicale (Inserm) France 59 4732 80.20 10 Shanghai Jiao Tong University China 58 1070 18.45 3.4 Analysis of journals and co-cited journals The top ten journals by publication volume are listed in Table 3 . The most publications were published by Frontiers in Oncology (178 papers, IF: 3.5), followed by scientific reports(87 papers, IF: 3.8), cancers༈85 papers, IF: 4.5༉, european radiology (65 papers, IF: 4.7༉. The European Journal of Nuclear Medicine and Molecular Imaging, with an impact factor of 8.6, is the leading journal among the top ten most prolific in its field. Among the leading ten journals by number of publications, Frontiers in Oncology and Clinical radiology were classified in JCR Q2, while others were all classified in JCR Q1, which represents a promising development in lung cancer radiomics. The density map of journal publications is illustrated in Fig. 4a. A high volume of publications reflects the journal's interest and activity in radiomics in lung cancer, indicating the research frontiers and trends in this field. Through the number of co-citations in this field, we can see how influential journals are in this field. The top ten journals by number of co-citations are listed in Table 4 . The Co-citation network map of journals is illustrated in Fig. 4b. Radiology (citations: 1626, IF: 12.1) was the journal with the most co-citations, followed by SCI REP-UK (citations: 1261, IF: 3.8), and EUR RADIOL (citations: 1257, IF: 4.7). J THORAC ONCOL, among the top 10 journals with the most joint citations, was cited 938 times and had the highest impact factor (IF 21.0). Among the leading ten journals by Co-citation, Frontiers in Oncology was classified in JCR Q2, while others were all classified in JCR Q1, indicating that research of radiomics in lung cancer can be recognized by outstanding journals. Table 3 Table of Journal Publications Rank Journal Article counts Percentage IF (2024) Quartile in category 1 frontiers in oncology 178 8.65% 3.5 Q2 2 scientific reports 87 4.23% 3.8 Q1 3 cancers 85 4.13% 4.5 Q1 4 european radiology 65 3.16% 4.7 Q1 5 medical physics 65 3.16% 3.2 Q1 6 academic radiology 42 2.04% 3.8 Q1 7 physics in medicine and biology 42 2.04% 3.3 Q1 8 european journal of nuclear medicine and molecular imaging 35 1.70% 8.6 Q1 9 translational lung cancer research 35 1.70% 4.0 Q1 10 clinical radiology 34 1.65% 2.1 Q2 Table 4 Co-citation table of journals Rank Cited Journal Co-Citation IF(2024) Quartile in category 1 RADIOLOGY 1626 12.1 Q1 2 SCI REP-UK 1261 3.8 Q1 3 EUR RADIOL 1257 4.7 Q1 4 PLOS ONE 989 2.9 Q1 5 FRONT ONCOL 960 3.5 Q2 6 J THORAC ONCOL 938 21.0 Q1 7 MED PHYS 931 3.2 Q1 8 LUNG CANCER 904 4.5 Q1 9 NAT COMMUN 862 14.7 Q1 10 EUR J CANCER 855 7.6 Q1 The dual map of journal is illustrated in Fig. 4e, through which we can visualize theme distribution of academic publications. Colorful trajectories represent citation connections, with the journal on the left represents early research publications and the journal on the right corresponds to more recent publications. According to the graphical results, the research published in journals in the fields of medicine/medical/clinical provided a theoretical foundation for subsequent research. The clusters on the right illustrate the development and current status of radiomics applications in cancer. Important clusters include "Health/Nursing/Medicine," which indicates that the current focus of lung cancer radiomics is on clinical applications and healthcare research, and "Molecular/Biology/Genetics," which indicates that the study of genetically related molecular issues through lung cancer radiomics is a current research hotspot. 3.5 Analysis of authors and co-cited authors The leading ten journals by the number of publications and the top 10 authors by citation were listed in Table 5 . The top 10 authors by the number of publications have published a total of 264 papers, accounting for 12.83% of all papers in the field. It can be intuitively seen that Aerts, Hugo j. W. L.c. has authored the most papers, totaling 41, published from 2010 to 2024, followed by Gillies, Robert j. (36 papers), Lambin, Philippe (36 papers), Schabath, Matthew b. (29 papers), Lee, ho Yun (23 papers). Subsequently, we conducted a visual assessment of the author's cooperative relationships. Figure 4c depicted the network of author collaboration, revealing the collaborative connections between these authors. In Fig. 4d, the authors' co-citation network is depicted. Lambin P's papers have the most citations, totaling 919, and the highest average of 25.53 citations, indicating he is a pioneer in lung cancer radiomics. Table 5 Author's publications and co-citation table Rank Author Count Rank Co-cited author Citation 1 Aerts, Hugo j. W. L. 41 1 Lambin P 919 2 Gillies, Robert j. 36 2 Aerts Hjwl 729 3 Lambin, Philippe 36 3 Gillies Rj 717 4 Schabath, Matthew b. 29 4 Van Griethuysenjjm 490 5 Lee, ho Yun 23 5 Zwanenburg a 402 6 Tian, Jie 22 6 Parmar C 374 7 Dekker, Andre 20 7 Coroller Tp 322 8 Schwartz, Lawrence h. 20 8 Kumar V 311 9 Madabhushi, Anant 19 9 LiuY 300 10 Leijenaar, ralph t. H. 18 10 Siegel Rl 259 Table 6 Co-citation table of literature Rank Title Journal author(s) Total citations 1 Radiomics: Images Are More than Pictures, They Are Data RADIOLOGY Gillies RJ 393 2 The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping RADIOLOGY Zwanenburg A 318 3 Computational Radiomics System to Decode the Radiographic Phenotype CANCER RESEARCH van Griethuysen JJM 292 4 Radiomics: the bridge between medical imaging and personalized medicine NATURE REVIEWS CLINICAL ONCOLOGY Lambin P 290 5 Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach NATURE COMMUNICATIONS Aerts HJWL 227 6 Radiomics and radiogenomics in lung cancer: A review for the clinician LUNG CANCER Thawani R 147 7 A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study LANCET ONCOLOGY Sun R 145 8 CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma RADIOTHERAPY AND ONCOLOGY Coroller TP 128 9 Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers ANNALS OF ONCOLOGY Trebeschi S 116 10 Repeatability and Reproducibility of Radiomic Features: A Systematic Review INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Traverso A 107 3.6 Analysis of co-cited references With a timeslice of one year, the data covers the period from 2010 to 2024, the network of co-cited references depicted in Fig. 5a comprises 981 nodes and 5,707 links. Table 4 lists the ten references with the most co-citations. The most frequently co-cited reference from Philippe Lambin, titled 'Radiomics: Images Are More than Pictures, They Are Data' was published in RADIOLOGY and had 393 co-citations. Among these references, the research of lung cancer radiomics was more inclined towards clinical applications, including screening, diagnosis and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend. We performed co-citation reference and temporal clustering analyses, as illustrated in Figs. 7b and 7c. We found that imaging (cluster 8) is an early research hotspot. Quantitative imaging (cluster 0) and cone-beam CT (cluster 10) are mid-term research hotspots. Epidermal growth factor receptor, PET, immunotherapy, radiation pneumonitis, reproducibility, deep learning, solitary pulmonary nodule, and brain metastasis are hot topics and trends in this field. It is especially reflected in three aspects of clinical applications: screening (solitary pulmonary nodule), diagnosis (Epidermal growth factor receptor) and prediction of clinical outcome (immunotherapy). 3.7 Analysis of keywords Keywords highlight the article's theme, and their analysis reveals research hotspots and field development trends. Table 7 lists the 10 keywords that appear most frequently. In VOSviewer, 'radiomics' (1320) emerges as the most frequently occurring keyword based on keyword co-occurrence analysis, followed by features (396), images (342), ct (332), and survival (318), which is shown in Fig. 6a and Fig. 6b. Then we removed the redundant keywords and developed a network comprising 173 keywords, each appearing a minimum of 18 times, and obtained different clusters. The red cluster, labeled as Cluster 1, includes 44 keywords such as ct, challenges, delineation, fdg-pet, image, metabolism, impact, parameters, prognostic value, radiomic feature, repeatability, stability, tumor volume, textural feature, robustness, reproducibility. Cluster 2 (green) has 42 keywords, including radiomics, metastasis, radiogenomics, biomarkers, blockade, chemotherapy, EGFR, gefitinib, imaging phenotype, immunotherapy, intratumor heterogeneity, machine learning, MRI, multicenter, nivolumab, open label, PD-L1, precision medicine, resistance, somatic mutations. Cluster 3(blue)has 34 keywords, including classification, computed tomography, nomogram, signature, body radiation therapy, differentiation, ground glass nodules, international association, lesions, lobectomy, pattern, recurrence, subtype, system, surgery. Cluster 4 (yellow) has 30 keywords, including artificial intelligence, computer-aided detection, convolutional neural network, CT image, diagnosis, epidemiology, deep learning, guideline, malignancy, management, model, performance, mortality, prediction, risk, size, validation. Cluster 5 (purple) has 23 keywords, including chemoradiotherapy, dosiomics, feature selection, outcome, pathological responses, phenotype, pneumonitis, quantitative imaging, risk factors, texture, toxicity, volume. Table 7 High Frequency Keyword Table Rank Keyword Counts Rank Keyword Counts 1 radiomics 1320 11 heterogeneity 188 2 features 395 12 deep learning 185 3 images 342 13 diagnosis 179 4 ct 332 14 radiotherapy 150 5 survival 318 15 computed-tomography 131 6 machine learning 273 16 signature 126 7 classification 258 17 pet 125 8 texture analysis 235 18 pulmonary nodules 122 9 prediction 224 19 immunotherapy 118 10 computed tomography 216 20 artificial intelligence 114 We used CiteSpace to conduct keywords clustering and temporal clustering analysis shown in Fig. 6c and Fig. 6d. We found that deep learning, immunotherapy, lung adenocarcinoma, stage I, radiation pneumonitis, lymph nodes, and tumor microenvironment are current research hotspots. 3.8 Citation bursts The references that saw a sudden increase in citations were displayed in Fig. 5d. We have identified the top 50 most consistent citation bursts in radiomics related to lung cancer through CiteSpace. The 50 references, published from 2010 to 2024, demonstrate frequent citations in recent years. Furthermore, currently six of these papers are experiencing a period of high citation, indicating that applying radiomics to lung cancer studies will continue to receive attention in the future. Figure 6e displayed the top 50 keywords with the most significant citation bursts. Technical keywords such as 'texture analysis', 'fdg pet' and 'image features' were the first keyword burst identified in 2012. Subsequently, studies on tumor treatment methods like 'radiotherapy' or 'chemoradiotherapy' gained popularity. The most recent surge in keywords started in 2022 and has continued to the present. The primary keywords included: “neoadjuvant chemotherapy”, “ground-glass nodules”, “radiomics signature” and “surgery”, suggesting that these research themes might become future directions of research in the next few years. 4 Discussion 4.1 Analysis of general information Our research methodology provides a more comprehensive measure of literature synthesis than the review in this field ( 7 , 8 ) and gives the researcher a deeper understanding and intuitive experience of this field through quantitative data and visual graphics. Moreover, our research fulfills the basic criteria outlined in the initial guidelines for bibliometric studies ( 9 ). In our study, a total of 2057 publications were retrieved from 1 January 2010 to 21 October 2024, including 1734 articles (84.30%) and 323 reviews (15.70%). In general, from 2010 to 2024, there has been a consistent increase in the number of publications regarding the use of radiomics in lung cancer. As artificial intelligence becomes more advanced, machine learning techniques, such as deep learning methods ( 10 , 11 ), can assist researchers in processing complex medical imaging data. Additionally, more and more researchers are standardizing the creation of databases and subsequently conducting more multicenter collaborations ( 12 , 13 ). We foresee a rise in the number of publications in this field in the coming years. The results show that China and the United States significantly influence progress in this domain. From the heat map of national publications, it is easy to see that China is getting ahead of the US in the implementation of radiomics in cases of lung cancer in recent years. However, The United States boasts 42,203 citations, significantly more than any other nation, which implies that its published works are generally of high quality. We need to pay more attention to the cutting-edge development in this field, publish more high-quality articles, and further corroborate the importance of our study. Institutions preferred to cooperate with their own domestic units. Multicenter studies are a trend in the development of lung cancer radiomics, which can not only address the issues of limited samples, but also validate the validity of the model through multiple centers. However, due to competition and data privacy issues, integrating multi-center data to promote collaboration is quite difficult. Federated learning is a great way to solve the data island problem and promote multi-center collaboration ( 14 , 15 ), permitting multicentric analyses without the need to centralize data in a single repository. Federated learning has been used in many cancer-related studies, such as using federated learning to predict the efficacy of neoadjuvant chemotherapy for breast cancer ( 16 ). In the field of lung cancer radiomics research, a study developed an FL prediction model ( 17 ) for non-small cell lung cancer (NSCLC) treatment response by integrating convolutional neural networks (CNN) with data from four medical centers, focusing on patients undergoing chemoradiotherapy (CRT). To break down academic barriers, we advocate strengthening collaboration between domestic and international institutions. Professor Aerts, Hugo j. W. L.c. and professor Lambin P are pioneers in this field, they collaborated to introduced the notion of radiomics, ushering in a new era for its application in lung cancer ( 4 ). They then advanced their study of lung cancer radiomics by using artificial intelligence and deep learning, which has been studied and imitated by lots of researchers ( 18 – 20 ). However, collaboration among authors from different institutions is relatively rare. We should learn from our predecessors, cooperate for mutual benefit, overcome difficulties together, and provide more momentum for the development of lung cancer radiomics. Radiomics applications in lung cancer are reflected in the journal's publication volume. Meanwhile, analyzing journals can help researchers retrieve literature faster and prioritize journals for publishing their research results ( 21 ). Among the leading ten journals by Co-citation, Frontiers in Oncology was classified in JCR Q2, while others were all classified in JCR Q1, indicating that research of radiomics in lung cancer can be recognized by outstanding journals. In addition, through the dual map of journal we can visualize major applications of radiomics in lung cancer: 1) Screening and Diagnosis: Early identification of tumor types and malignancy ( 22 , 23 ) and assisting physicians with clinical staging ( 24 , 25 ). 2) Clinical outcome prediction: Radiomics models can predict treatment response ( 26 ) and survival prognosis ( 27 ), helping doctors make better clinical decisions. 3) Deep learning: Deep learning can automatically outline tumor lesions ( 28 ), reducing the workload of manual outlining, at the same time using deep learning models can extract deeper features ( 29 ) that cannot be manually extracted. 4) Integration of multiple omics studies: The integration of radiomics with pathomics ( 30 ) and genomics ( 31 ) can help achieve precise diagnosis and personalized treatment. 4.2 Identify emerging topics and research hotspots 4.2.1 Analysis of co-citation reference According to the findings from co-citation reference clustering and temporal clustering analysis, we found Epidermal growth factor receptor, PET, immunotherapy, radiation pneumonitis, reproducibility, deep learning, solitary pulmonary nodule, and brain metastasis are hot topics and trends in the use of radiomics for lung cancer. Overall, “solitary pulmonary nodule” and “solitary pulmonary nodule” are related to screening and diagnosis in lung cancer, “immunotherapy”, “brain metastasis” and “radiation pneumonitis” are related to prediction of clinical outcome in lung cancer, “PET” and “deep learning” are related to the imaging modalities and research methods of radiomics. The research in these areas will also be the main focus for future scholars. 4.2.2 Analysis of keywords We found that deep learning, immunotherapy, lung adenocarcinoma, stage I, radiation pneumonitis, lymph nodes, and tumor microenvironment are current research hotspots through keywords clustering and temporal clustering analysis. And the citation bursts showed the major keywords were “neoadjuvant chemotherapy”, “ground-glass nodules”, “radiomics signature” and “surgery”. These findings align with co-citation reference clustering and temporal clustering analysis. We confirmed that “screening and diagnosis”, “prediction of clinical outcome”, “deep learning” and “interconnectivity between multimodal data” are research hotspots and future research directions. Next, we discuss the latest progress and future challenges in these areas. 4.3 Advances in lung cancer radiomics In the above study, we have identified the current research status, research hotspots and future research directions in the field of lung cancer radiomics. Next, we explore the latest progress in using radiomics for lung cancer and examine the challenges and future prospects in this area. The dual map of journal and the analysis of co-citation reference and keyword clustering have demonstrated radiomics applied in lung cancer: 1) Screening and Diagnosis. 2) Clinical outcome prediction. 3) Deep learning. 4) Integration of multiple omics studies. 4.3.1 Screening and diagnosis Radiomics can assist in lung cancer screening and classify subtypes of lung cancer. The NLST in the U.S. and the European NELSON trial demonstrated that LDCT can greatly lower the risk of dying from lung cancer ( 32 , 33 ). However, LDCT still poses the problem of low specificity ( 34 ), often leading to false-positive cases, and there are many indeterminate nodules that need to be further biopsied to distinguish benign or malignant. Radiomics can non-invasively differentiate benign from malignant tumors. According to a recent study conducted by Warkentin et al. ( 35 ), three ML models were employed to forecast the malignancy risk of lung nodules, achieving an AUC of 0.93. In another study, Lin et al. ( 36 ) proposed a combined model to classify lung nodules into benign or malignant using radiomics and deep learning, achieving an accuracy of 92.8%. They proved the potential of radiomics in classifying lung nodules into benign or malignant. Radiomics has also made new progress in distinguishing subtypes of lung cancer. Dunn et al. ( 37 ) combined artificial intelligence and radiomics to automatically classify three histological subtypes of lung cancer (adenocarcinoma, small cell carcinoma, and squamous cell carcinoma), achieving an AUC of 0.97 AUC. The predictive performance of the radiomics model has significantly improved compared to previous studies ( 38 , 39 ). Besides, radiomics has also extended to predict rare lung cancer subtypes ( 40 ). Radiomics can also provide additional information for clinical decision-making in tumor staging. Chen et al. ( 41 ) proposed a multi-task learning-based radiomics model that can automatically classify lung cancer tumor subtypes and perform tumor staging, achieving an AUC of 0.843. Recently, Song et al. ( 42 ) accurately predicted the distant metastasis of non-small cell lung cancer using the combined method of quantitative image analysis and deep learning, achieving an AUC of 0.893. In addition, there are also radiomics studies to evaluate the mediastinal lymph node situation ( 43 ). Overall, radiomics plays an important role in the precise diagnosis of lung cancer. As the dual map of journal shown in Fig. 4e, the study of radiomics and molecular characteristics is also at the forefront of research in this field. Radiomics has also shown good efficacy in predicting lung cancer genotypes. Most radiomics studies in lung cancer focus on predicting EGFR/ALK/ROS1 mutations in lung adenocarcinoma. The latest CT radiomics-based model ( 44 ) for EGFR mutation prediction has achieved an AUC of 0.877, using both intratumoral and peritumoral features. Ninomiya et al. ( 45 ) further investigated the prognostic significance of EGFR Del19 and L858R mutation subtypes, achieving an accuracy of 81%. In addition, An AUC of 0.914 was achieved by the leading model based on CT images and clinical data ( 46 ) for determining ALK rearrangement status. In the latest study, Zhang et al. ( 47 ) used a multiscale radiomics approach to distinguish six genotypes and infiltrative immune phenotypes (EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status), achieving AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, providing a feasible method for developing personalized treatment plans for patients. There are also radiomics studies targeting the prediction of PD-L1 ( 48 ), tumor mutation burden status ( 49 ), and Ki-67 ( 50 ) in lung cancer. As more multicenter studies are conducted, the prediction of tumor genotypes will become more accurate. 4.3.2 Clinical outcome prediction Radiomics can assist in predicting treatment responses, adverse reactions, and disease recurrence in medical decision-making for precision medicine. Through the analysis of keywords in the previous sections of this article, we found that radiomics prediction for immunotherapy is currently a research hotspot. Radiomics can serve as an effective biomarker to forecast lung cancer immunotherapy outcomes. Machine learning-based radiomics has been effectively applied to forecast the response to immunotherapy in lung cancer ( 51 , 52 ). Delta-radiomics can reflect temporal heterogeneity of cancers, and has been used to predict clinical outcomes in lung cancer ( 53 ). Wu et al. ( 54 ) have proposed a deep learning model for predicting lung cancer response to immunotherapy, achieving an AUC of 0.778. For radiomics prediction of chemotherapy efficacy, a deep radiomic model ( 55 ) based on intratumoral and peritumoral features was built to predict the response to chemotherapy in lung cancer, achieving an AUC of 0.96. For radiomics prediction of radiotherapy efficacy, Lucia et al. ( 56 ) developed machine learning models for the prediction of regional and/or distant recurrence in lung cancer, with C-statistics from 0.53 to 0.59. Regarding the prediction of response to combination therapy, a recent radiomics nomogram ( 26 ) provided an efficient model to predict chemo-immunotherapy in advanced LC patients, achieving an AUC of 0.85. In addition, there were several radiomics researches ( 57 – 60 ) on neoadjuvant chemotherapy combined with immunotherapy for lung cancer, to assist in treatment decisions, achieving the goal of precise individualized medicine. Multicenter collaboration and prospective validation are future development trends to improve the predictive efficacy of treatment response prediction. Adverse reactions that may be caused by radiotherapy can be detected through radiomics, and correlates with dosiomics. Huang et al. ( 61 ) established radiomics models based on dosiomics and deep learning features to predict radiation pneumonitis after radiation therapy, achieving an AUC of 0.9986. Immune-related adverse events (AEs) are also a common challenge we encounter in clinical practice. In the research on forecasting immune-related adverse events in LC patients ( 62 ), a nomogram model was developed from PET/CT images, achieving an AUC of 0.92. Moreover, a multicenter retrospective study ( 63 ) was conducted to checkpoint inhibitor-related pneumonitis from radiation pneumonitis and the CT-based radiomics model had a good classification function, achieving an AUC of 0.859. Radiomics has been applied to predict lung cancer survival prognosis for a long time ( 18 ). In a retrospective study ( 10 ) including 976 patients who were treated with immunotherapy at MD Anderson and Stanford, Deep-CT model was used to predict overall survival and progression-free survival, achieving overall C-statistics from 0.70 to 0.75. Many other studies on radiomics to predict the overall survival ( 64 , 65 ) or progression-free survival ( 66 ) of lung cancer immunotherapy have also been reported. Radiomics can also indirectly predict survival prognosis by predicting biomarkers related to prognosis. Meng et al. ( 67 ) developed a CT-based radiomics model to forecast the infiltration of natural killer cells and shown that a larger NK cell infiltration was associated with improved overall survival (OS), with an AUC of 0.731. Chen et al. ( 68 ) established two radiomics models to predict expression levels of CD3 and CD8 T lymphocytes in lung cancer, achieving an AUC of 0.943. According to the bibliometric analysis in the earlier part of our article, an increasing number of radiomics studies will be applied to lung cancer clinical practice, providing more effective assistance for precision treatment of lung cancer. 4.3.3 Deep learning Deep learning is a sub-branch of machine learning (ML), which was first introduced by Hinton in 2006 ( 69 ). Deep learning methods for lung cancer mainly include classification, detection and segmentation. For example, deep learning can assist in the classification of benign and malignant lung nodules ( 36 ). Compared to traditional radiomics, deep learning has two main advantages: 1) Deep learning can assist doctors in automatically segmenting lesions ( 70 ), significantly reducing the workload of manual delineation and minimizing the errors caused by manual sketching. 2) Using deep learning models can extract deeper-level features that cannot be obtained through manual extraction ( 71 ). The end-to-end models created by deep learning can also provide more clinical significance for the application of radiomics in lung cancer. With the maturity of artificial intelligence technology, by improving deep learning algorithms, more opportunities will be provided for lung cancer radiomics. 4.3.4 Integration of multiple omics studies Radiomics features combined with clinical features have been applied to predict the efficacy of lung cancer treatment ( 72 ) and survival prognosis ( 73 ), to enhance model performance. Similarly, Radiogenomics can improve predictive performance by combining radiomics and genomics. Chen et al. ( 31 ) established a radiogenomics model to predict the response and pneumotoxicity to immunotherapy in lung cancer, with an AUC of 0.70. By integrating radiomics with transcriptomics using gene set enrichment analysis (GSEA), biological relevance of radiomics models can be further analyzed ( 58 – 60 ). High-throughput quantitative features can be extracted from medical images using radiomics, providing a reference for accurate tumor diagnosis and prognosis prediction. However, radiomics aims to describe tumors from a macroscopic perspective and is essentially data-driven. The underlying biological significance remains unclear. Pathomics uses similar high-throughput image feature extraction techniques to convert whole-slide digital pathology slides into quantitative datasets, objectively reflecting tumor cell and tissue structure information at the microscopic level. It can more efficiently assist in tumor pathological diagnosis, treatment efficacy, and prognosis prediction. Therefore, the combination of radiomics and pathomics, with the help of artificial intelligence methods, can fully integrate macroscopic and microscopic features, which is expected to provide new ideas for accurate tumor diagnosis and treatment. The radiomics combined with pathology model is widely used for prognosis prediction in tumors such as gastric cancer ( 30 ), rectal cancer ( 74 , 75 ), breast cancer ( 76 ) and nasopharyngeal carcinoma ( 77 ). However, there have been no relevant reports in the field of lung cancer radiomics prognosis, and it is a promising research direction in the future. The interconnectivity between multimodal data can be further explored as a development trend in lung cancer radiomics. 5 Limitations However, our study still has some limitations. First, the limitations of bibliometric analysis software mean that our data were only from the WoSCC database. Second, several significant non-English works might have been overlooked, as the study solely looked at English-language works. Finally, due to the dynamic nature of the database, some high-quality recently published studies may be undervalued for their low citation frequency. 6 Conclusion In summary, our study comprehensively and quantitatively presents the research status, research hotspots and trends in this field's development. Our results show that the application of radiomics in lung cancer is a highly promising research area. Multicenter studies are a trend in the development of lung cancer radiomics, and we advocate strengthening cooperation between countries/regions, institutions, and authors to break down academic barriers. The research hotspot of lung cancer radiomics is more inclined towards clinical applications, including screening, diagnosis and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend. Furthermore, deep learning can provide strong technical support for radiomics. Multimodal learning for information fusion is another crucial development trend, we should pay more attention to multi-omics integration in the future. Declarations Names of authors: Jiangbo He 1 , Chaoyuan Liu 1 (corresponding author), Fang Ma 1 , Yiguang Zhou 1 , Xianling Liu 1,2 (corresponding author) Email Address of the Corresponding Author: Chaoyuan Liu: [email protected] Xianling Liu: [email protected] Affiliations and addresses: 1 Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China. 2 Department of Oncology, Guilin Hospital of the Second Xiangya Hospital, Central South University, Guilin, China. Funding: This work was supported by the Natural Science Foundation of Hunan Province (No. 2023JJ40837 and No. 2022JJ40704), Beijing Science and Technology Innovation Medical Development Foundation (No. KC2023-JX-0288-PM37), Education and Teaching Reform Research Project of Central South University (No.2022jy197). Data availability The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study did not involve human and/ or animal studies. Therefore, ethical approval does not apply to this study. Competing interests The authors declare no competing interests. Clinical trial number: not applicable. Author Contribution Study conception and design: Jiangbo He, Chaoyuan Liu, and Xianling Liu. Data collection and data curation: Jiangbo He and Yiguang Zhou. Data analysis and visualization: Jiangbo He.Interpretation of data: Chaoyuan Liu, Fang Ma, and Xianling Liu. Writing—original draft & editing: Jiangbo He. Writing—Review: Chaoyuan Liu, Fang Ma, and Xianling Liu. Funding acquisition: Chaoyuan Liu and Xianling Liu. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-a Cancer Journal for Clinicians. 2024;74(3):229-63. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. 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Table of country published literature Rank Country/region Article counts centrality Percentage (%) Citation Citation per publication 1 CHINA 983 0.15 47.79% 15891 16.17 2 USA 532 0.41 25.86% 42203 79.33 3 ITALY 189 0.25 9.19% 8235 43.57 4 NETHERLANDS 133 0.09 6.47% 24473 184.01 5 FRANCE 107 0.02 5.20% 6006 56.13 6 GERMANY 98 0.09 4.76% 4399 44.89 7 CANADA 94 0.11 4.57% 11329 120.52 8 ENGLAND 87 0.24 4.23% 6283 72.22 9 SOUTH KOREA 77 0.01 3.74% 1907 24.77 10 JAPAN 72 0.01 3.50% 1277 17.74 Table 2. Table of Institutional Published Literature Rank Institution Country Number of studies Total citations Average citation 1 General Electric USA 86 988 11.49 2 Maastricht University USA 81 22573 278.68 3 Fudan University China 78 977 12.53 4 Shandong First Medical University & Shandong Academy of Medical Sciences China 77 961 12.48 5 Harvard University USA 75 24172 322.29 6 Chinese Academy of Sciences China 73 2607 35.71 7 H Lee Moffitt Cancer Center & Research Institute USA 64 16966 265.09 8 University of Texas System USA 62 4005 64.60 9 Institut National de la Sante et de la Recherche Médicale (Inserm) France 59 4732 80.20 10 Shanghai Jiao Tong University China 58 1070 18.45 Table 3. Table of Journal Publications Rank Journal Article counts Percentage IF (2024) Quartile in category 1 frontiers in oncology 178 8.65% 3.5 Q2 2 scientific reports 87 4.23% 3.8 Q1 3 cancers 85 4.13% 4.5 Q1 4 european radiology 65 3.16% 4.7 Q1 5 medical physics 65 3.16% 3.2 Q1 6 academic radiology 42 2.04% 3.8 Q1 7 physics in medicine and biology 42 2.04% 3.3 Q1 8 european journal of nuclear medicine and molecular imaging 35 1.70% 8.6 Q1 9 translational lung cancer research 35 1.70% 4.0 Q1 10 clinical radiology 34 1.65% 2.1 Q2 Table 4. Co-citation table of journals Rank Cited Journal Co-Citation IF（2024） Quartile in category 1 RADIOLOGY 1626 12.1 Q1 2 SCI REP-UK 1261 3.8 Q1 3 EUR RADIOL 1257 4.7 Q1 4 PLOS ONE 989 2.9 Q1 5 FRONT ONCOL 960 3.5 Q2 6 J THORAC ONCOL 938 21.0 Q1 7 MED PHYS 931 3.2 Q1 8 LUNG CANCER 904 4.5 Q1 9 NAT COMMUN 862 14.7 Q1 10 EUR J CANCER 855 7.6 Q1 Table 5. Author's publications and co-citation table Rank Author Count Rank Co-cited author Citation 1 Aerts, Hugo j. W. L. 41 1 Lambin P 919 2 Gillies, Robert j. 36 2 Aerts Hjwl 729 3 Lambin, Philippe 36 3 Gillies Rj 717 4 Schabath, Matthew b. 29 4 Van Griethuysenjjm 490 5 Lee, ho Yun 23 5 Zwanenburg a 402 6 Tian, Jie 22 6 Parmar C 374 7 Dekker, Andre 20 7 Coroller Tp 322 8 Schwartz, Lawrence h. 20 8 Kumar V 311 9 Madabhushi, Anant 19 9 LiuY 300 10 Leijenaar, ralph t. H. 18 10 Siegel Rl 259 Table 6. Co-citation table of literature Rank Title Journal author(s) Total citations 1 Radiomics: Images Are More than Pictures, They Are Data RADIOLOGY Gillies RJ 393 2 The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping RADIOLOGY Zwanenburg A 318 3 Computational Radiomics System to Decode the Radiographic Phenotype CANCER RESEARCH van Griethuysen JJM 292 4 Radiomics: the bridge between medical imaging and personalized medicine NATURE REVIEWS CLINICAL ONCOLOGY Lambin P 290 5 Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach NATURE COMMUNICATIONS Aerts HJWL 227 6 Radiomics and radiogenomics in lung cancer: A review for the clinician LUNG CANCER Thawani R 147 7 A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study LANCET ONCOLOGY Sun R 145 8 CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma RADIOTHERAPY AND ONCOLOGY Coroller TP 128 9 Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers ANNALS OF ONCOLOGY Trebeschi S 116 10 Repeatability and Reproducibility of Radiomic Features: A Systematic Review INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS Traverso A 107 Table 7 High Frequency Keyword Table Rank Keyword Counts Rank Keyword Counts 1 radiomics 1320 11 heterogeneity 188 2 features 395 12 deep learning 185 3 images 342 13 diagnosis 179 4 ct 332 14 radiotherapy 150 5 survival 318 15 computed-tomography 131 6 machine learning 273 16 signature 126 7 classification 258 17 pet 125 8 texture analysis 235 18 pulmonary nodules 122 9 prediction 224 19 immunotherapy 118 10 computed tomography 216 20 artificial intelligence 114 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6409960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457820954,"identity":"c133201f-64b3-4f98-b543-f06efc3814cb","order_by":0,"name":"Jiangbo He","email":"","orcid":"","institution":"Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan","correspondingAuthor":false,"prefix":"","firstName":"Jiangbo","middleName":"","lastName":"He","suffix":""},{"id":457820955,"identity":"5bef223f-d768-48ab-b9bc-838c54b9b723","order_by":1,"name":"Chaoyuan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACPigtB6HYiNACU2NMupbEBuK1sJ8xk+ZtO5ze33/GgOFD2WEG/tkNBLTw5JhJ85xJy51xI8eAcca5wwwSdw4Q0CLBA9RSYZO7QYLHgBloHYOBRAIxWgwk0g34zxgw/yVeS4VNggFDjgEzI1FaeNKKLeecSTOccSOt4GDPuXQeiRsEtPCzH954423bYXn+/sMbH/wos5bjn0FACwMDh4kUD5R5AIh58CiFAfbHH38QoWwUjIJRMApGMAAAoag4QchQq+cAAAAASUVORK5CYII=","orcid":"","institution":"Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan","correspondingAuthor":true,"prefix":"","firstName":"Chaoyuan","middleName":"","lastName":"Liu","suffix":""},{"id":457820956,"identity":"d9e39c27-bc3e-42aa-a955-5cd2adc6017e","order_by":2,"name":"Fang Ma","email":"","orcid":"","institution":"Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Ma","suffix":""},{"id":457820957,"identity":"81d168f3-f12b-492b-967f-7ebe9c06baf7","order_by":3,"name":"Yiguang Zhou","email":"","orcid":"","institution":"Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan","correspondingAuthor":false,"prefix":"","firstName":"Yiguang","middleName":"","lastName":"Zhou","suffix":""},{"id":457820958,"identity":"c17e2031-77ac-4ced-aec6-cf6b606a20b1","order_by":4,"name":"Xianling Liu","email":"","orcid":"","institution":"Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan","correspondingAuthor":false,"prefix":"","firstName":"Xianling","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-04-09 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version\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6409960/v1/a021c902e2efe0a613fdeba8.jpg"},{"id":83040354,"identity":"e924a4f5-d5d3-4459-9db0-6b62d8eeb76b","added_by":"auto","created_at":"2025-05-19 10:38:47","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1191461,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6409960/v1/921b9206480b2c37e176a256.jpg"},{"id":83040305,"identity":"af41d287-e1f7-4f33-94a2-88ad7b40bc8a","added_by":"auto","created_at":"2025-05-19 10:38:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1107749,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6409960/v1/26c9c9bac53d117076c5b9a6.jpg"},{"id":88397974,"identity":"95e1e920-f71f-4267-90ea-254dc72fb901","added_by":"auto","created_at":"2025-08-06 06:32:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6900764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6409960/v1/68cfca39-afb7-4336-8c31-7b5c4d567bd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Worldwide research landscape of radiomics in lung cancer: A scientometric study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer ranks among the most prevalent cancers and is responsible for the highest number of cancer-related deaths globally. The Global Cancer Statistics 2022 report indicates that lung cancer was the most prevalent cancer in the United States in 2022, with nearly 2.5\u0026nbsp;million new cases, accounting for one-eighth of all new cancer cases globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Additionally, the most common cause of cancer-related death is lung cancer, which accounts for 18.7% of cancer-related deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In both men and women, lung cancer ranks first in terms of incidence and mortality rates, according to the National Cancer Center of China. It is common for lung cancer to show no symptoms when it is in its early stages, leading to progression or tumor metastasis by the time of diagnosis, which contributes to its poor mean 5-year survival rate of 20\u0026ndash;30% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In order to improve the survival rate of lung cancer (LC) patients, timely diagnosis and accurate prediction are crucial.\u003c/p\u003e \u003cp\u003eThe earliest application of radiomics can be traced back to a study aimed to explore the image-based biomarkers for solid mass pulmonary tumors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). And Lambin et al (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) first introduced the concept of radiomics in 2012. Large amounts of medical data are often difficult to filter and interpret, and the rapid development of artificial intelligence in recent years has provided unique opportunities for the processing of medical data. Radiomics is the bridge between medical imaging and medicine, which extracts features related to tumor and the microenvironment from medical imaging data, ultimately transformed into exploitable high-dimensional data (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Ralf Floca et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) have recently summarized the previous research of radiomics and proposed a consensus radiomics workflow definition proposal with high agreement comprising seven phases: study design, data acquisition, data management, image processing, feature extraction, modeling and reporting.\u003c/p\u003e \u003cp\u003eBibliometric analysis employs quantitative mathematical and statistical techniques to study the yearly distribution across various countries, institutions, journals, authors, keywords, and references. Numerous fields have utilized bibliometric analysis. To enhance the execution of radiomics research on lung cancer, research status, hotspots, research frontiers, and research trends were analyzed through a bibliometric study using the Web of Science Core Collection (WOSCC) database, which will provide researchers and institutions with directions for subsequent research and references for the application of radiomics in clinical decision-making for LC patients.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Database and systematic search strategy\u003c/h2\u003e \u003cp\u003eFrom the Web of Science Core Collection (WoSCC) database, we obtained the papers related to radiomics in lung cancer. There are other databases available, such as PubMed, Scopus, and Google Scholar. However, the WoSCC database offers several advantages over the others. The Web of Science Core Collection (WoSCC) database is the world's largest scientific citation database, it covers multiple disciplines and can simultaneously retrieve numerous sub-disciplines, which is very helpful for precise searches. The WoSCC database provides detailed retrieval and paper analysis features, including citation counts, citation reports, and H-index, and is generally considered to have more authoritative data than other sources. In addition, the WoSCC database is frequently used in bibliometric analysis and is regarded as one of the most prominent databases. As a result, the WoSCC database was chosen for our literature search. To reduce biases from database updates, the literature search was performed in a single day on 21 October 2024. The search formula is ((((((((((((((((TS=(Lung Neoplasms)) OR TS=(Pulmonary Neoplasms)) OR TS=(Neoplasms, Lung)) OR TS=(Lung Neoplasm)) OR TS=(Neoplasm, Lung)) OR TS=(Neoplasms, Pulmonary)) OR TS=(Neoplasm, Pulmonary)) OR TS=(Pulmonary Neoplasm)) OR TS=(Lung Cancer)) OR TS=(Cancer, Lung)) OR TS=(Cancers, Lung)) OR TS=(Lung Cancers)) OR TS=(Pulmonary Cancer)) OR TS=(Cancer, Pulmonary)) OR TS=(Pulmonary Cancers)) OR TS=(Cancer of the Lung)) OR TS=(Cancer of Lung) AND ((TS=(Radiomics)) OR TS=(Radiomic*)) OR TS=(imageomics).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis tools\u003c/h2\u003e \u003cp\u003eThe publication trends and proportions of national and international publications were analyzed and plotted using GraphPad Prism version 8.0.2. In addition, in order to analyze the data and visualize the science mapping, CiteSpace (6.2.4R (64 bit) Advanced Edition) and VOSviewer (1.6.18 Edition) were used. In 2009, Waltman et al developed VOSviewer, a free Java-based program, that allowed researchers to create and view bibliometric maps, especially large bibliometric maps easily readable. Professor Chen created the CiteSpace software, and our research used this tool to analyze the dual-map overlays of the journals, the timeline view of references, keyword co-occurrences, and clustering of keywords.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Database search\u003c/h2\u003e \u003cp\u003eIn our study, we retrieved 2057 publications between 1 January 2010 and 21 October 2024, including 1734 articles (84.30%) and 323 reviews (15.70%). 9539 authors contributed to these publications from 2263 institutions across 61 countries. The 2057 relevant documents were exported as plain text files and Excel tables for bibliometric evaluation and visual analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The general global trends\u003c/h2\u003e \u003cp\u003eThe first article in this field was published in 2010, and Fig.\u0026nbsp;2 displayed the volume and trends of publications from 2010 to 2024. As we mentioned before, radiomics emerged as a formal concept in 2012. Before 2015, radiomics in lung cancer had not received much attention. From 2010 to 2024, it has shown a continuous growth trend. In general, the development stage can be divided into three stages: the early stage (2010\u0026ndash;2014), the growth stage (2015\u0026ndash;2019), and the prosperity stage (2020\u0026ndash;2024). The increasing trend in the number of publications reflects research on lung cancer using radiomics is developing and gaining interest. We predict that the annual publication volume in 2024 will continue to grow and exceed the annual publication volume in 2023. With the maturity of artificial intelligence, the application of radiomics in lung cancer will become increasingly widespread.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Analysis of countries/regions and institutions\u003c/h2\u003e \u003cp\u003eA total of 2,263 institutions across 61 nations/regions participated in the study of radiomics in lung cancer, according to the literature that was retrieved. Figure\u0026nbsp;3a displayed the top 10 countries published the most publications per year between 2010 and 2024. Almost half of the 983 publications were published by China (47.79%), making it the top contributor. A total of 983 publications were published by China, followed by 532 publications from the USA, 189 publications from Italy, 133 publications from the Netherlands, and 107 publications from France. The United States had the most citations overall (42203), followed by Netherlands (24473), China (15891), Canada (11329) and Italy (8235).\u003c/p\u003e \u003cp\u003eThe United States had 42,203 citations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) among the top 10 published nations, which is much more than any other nation/region, and it ranks third overall with a citation-to-publication ratio of 79.33, indicating that the quality of its published papers is generally high. China's publication volume (983 publications) ranks first among countries, while its citation count (15,891 times) ranks third, and it had the lowest citation-to-publication ratio (16.17). This result shows that we need to pay more attention to the cutting-edge development in this field, publish more high-quality articles, and further corroborate the importance of our study. The networks of country cooperation are illustrated in Fig.\u0026nbsp;3c. China and the United States, the world's two largest producers, have extensive cooperative relationships. China's cooperation with Japan, the Netherlands and South Korea is close, while a closer relationship exists between the United States and Italy, Canada, and the United Kingdom. The development of radiomics in lung cancer was significantly influenced by the United States, which had the highest centrality (0.41). We can also find that international cooperation can promote the innovation and development of the application of radiomics in lung cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of country published literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry/region\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArticle counts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecentrality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCitation per publication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHINA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e79.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITALY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNETHERLANDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e184.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRANCE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGERMANY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCANADA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e120.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENGLAND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e72.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOUTH KOREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAPAN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the top ten institutions by publication volume that published relevant papers on radiomics in lung cancer. A total of 2263 institutions published relevant papers on radiomics in lung cancer in the past few years. Among them, five were from the United States, four were from China, and the remaining one was from France. General Electric merged as the most prolific (86 papers, 988 citations, and 11.49 citations per paper), followed by Maastricht University (81 papers, 22573 citations, 278.68 per paper), Fudan University (78 papers, 977 citations, 12.53 per paper), Shandong First Medical University (77 papers, 961 citations, 12.48 per paper) and Harvard University (75 papers, 24172 citations, 322.29 per paper). The networks of institutional cooperation are illustrated in Fig.\u0026nbsp;3d. Through the visualization analysis, we found that domestic and foreign institutions preferred to cooperate with their own domestic units. In order to break down academic barriers, it is crucial to strengthen cooperation between domestic and international institutions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of Institutional Published Literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal citations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAverage citation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Electric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaastricht University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFudan University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShandong First Medical University \u0026amp; Shandong Academy of Medical Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarvard University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e322.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese Academy of Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH Lee Moffitt Cancer Center \u0026amp; Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e265.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Texas System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e64.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitut National de la Sante et de la Recherche M\u0026eacute;dicale (Inserm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShanghai Jiao Tong University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of journals and co-cited journals\u003c/h2\u003e \u003cp\u003eThe top ten journals by publication volume are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The most publications were published by Frontiers in Oncology (178 papers, IF: 3.5), followed by scientific reports(87 papers, IF: 3.8), cancers༈85 papers, IF: 4.5༉, european radiology (65 papers, IF: 4.7༉. The European Journal of Nuclear Medicine and Molecular Imaging, with an impact factor of 8.6, is the leading journal among the top ten most prolific in its field. Among the leading ten journals by number of publications, Frontiers in Oncology and Clinical radiology were classified in JCR Q2, while others were all classified in JCR Q1, which represents a promising development in lung cancer radiomics. The density map of journal publications is illustrated in Fig.\u0026nbsp;4a. A high volume of publications reflects the journal's interest and activity in radiomics in lung cancer, indicating the research frontiers and trends in this field. Through the number of co-citations in this field, we can see how influential journals are in this field. The top ten journals by number of co-citations are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The Co-citation network map of journals is illustrated in Fig.\u0026nbsp;4b. Radiology (citations: 1626, IF: 12.1) was the journal with the most co-citations, followed by SCI REP-UK (citations: 1261, IF: 3.8), and EUR RADIOL (citations: 1257, IF: 4.7). J THORAC ONCOL, among the top 10 journals with the most joint citations, was cited 938 times and had the highest impact factor (IF 21.0). Among the leading ten journals by Co-citation, Frontiers in Oncology was classified in JCR Q2, while others were all classified in JCR Q1, indicating that research of radiomics in lung cancer can be recognized by outstanding journals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTable of Journal Publications\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArticle counts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIF\u003c/p\u003e \u003cp\u003e(2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQuartile in category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efrontiers in oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003escientific reports\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeuropean radiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedical physics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eacademic radiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ephysics in medicine and biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeuropean journal of nuclear medicine and molecular imaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etranslational lung cancer research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclinical radiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCo-citation table of journals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCited Journal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCo-Citation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIF(2024)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuartile in category\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRADIOLOGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCI REP-UK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR RADIOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLOS ONE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFRONT ONCOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJ THORAC ONCOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMED PHYS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUNG CANCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNAT COMMUN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEUR J CANCER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe dual map of journal is illustrated in Fig.\u0026nbsp;4e, through which we can visualize theme distribution of academic publications. Colorful trajectories represent citation connections, with the journal on the left represents early research publications and the journal on the right corresponds to more recent publications. According to the graphical results, the research published in journals in the fields of medicine/medical/clinical provided a theoretical foundation for subsequent research. The clusters on the right illustrate the development and current status of radiomics applications in cancer. Important clusters include \"Health/Nursing/Medicine,\" which indicates that the current focus of lung cancer radiomics is on clinical applications and healthcare research, and \"Molecular/Biology/Genetics,\" which indicates that the study of genetically related molecular issues through lung cancer radiomics is a current research hotspot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Analysis of authors and co-cited authors\u003c/h2\u003e \u003cp\u003eThe leading ten journals by the number of publications and the top 10 authors by citation were listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The top 10 authors by the number of publications have published a total of 264 papers, accounting for 12.83% of all papers in the field. It can be intuitively seen that Aerts, Hugo j. W. L.c. has authored the most papers, totaling 41, published from 2010 to 2024, followed by Gillies, Robert j. (36 papers), Lambin, Philippe (36 papers), Schabath, Matthew b. (29 papers), Lee, ho Yun (23 papers). Subsequently, we conducted a visual assessment of the author's cooperative relationships. Figure\u0026nbsp;4c depicted the network of author collaboration, revealing the collaborative connections between these authors. In Fig.\u0026nbsp;4d, the authors' co-citation network is depicted. Lambin P's papers have the most citations, totaling 919, and the highest average of 25.53 citations, indicating he is a pioneer in lung cancer radiomics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAuthor's publications and co-citation table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCo-cited author\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAerts, Hugo j. W. L.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLambin P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGillies, Robert j.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAerts Hjwl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLambin, Philippe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGillies Rj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchabath, Matthew b.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVan Griethuysenjjm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLee, ho Yun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZwanenburg a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTian, Jie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParmar C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDekker, Andre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoroller Tp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchwartz, Lawrence h.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKumar V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMadabhushi, Anant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiuY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeijenaar, ralph t. H.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSiegel Rl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCo-citation table of literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eauthor(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal citations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiomics: Images Are More than Pictures, They Are Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRADIOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGillies RJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRADIOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZwanenburg A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputational Radiomics System to Decode the Radiographic Phenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCANCER RESEARCH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003evan Griethuysen JJM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiomics: the bridge between medical imaging and personalized medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNATURE REVIEWS CLINICAL ONCOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLambin P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNATURE COMMUNICATIONS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAerts HJWL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiomics and radiogenomics in lung cancer: A review for the clinician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLUNG CANCER\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThawani R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLANCET ONCOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSun R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT-based radiomic signature predicts distant metastasis in lung adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRADIOTHERAPY AND ONCOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoroller TP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredicting response to cancer immunotherapy using noninvasive radiomic biomarkers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eANNALS OF ONCOLOGY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrebeschi S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRepeatability and Reproducibility of Radiomic Features: A Systematic Review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eINTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraverso A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Analysis of co-cited references\u003c/h2\u003e \u003cp\u003eWith a timeslice of one year, the data covers the period from 2010 to 2024, the network of co-cited references depicted in Fig.\u0026nbsp;5a comprises 981 nodes and 5,707 links. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists the ten references with the most co-citations. The most frequently co-cited reference from Philippe Lambin, titled 'Radiomics: Images Are More than Pictures, They Are Data' was published in RADIOLOGY and had 393 co-citations. Among these references, the research of lung cancer radiomics was more inclined towards clinical applications, including screening, diagnosis and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend.\u003c/p\u003e \u003cp\u003eWe performed co-citation reference and temporal clustering analyses, as illustrated in Figs.\u0026nbsp;7b and 7c. We found that imaging (cluster 8) is an early research hotspot. Quantitative imaging (cluster 0) and cone-beam CT (cluster 10) are mid-term research hotspots. Epidermal growth factor receptor, PET, immunotherapy, radiation pneumonitis, reproducibility, deep learning, solitary pulmonary nodule, and brain metastasis are hot topics and trends in this field. It is especially reflected in three aspects of clinical applications: screening (solitary pulmonary nodule), diagnosis (Epidermal growth factor receptor) and prediction of clinical outcome (immunotherapy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Analysis of keywords\u003c/h2\u003e \u003cp\u003eKeywords highlight the article's theme, and their analysis reveals research hotspots and field development trends. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e lists the 10 keywords that appear most frequently. In VOSviewer, 'radiomics' (1320) emerges as the most frequently occurring keyword based on keyword co-occurrence analysis, followed by features (396), images (342), ct (332), and survival (318), which is shown in Fig.\u0026nbsp;6a and Fig.\u0026nbsp;6b. Then we removed the redundant keywords and developed a network comprising 173 keywords, each appearing a minimum of 18 times, and obtained different clusters. The red cluster, labeled as Cluster 1, includes 44 keywords such as ct, challenges, delineation, fdg-pet, image, metabolism, impact, parameters, prognostic value, radiomic feature, repeatability, stability, tumor volume, textural feature, robustness, reproducibility. Cluster 2 (green) has 42 keywords, including radiomics, metastasis, radiogenomics, biomarkers, blockade, chemotherapy, EGFR, gefitinib, imaging phenotype, immunotherapy, intratumor heterogeneity, machine learning, MRI, multicenter, nivolumab, open label, PD-L1, precision medicine, resistance, somatic mutations. Cluster 3(blue)has 34 keywords, including classification, computed tomography, nomogram, signature, body radiation therapy, differentiation, ground glass nodules, international association, lesions, lobectomy, pattern, recurrence, subtype, system, surgery. Cluster 4 (yellow) has 30 keywords, including artificial intelligence, computer-aided detection, convolutional neural network, CT image, diagnosis, epidemiology, deep learning, guideline, malignancy, management, model, performance, mortality, prediction, risk, size, validation. Cluster 5 (purple) has 23 keywords, including chemoradiotherapy, dosiomics, feature selection, outcome, pathological responses, phenotype, pneumonitis, quantitative imaging, risk factors, texture, toxicity, volume.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHigh Frequency Keyword Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKeyword\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKeyword\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCounts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eradiomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eheterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efeatures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edeep learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eimages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ediagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecomputed-tomography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emachine learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003esignature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eclassification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etexture analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epulmonary nodules\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprediction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eimmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eartificial intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe used CiteSpace to conduct keywords clustering and temporal clustering analysis shown in Fig.\u0026nbsp;6c and Fig.\u0026nbsp;6d. We found that deep learning, immunotherapy, lung adenocarcinoma, stage I, radiation pneumonitis, lymph nodes, and tumor microenvironment are current research hotspots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Citation bursts\u003c/h2\u003e \u003cp\u003eThe references that saw a sudden increase in citations were displayed in Fig.\u0026nbsp;5d. We have identified the top 50 most consistent citation bursts in radiomics related to lung cancer through CiteSpace. The 50 references, published from 2010 to 2024, demonstrate frequent citations in recent years. Furthermore, currently six of these papers are experiencing a period of high citation, indicating that applying radiomics to lung cancer studies will continue to receive attention in the future.\u003c/p\u003e \u003cp\u003eFigure 6e displayed the top 50 keywords with the most significant citation bursts. Technical keywords such as 'texture analysis', 'fdg pet' and 'image features' were the first keyword burst identified in 2012. Subsequently, studies on tumor treatment methods like 'radiotherapy' or 'chemoradiotherapy' gained popularity. The most recent surge in keywords started in 2022 and has continued to the present. The primary keywords included: \u0026ldquo;neoadjuvant chemotherapy\u0026rdquo;, \u0026ldquo;ground-glass nodules\u0026rdquo;, \u0026ldquo;radiomics signature\u0026rdquo; and \u0026ldquo;surgery\u0026rdquo;, suggesting that these research themes might become future directions of research in the next few years.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis of general information\u003c/h2\u003e \u003cp\u003eOur research methodology provides a more comprehensive measure of literature synthesis than the review in this field (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and gives the researcher a deeper understanding and intuitive experience of this field through quantitative data and visual graphics. Moreover, our research fulfills the basic criteria outlined in the initial guidelines for bibliometric studies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In our study, a total of 2057 publications were retrieved from 1 January 2010 to 21 October 2024, including 1734 articles (84.30%) and 323 reviews (15.70%). In general, from 2010 to 2024, there has been a consistent increase in the number of publications regarding the use of radiomics in lung cancer. As artificial intelligence becomes more advanced, machine learning techniques, such as deep learning methods (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), can assist researchers in processing complex medical imaging data. Additionally, more and more researchers are standardizing the creation of databases and subsequently conducting more multicenter collaborations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). We foresee a rise in the number of publications in this field in the coming years.\u003c/p\u003e \u003cp\u003eThe results show that China and the United States significantly influence progress in this domain. From the heat map of national publications, it is easy to see that China is getting ahead of the US in the implementation of radiomics in cases of lung cancer in recent years. However, The United States boasts 42,203 citations, significantly more than any other nation, which implies that its published works are generally of high quality. We need to pay more attention to the cutting-edge development in this field, publish more high-quality articles, and further corroborate the importance of our study. Institutions preferred to cooperate with their own domestic units. Multicenter studies are a trend in the development of lung cancer radiomics, which can not only address the issues of limited samples, but also validate the validity of the model through multiple centers. However, due to competition and data privacy issues, integrating multi-center data to promote collaboration is quite difficult. Federated learning is a great way to solve the data island problem and promote multi-center collaboration (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), permitting multicentric analyses without the need to centralize data in a single repository. Federated learning has been used in many cancer-related studies, such as using federated learning to predict the efficacy of neoadjuvant chemotherapy for breast cancer (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In the field of lung cancer radiomics research, a study developed an FL prediction model (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) for non-small cell lung cancer (NSCLC) treatment response by integrating convolutional neural networks (CNN) with data from four medical centers, focusing on patients undergoing chemoradiotherapy (CRT). To break down academic barriers, we advocate strengthening collaboration between domestic and international institutions.\u003c/p\u003e \u003cp\u003eProfessor Aerts, Hugo j. W. L.c. and professor Lambin P are pioneers in this field, they collaborated to introduced the notion of radiomics, ushering in a new era for its application in lung cancer (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). They then advanced their study of lung cancer radiomics by using artificial intelligence and deep learning, which has been studied and imitated by lots of researchers (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, collaboration among authors from different institutions is relatively rare. We should learn from our predecessors, cooperate for mutual benefit, overcome difficulties together, and provide more momentum for the development of lung cancer radiomics.\u003c/p\u003e \u003cp\u003eRadiomics applications in lung cancer are reflected in the journal's publication volume. Meanwhile, analyzing journals can help researchers retrieve literature faster and prioritize journals for publishing their research results (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Among the leading ten journals by Co-citation, Frontiers in Oncology was classified in JCR Q2, while others were all classified in JCR Q1, indicating that research of radiomics in lung cancer can be recognized by outstanding journals. In addition, through the dual map of journal we can visualize major applications of radiomics in lung cancer: 1) Screening and Diagnosis: Early identification of tumor types and malignancy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and assisting physicians with clinical staging (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). 2) Clinical outcome prediction: Radiomics models can predict treatment response (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and survival prognosis (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), helping doctors make better clinical decisions. 3) Deep learning: Deep learning can automatically outline tumor lesions (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), reducing the workload of manual outlining, at the same time using deep learning models can extract deeper features (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) that cannot be manually extracted. 4) Integration of multiple omics studies: The integration of radiomics with pathomics (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) and genomics (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) can help achieve precise diagnosis and personalized treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Identify emerging topics and research hotspots\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Analysis of co-citation reference\u003c/h2\u003e \u003cp\u003eAccording to the findings from co-citation reference clustering and temporal clustering analysis, we found Epidermal growth factor receptor, PET, immunotherapy, radiation pneumonitis, reproducibility, deep learning, solitary pulmonary nodule, and brain metastasis are hot topics and trends in the use of radiomics for lung cancer. Overall, \u0026ldquo;solitary pulmonary nodule\u0026rdquo; and \u0026ldquo;solitary pulmonary nodule\u0026rdquo; are related to screening and diagnosis in lung cancer, \u0026ldquo;immunotherapy\u0026rdquo;, \u0026ldquo;brain metastasis\u0026rdquo; and \u0026ldquo;radiation pneumonitis\u0026rdquo; are related to prediction of clinical outcome in lung cancer, \u0026ldquo;PET\u0026rdquo; and \u0026ldquo;deep learning\u0026rdquo; are related to the imaging modalities and research methods of radiomics. The research in these areas will also be the main focus for future scholars.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Analysis of keywords\u003c/h2\u003e \u003cp\u003eWe found that deep learning, immunotherapy, lung adenocarcinoma, stage I, radiation pneumonitis, lymph nodes, and tumor microenvironment are current research hotspots through keywords clustering and temporal clustering analysis. And the citation bursts showed the major keywords were \u0026ldquo;neoadjuvant chemotherapy\u0026rdquo;, \u0026ldquo;ground-glass nodules\u0026rdquo;, \u0026ldquo;radiomics signature\u0026rdquo; and \u0026ldquo;surgery\u0026rdquo;. These findings align with co-citation reference clustering and temporal clustering analysis. We confirmed that \u0026ldquo;screening and diagnosis\u0026rdquo;, \u0026ldquo;prediction of clinical outcome\u0026rdquo;, \u0026ldquo;deep learning\u0026rdquo; and \u0026ldquo;interconnectivity between multimodal data\u0026rdquo; are research hotspots and future research directions. Next, we discuss the latest progress and future challenges in these areas.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Advances in lung cancer radiomics\u003c/h2\u003e \u003cp\u003eIn the above study, we have identified the current research status, research hotspots and future research directions in the field of lung cancer radiomics. Next, we explore the latest progress in using radiomics for lung cancer and examine the challenges and future prospects in this area.\u003c/p\u003e \u003cp\u003eThe dual map of journal and the analysis of co-citation reference and keyword clustering have demonstrated radiomics applied in lung cancer: 1) Screening and Diagnosis. 2) Clinical outcome prediction. 3) Deep learning. 4) Integration of multiple omics studies.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Screening and diagnosis\u003c/h2\u003e \u003cp\u003eRadiomics can assist in lung cancer screening and classify subtypes of lung cancer. The NLST in the U.S. and the European NELSON trial demonstrated that LDCT can greatly lower the risk of dying from lung cancer (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, LDCT still poses the problem of low specificity (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), often leading to false-positive cases, and there are many indeterminate nodules that need to be further biopsied to distinguish benign or malignant. Radiomics can non-invasively differentiate benign from malignant tumors. According to a recent study conducted by Warkentin et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), three ML models were employed to forecast the malignancy risk of lung nodules, achieving an AUC of 0.93. In another study, Lin et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) proposed a combined model to classify lung nodules into benign or malignant using radiomics and deep learning, achieving an accuracy of 92.8%. They proved the potential of radiomics in classifying lung nodules into benign or malignant. Radiomics has also made new progress in distinguishing subtypes of lung cancer. Dunn et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) combined artificial intelligence and radiomics to automatically classify three histological subtypes of lung cancer (adenocarcinoma, small cell carcinoma, and squamous cell carcinoma), achieving an AUC of 0.97 AUC. The predictive performance of the radiomics model has significantly improved compared to previous studies (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Besides, radiomics has also extended to predict rare lung cancer subtypes (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRadiomics can also provide additional information for clinical decision-making in tumor staging. Chen et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) proposed a multi-task learning-based radiomics model that can automatically classify lung cancer tumor subtypes and perform tumor staging, achieving an AUC of 0.843. Recently, Song et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) accurately predicted the distant metastasis of non-small cell lung cancer using the combined method of quantitative image analysis and deep learning, achieving an AUC of 0.893. In addition, there are also radiomics studies to evaluate the mediastinal lymph node situation (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Overall, radiomics plays an important role in the precise diagnosis of lung cancer.\u003c/p\u003e \u003cp\u003eAs the dual map of journal shown in Fig.\u0026nbsp;4e, the study of radiomics and molecular characteristics is also at the forefront of research in this field. Radiomics has also shown good efficacy in predicting lung cancer genotypes. Most radiomics studies in lung cancer focus on predicting EGFR/ALK/ROS1 mutations in lung adenocarcinoma. The latest CT radiomics-based model (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) for EGFR mutation prediction has achieved an AUC of 0.877, using both intratumoral and peritumoral features. Ninomiya et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) further investigated the prognostic significance of EGFR Del19 and L858R mutation subtypes, achieving an accuracy of 81%. In addition, An AUC of 0.914 was achieved by the leading model based on CT images and clinical data (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) for determining ALK rearrangement status. In the latest study, Zhang et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) used a multiscale radiomics approach to distinguish six genotypes and infiltrative immune phenotypes (EGFR, KRAS, ALK, TP53, PIK3CA, and ROS1 mutation status), achieving AUCs of 0.866, 0.874, 0.902, 0.850, 0.860, and 0.900, providing a feasible method for developing personalized treatment plans for patients. There are also radiomics studies targeting the prediction of PD-L1 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), tumor mutation burden status (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), and Ki-67 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) in lung cancer. As more multicenter studies are conducted, the prediction of tumor genotypes will become more accurate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Clinical outcome prediction\u003c/h2\u003e \u003cp\u003eRadiomics can assist in predicting treatment responses, adverse reactions, and disease recurrence in medical decision-making for precision medicine. Through the analysis of keywords in the previous sections of this article, we found that radiomics prediction for immunotherapy is currently a research hotspot. Radiomics can serve as an effective biomarker to forecast lung cancer immunotherapy outcomes. Machine learning-based radiomics has been effectively applied to forecast the response to immunotherapy in lung cancer (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Delta-radiomics can reflect temporal heterogeneity of cancers, and has been used to predict clinical outcomes in lung cancer (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Wu et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) have proposed a deep learning model for predicting lung cancer response to immunotherapy, achieving an AUC of 0.778. For radiomics prediction of chemotherapy efficacy, a deep radiomic model (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) based on intratumoral and peritumoral features was built to predict the response to chemotherapy in lung cancer, achieving an AUC of 0.96. For radiomics prediction of radiotherapy efficacy, Lucia et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e) developed machine learning models for the prediction of regional and/or distant recurrence in lung cancer, with C-statistics from 0.53 to 0.59. Regarding the prediction of response to combination therapy, a recent radiomics nomogram (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) provided an efficient model to predict chemo-immunotherapy in advanced LC patients, achieving an AUC of 0.85. In addition, there were several radiomics researches (\u003cspan additionalcitationids=\"CR58 CR59\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) on neoadjuvant chemotherapy combined with immunotherapy for lung cancer, to assist in treatment decisions, achieving the goal of precise individualized medicine. Multicenter collaboration and prospective validation are future development trends to improve the predictive efficacy of treatment response prediction.\u003c/p\u003e \u003cp\u003eAdverse reactions that may be caused by radiotherapy can be detected through radiomics, and correlates with dosiomics. Huang et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e) established radiomics models based on dosiomics and deep learning features to predict radiation pneumonitis after radiation therapy, achieving an AUC of 0.9986. Immune-related adverse events (AEs) are also a common challenge we encounter in clinical practice. In the research on forecasting immune-related adverse events in LC patients (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), a nomogram model was developed from PET/CT images, achieving an AUC of 0.92. Moreover, a multicenter retrospective study (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) was conducted to checkpoint inhibitor-related pneumonitis from radiation pneumonitis and the CT-based radiomics model had a good classification function, achieving an AUC of 0.859.\u003c/p\u003e \u003cp\u003eRadiomics has been applied to predict lung cancer survival prognosis for a long time (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In a retrospective study (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) including 976 patients who were treated with immunotherapy at MD Anderson and Stanford, Deep-CT model was used to predict overall survival and progression-free survival, achieving overall C-statistics from 0.70 to 0.75. Many other studies on radiomics to predict the overall survival (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) or progression-free survival (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) of lung cancer immunotherapy have also been reported. Radiomics can also indirectly predict survival prognosis by predicting biomarkers related to prognosis. Meng et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) developed a CT-based radiomics model to forecast the infiltration of natural killer cells and shown that a larger NK cell infiltration was associated with improved overall survival (OS), with an AUC of 0.731. Chen et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) established two radiomics models to predict expression levels of CD3 and CD8 T lymphocytes in lung cancer, achieving an AUC of 0.943. According to the bibliometric analysis in the earlier part of our article, an increasing number of radiomics studies will be applied to lung cancer clinical practice, providing more effective assistance for precision treatment of lung cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Deep learning\u003c/h2\u003e \u003cp\u003eDeep learning is a sub-branch of machine learning (ML), which was first introduced by Hinton in 2006 (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Deep learning methods for lung cancer mainly include classification, detection and segmentation. For example, deep learning can assist in the classification of benign and malignant lung nodules (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Compared to traditional radiomics, deep learning has two main advantages: 1) Deep learning can assist doctors in automatically segmenting lesions (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), significantly reducing the workload of manual delineation and minimizing the errors caused by manual sketching. 2) Using deep learning models can extract deeper-level features that cannot be obtained through manual extraction (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). The end-to-end models created by deep learning can also provide more clinical significance for the application of radiomics in lung cancer. With the maturity of artificial intelligence technology, by improving deep learning algorithms, more opportunities will be provided for lung cancer radiomics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.4 Integration of multiple omics studies\u003c/h2\u003e \u003cp\u003eRadiomics features combined with clinical features have been applied to predict the efficacy of lung cancer treatment (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) and survival prognosis (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e), to enhance model performance. Similarly, Radiogenomics can improve predictive performance by combining radiomics and genomics. Chen et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) established a radiogenomics model to predict the response and pneumotoxicity to immunotherapy in lung cancer, with an AUC of 0.70. By integrating radiomics with transcriptomics using gene set enrichment analysis (GSEA), biological relevance of radiomics models can be further analyzed (\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigh-throughput quantitative features can be extracted from medical images using radiomics, providing a reference for accurate tumor diagnosis and prognosis prediction. However, radiomics aims to describe tumors from a macroscopic perspective and is essentially data-driven. The underlying biological significance remains unclear. Pathomics uses similar high-throughput image feature extraction techniques to convert whole-slide digital pathology slides into quantitative datasets, objectively reflecting tumor cell and tissue structure information at the microscopic level. It can more efficiently assist in tumor pathological diagnosis, treatment efficacy, and prognosis prediction. Therefore, the combination of radiomics and pathomics, with the help of artificial intelligence methods, can fully integrate macroscopic and microscopic features, which is expected to provide new ideas for accurate tumor diagnosis and treatment. The radiomics combined with pathology model is widely used for prognosis prediction in tumors such as gastric cancer (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), rectal cancer (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e), breast cancer (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e) and nasopharyngeal carcinoma (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). However, there have been no relevant reports in the field of lung cancer radiomics prognosis, and it is a promising research direction in the future. The interconnectivity between multimodal data can be further explored as a development trend in lung cancer radiomics.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5 Limitations","content":"\u003cp\u003eHowever, our study still has some limitations. First, the limitations of bibliometric analysis software mean that our data were only from the WoSCC database. Second, several significant non-English works might have been overlooked, as the study solely looked at English-language works. Finally, due to the dynamic nature of the database, some high-quality recently published studies may be undervalued for their low citation frequency.\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn summary, our study comprehensively and quantitatively presents the research status, research hotspots and trends in this field's development. Our results show that the application of radiomics in lung cancer is a highly promising research area. Multicenter studies are a trend in the development of lung cancer radiomics, and we advocate strengthening cooperation between countries/regions, institutions, and authors to break down academic barriers. The research hotspot of lung cancer radiomics is more inclined towards clinical applications, including screening, diagnosis and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend. Furthermore, deep learning can provide strong technical support for radiomics. Multimodal learning for information fusion is another crucial development trend, we should pay more attention to multi-omics integration in the future.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eNames of authors:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiangbo He\u003csup\u003e1\u003c/sup\u003e, Chaoyuan Liu\u003csup\u003e1\u003c/sup\u003e (corresponding author), Fang Ma\u003csup\u003e1\u003c/sup\u003e, Yiguang Zhou\u003csup\u003e1\u003c/sup\u003e, Xianling Liu\u003csup\u003e1,2\u003c/sup\u003e (corresponding author)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmail Address of the Corresponding Author:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChaoyuan Liu: [email protected]\u003c/p\u003e\n\u003cp\u003eXianling Liu: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliations and addresses:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Oncology, Guilin Hospital of the Second Xiangya Hospital, Central South University, Guilin, China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Hunan Province (No. 2023JJ40837 and No. 2022JJ40704), Beijing Science and Technology Innovation Medical Development Foundation (No. KC2023-JX-0288-PM37), Education and Teaching Reform Research Project of Central South University (No.2022jy197).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve human and/ or animal studies. Therefore, ethical approval does not apply to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eStudy conception and design: Jiangbo He, Chaoyuan Liu, and Xianling Liu. Data collection and data curation: Jiangbo He and Yiguang Zhou. Data analysis and visualization: Jiangbo He.Interpretation of data: Chaoyuan Liu, Fang Ma, and Xianling Liu. Writing\u0026mdash;original draft \u0026amp; editing: Jiangbo He. Writing\u0026mdash;Review: Chaoyuan Liu, Fang Ma, and Xianling Liu. Funding acquisition: Chaoyuan Liu and Xianling Liu.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-a Cancer Journal for Clinicians. 2024;74(3):229-63.\u003c/li\u003e\n\u003cli\u003eJemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, et al. Cancer statistics, 2008. CA: a cancer journal for clinicians. 2008;58(2):71-96.\u003c/li\u003e\n\u003cli\u003eNwogu I, Corso JJ. Exploratory identification of image-based biomarkers for solid mass pulmonary tumors. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2008;11(Pt 1):612-9.\u003c/li\u003e\n\u003cli\u003eLambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. 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Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology. 2024;191:110082.\u003c/li\u003e\n\u003cli\u003eHashimoto K, Murakami Y, Omura K, Takahashi H, Suzuki R, Yoshioka Y, et al. Prediction of Tumor PD-L1 Expression in Resectable Non-Small Cell Lung Cancer by Machine Learning Models Based on Clinical and Radiological Features: Performance Comparison With Preoperative Biopsy. Clinical lung cancer. 2024;25(1):e26-e34.e6.\u003c/li\u003e\n\u003cli\u003eYang J, Shi W, Yang Z, Yu H, Wang M, Wei Y, et al. Establishing a predictive model for tumor mutation burden status based on CT radiomics and clinical features of non-small cell lung cancer patients. Translational lung cancer research. 2023;12(4):808-23.\u003c/li\u003e\n\u003cli\u003eLiu F, Li Q, Xiang Z, Li X, Li F, Huang Y, et al. CT radiomics model for predicting the Ki-67 proliferation index of pure-solid non-small cell lung cancer: a multicenter study. Frontiers in oncology. 2023;13:1175010.\u003c/li\u003e\n\u003cli\u003eLiu Y, Wu M, Zhang Y, Luo Y, He S, Wang Y, et al. Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Frontiers in oncology. 2021;11:657615.\u003c/li\u003e\n\u003cli\u003eMu W, Tunali I, Gray JE, Qi J, Schabath MB, Gillies RJ. Radiomics of (18)F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. European journal of nuclear medicine and molecular imaging. 2020;47(5):1168-82.\u003c/li\u003e\n\u003cli\u003eCousin F, Louis T, Dheur S, Aboubakar F, Ghaye B, Occhipinti M, et al. Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors. Cancers. 2023;15(7).\u003c/li\u003e\n\u003cli\u003eWu Q, Wang J, Sun Z, Xiao L, Ying W, Shi J. Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks. IEEE journal of biomedical and health informatics. 2023;27(11):5564-75.\u003c/li\u003e\n\u003cli\u003eChang R, Qi S, Wu Y, Yue Y, Zhang X, Guan Y, et al. Deep radiomic model based on the sphere-shell partition for predicting treatment response to chemotherapy in lung cancer. Translational oncology. 2023;35:101719.\u003c/li\u003e\n\u003cli\u003eLucia F, Louis T, Cousin F, Bourbonne V, Visvikis D, Mievis C, et al. Multicentric development and evaluation of [(18)F]FDG PET/CT and CT radiomic models to predict regional and/or distant recurrence in early-stage non-small cell lung cancer treated by stereotactic body radiation therapy. European journal of nuclear medicine and molecular imaging. 2024;51(4):1097-108.\u003c/li\u003e\n\u003cli\u003eLiu C, Zhao W, Xie J, Lin H, Hu X, Li C, et al. 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Therapeutic advances in medical oncology. 2020;12:1758835920971416.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Table of country published literature\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCountry/region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eArticle counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003ecentrality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eCitation per publication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e47.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e16.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e25.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e42203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e79.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eITALY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e9.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e43.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eNETHERLANDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e6.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e184.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eFRANCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e5.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e56.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eGERMANY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e44.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eCANADA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e120.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eENGLAND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e4.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e72.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eSOUTH KOREA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e24.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eJAPAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e3.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e17.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Table of Institutional Published Literature\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eInstitution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003eNumber of studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eTotal citations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eAverage citation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eGeneral Electric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e11.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eMaastricht University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e22573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e278.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eFudan University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e12.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eShandong First Medical University \u0026amp; Shandong Academy of Medical Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e12.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eHarvard University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e24172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e322.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eChinese Academy of Sciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e35.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eH Lee Moffitt Cancer Center \u0026amp; Research Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e16966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e265.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eUniversity of Texas System\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e64.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eInstitut National de la Sante et de la Recherche M\u0026eacute;dicale (Inserm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e80.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 221px;\"\u003e\n \u003cp\u003eShanghai Jiao Tong University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e18.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Table of Journal Publications\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eArticle counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eIF\u003c/p\u003e\n \u003cp\u003e(2024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQuartile in category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003efrontiers in oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003escientific reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003ecancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4.13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eeuropean radiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003emedical physics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eacademic radiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003ephysics in medicine and biology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eeuropean journal of nuclear medicine and molecular imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003etranslational lung cancer research\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eclinical radiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 4. Co-citation table of journals\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"554\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eCited Journal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCo-Citation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eIF（2024）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQuartile in category\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eRADIOLOGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eSCI REP-UK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eEUR RADIOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e1257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003ePLOS ONE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eFRONT ONCOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eJ THORAC ONCOL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e21.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eMED PHYS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eLUNG CANCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eNAT COMMUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eEUR J CANCER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5. Author\u0026apos;s publications and co-citation table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003eCount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCo-cited author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eAerts, Hugo j. W. L.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLambin P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eGillies, Robert j.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAerts Hjwl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e729\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eLambin, Philippe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eGillies Rj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eSchabath, Matthew b.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eVan Griethuysenjjm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eLee, ho Yun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eZwanenburg a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eTian, Jie\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eParmar C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eDekker, Andre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCoroller Tp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eSchwartz, Lawrence h.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eKumar V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eMadabhushi, Anant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLiuY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 131px;\"\u003e\n \u003cp\u003eLeijenaar, ralph t. H.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSiegel Rl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6. Co-citation table of literature\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"560\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eauthor(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eTotal citations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRadiomics: Images Are More than Pictures, They Are Data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eRADIOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eGillies RJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eThe Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eRADIOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eZwanenburg A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eComputational Radiomics System to Decode the Radiographic Phenotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eCANCER RESEARCH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003evan Griethuysen JJM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRadiomics: the bridge between medical imaging and personalized medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eNATURE REVIEWS CLINICAL ONCOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eLambin P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eNATURE COMMUNICATIONS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eAerts HJWL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRadiomics and radiogenomics in lung cancer: A review for the clinician\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eLUNG CANCER\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eThawani R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eA radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eLANCET ONCOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eSun R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eCT-based radiomic signature predicts distant metastasis in lung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eRADIOTHERAPY AND ONCOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eCoroller TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003ePredicting response to cancer immunotherapy using noninvasive radiomic biomarkers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eANNALS OF ONCOLOGY\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eTrebeschi S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eRepeatability and Reproducibility of Radiomic Features: A Systematic Review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cem\u003eINTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003eTraverso A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 High Frequency Keyword Table\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eKeyword\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eCounts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eKeyword\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eCounts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eradiomics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eheterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003efeatures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003edeep learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eimages\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ediagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eradiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003esurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003ecomputed-tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003emachine learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003esignature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eclassification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003epet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003etexture analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003epulmonary nodules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eprediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eimmunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ecomputed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eartificial intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Radiomics, Lung cancer, Deep learning, Bibliometric analysis, VOSviewer, CiteSpace","lastPublishedDoi":"10.21203/rs.3.rs-6409960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6409960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe main cause of cancer-related deaths around the world is lung cancer. Therefore, the diagnosis and treatment of lung cancer make up the majority of clinical research focused on cancer. In recent years, there have been significant advancements in the application of radiomics in lung cancer. However, there are no studies on global research trends in the application of radiomics in lung cancer. To address this gap, this study investigates the current state of research and key application areas of radiomics in lung cancer, while predicting future research directions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOn 21 October, 2024, we identified 2057 papers on the application of radiomics in lung cancer from the Web of Science database Core Collection database. In order to examine and graph trends and proportions of publications by country, GraphPad Prism software was used. CiteSpace and VOSviewer were used to visualize and analyze the papers published from 1 January 2010 to 21 October 2024.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe collection included 2057 papers published from 2010 to 2024, of which most were articles (1734, 84.30%) and a few were reviews (323, 15.70%), contributed by 9539 authors from 61 countries/regions. There was an upward trend in both the number of publications per year and the total number of citations. China, accounting for 47.79% with 983 papers, and the USA, accounting for 25.86% with 532 papers, have made notable contributions in this domain. General Electric was the most productive institution (n\u0026thinsp;=\u0026thinsp;86). Lambin (n\u0026thinsp;=\u0026thinsp;919 citations) was the most co-cited author, whereas Aerts, Hugo J. W. L., was placed first among the top ten authors. The most published journal was Frontiers in Oncology (178 publications; IF 2023, 3.5; Q2). It is important for different countries and institutions to strengthen their cooperation in the future. Radiomics, features, images, CT, and survival were the most commonly used keywords. The analysis of references and keywords shows that the research hotspot of lung cancer radiomics is more inclined towards clinical applications. In the future, radiomics was mainly used for the classification, diagnosis, detection, and prediction of lung cancer, especially in immunotherapy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn summary, the bibliometric analysis comprehensively and quantitatively presents the research status, research hotspots, and development trends of radiomics applied in lung cancer. The application of radiomics to lung cancer is a highly promising research area based on our results. Multicenter studies are a trend in the development of lung cancer radiomics, and we advocate strengthening cooperation between countries/regions, institutions, and authors to break down academic barriers. The research hotspot of lung cancer radiomics is more inclined towards clinical applications, including screening, diagnosis, and prediction of clinical outcome. Immunotherapy is currently a hot research area in this field, and the efficacy and prognosis of personalized immunotherapy for lung cancer is the future development trend. Furthermore, deep learning can provide strong technical support for radiomics. Multimodal learning for information fusion is another crucial development trend; we should pay more attention to multi-omics integration in the future.\u003c/p\u003e","manuscriptTitle":"Worldwide research landscape of radiomics in lung cancer: A scientometric study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 10:38:31","doi":"10.21203/rs.3.rs-6409960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c40a7f56-2a49-4b6e-aba7-217cddba8394","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T06:24:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 10:38:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6409960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6409960","identity":"rs-6409960","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}