Radiology Journals Research Articles

Research status and hotspots in transjugular intrahepatic portosystemic shunts based on CiteSpace bibliometric analysis.

The transjugular intrahepatic portosystemic shunt (TIPS) is an important technique for treating complications related to portal hypertension in patients with cirrhosis, and the number of publications in the TIPS field continues to rise. To facilitate an understanding of the research status and hotspots in the field of TIPS using CiteSpace bibliometric analysis. CiteSpace is a software that depicts the strength of relationships through graphics and connections with diverse functionalities and can be used to analyze the status and hotspots of areas of research. Articles on TIPS in the Web of Science Core Collection were retrieved, and CiteSpace software was used to visualize and analyze the number of publications, journals, countries, institutions, authors, keywords, and citations. A total of 985 relevant documents were included in the analysis. From January 2013 to December 2022, the number of publications increased annually. The journal, institution, and author with the greatest number of publications in the field of TIPS are the Journal of Vascular and Interventional Radiology, the University of Bonn, and Jonel Trebicka, respectively. The main keywords used in this field are "transjugular intrahepatic portosystemic shunt", "portal hypertension", "cirrhosis", "management", "stent", "hepatic encephalopathy", "refractory ascite", "survival", "risk", and "variceal bleeding". The greatest obstacle to TIPS placement is currently the occurrence of hepatic encephalopathy. The research hotspots are the mechanism, risk factors, management, and control of hepatic encephalopathy. This bibliometric analysis reported the research status and hotspots of TIPS. Research on postoperative hepatic encephalopathy is the research hotspot in this field.

Amplifying Research: The Potential for Podcasts to Boost Radiology Journal Article Exposure.

Background Podcasts have become an increasingly popular method of communicating information in medicine, including in radiology. However, the effect of podcasts on the reach of journal articles remains unclear. Purpose To evaluate the influence of Radiology podcasts on the performance metrics, including downloads, citations, and Altmetric Attention Score (AAS), of Radiology articles. Materials and Methods This was a retrospective study. All articles published in the print version of Radiology from January 2021 to December 2022 were reviewed; editorials and case reports were excluded. Articles featured on Radiology podcasts were included in the podcast group. Articles published within the same journal issue and category were the nonpodcast group. Downloads, Google Scholar citations, Dimensions citations, and AAS metrics were recorded. The Mann-Whitney U test was used to compare medians and evaluate differences between older and more recently published articles. Results The podcast group, composed of 88 articles, exhibited significantly higher median values for downloads (PG, 4521.0; nonpodcast group, 2123.0; P < .001), Google Scholar citations (podcast group, 14.5; nonpodcast group, 10.0; P = .01), Dimensions citations (podcast group, 12.0; nonpodcast group, 9.0; P = .01), and AAS (podcast group, 43.0; nonpodcast group, 10.0; P < .001) compared with the nonpodcast group of 378 articles. Within both groups, articles published in the earlier nonpodcast group (January to June 2021) had higher downloads (podcast group, P = .08; nonpodcast group, P < .001), Google Scholar citations (podcast group and nonpodcast group, P < .001), and Dimension citations (podcast group and nonpodcast group, P < .001) than articles from the later period (July to December 2022). AAS markedly increased in recent podcast articles (P = .03), but AAS for nonpodcast articles significantly decreased over time (P = .01). Conclusion Radiology articles featured on the Radiology podcast had greater median metrics, including downloads, Google Scholar citations, Dimensions citations, and AAS, compared with nonpodcast articles, suggesting that podcasts can be an effective method of disseminating and amplifying research within the field of radiology. © RSNA, 2024 See also the editorial by Chu and Nicola in this issue.

Trends and hotspots of energy-based imaging in thoracic disease: a bibliometric analysis

ObjectiveTo conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention.MethodsThe Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source’s production over time, corresponding author’s countries, and a treemap of authors’ keywords.ResultsA total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the “European Radiology” (181 articles, h-index = 46), followed by the “European Journal of Radiology” (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest.ConclusionOur study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future.Critical relevance statementThis study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice.Key PointsBibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT.Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose.All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.Graphical

The policies on the use of large language models in radiological journals are lacking: a meta-research study

ObjectiveTo evaluate whether and how the radiological journals present their policies on the use of large language models (LLMs), and identify the journal characteristic variables that are associated with the presence.MethodsIn this meta-research study, we screened Journals from the Radiology, Nuclear Medicine and Medical Imaging Category, 2022 Journal Citation Reports, excluding journals in non-English languages and relevant documents unavailable. We assessed their LLM use policies: (1) whether the policy is present; (2) whether the policy for the authors, the reviewers, and the editors is present; and (3) whether the policy asks the author to report the usage of LLMs, the name of LLMs, the section that used LLMs, the role of LLMs, the verification of LLMs, and the potential influence of LLMs. The association between the presence of policies and journal characteristic variables was evaluated.ResultsThe LLM use policies were presented in 43.9% (83/189) of journals, and those for the authors, the reviewers, and the editor were presented in 43.4% (82/189), 29.6% (56/189) and 25.9% (49/189) of journals, respectively. Many journals mentioned the aspects of the usage (43.4%, 82/189), the name (34.9%, 66/189), the verification (33.3%, 63/189), and the role (31.7%, 60/189) of LLMs, while the potential influence of LLMs (4.2%, 8/189), and the section that used LLMs (1.6%, 3/189) were seldomly touched. The publisher is related to the presence of LLM use policies (p < 0.001).ConclusionThe presence of LLM use policies is suboptimal in radiological journals. A reporting guideline is encouraged to facilitate reporting quality and transparency.Critical relevance statementIt may facilitate the quality and transparency of the use of LLMs in scientific writing if a shared complete reporting guideline is developed by stakeholders and then endorsed by journals.Key PointsThe policies on LLM use in radiological journals are unexplored.Some of the radiological journals presented policies on LLM use.A shared complete reporting guideline for LLM use is desired.Graphical

A Lexicon for First-Trimester US: Society of Radiologists in Ultrasound Consensus Conference Recommendations.

The Society of Radiologists in Ultrasound convened a multisociety panel to develop a first-trimester US lexicon based on scientific evidence, societal guidelines, and expert consensus that would be appropriate for imagers, clinicians, and patients. Through a modified Delphi process with consensus of at least 80%, agreement was reached for preferred terms, synonyms, and terms to avoid. An intrauterine pregnancy (IUP) is defined as a pregnancy implanted in a normal location within the uterus. In contrast, an ectopic pregnancy (EP) is any pregnancy implanted in an abnormal location, whether extrauterine or intrauterine, thus categorizing cesarean scar implantations as EPs. The term pregnancy of unknown location is used in the setting of a pregnant patient without evidence of a definite or probable IUP or EP at transvaginal US. Since cardiac development is a gradual process and cardiac chambers are not fully formed in the first trimester, the term cardiac activity is recommended in lieu of 'heart motion' or 'heartbeat.' The terms 'living' and 'viable' should also be avoided in the first trimester. 'Pregnancy failure' is replaced by early pregnancy loss (EPL). When paired with various modifiers, EPL is used to describe a pregnancy in the first trimester that may or will not progress, is in the process of expulsion, or has either incompletely or completely passed. © RSNA and Elsevier, 2024 Supplemental material is available for this article. This article is a simultaneous joint publication in Radiology and American Journal of Obstetrics & Gynecology. All rights reserved. The articles are identical except for minor stylistic and spelling differences in keeping with each journal's style. Either version may be used in citing this article. See also the editorial by Scoutt and Norton in this issue.

Assessing sex and gender equity in submission guidelines of radiology journals: A cross-sectional study

PurposeOur study aimed to determine the current percentage of gender and sex equity promoting (GSEP) radiology journals, defined as satisfying at least one criterion of the Sex and Gender Equity in Research (SAGER) checklist, published by the European Association of Science Editors (EASE). A secondary objective was to compare characteristics of GSEP and non-GSEP journals. MethodsA cross-sectional analysis between June 24 and July 3, 2023, was conducted. The author submission guidelines of radiology journals with a 2021 Journal Impact Factor (JIF) were assessed according to the SAGER checklist. GSEP journals were defined as satisfying one or more SAGER checklist criteria in their research instructions. Bibliometric data and journal information were collected from the Journal Citation Reports and National Library of Medicine catalogue. ResultsOnly 39.7 % (52) of 132 journals satisfied at least one SAGER checklist criterion. Median 2021 JIFs were higher in GSEP journals (4.62, IQR: 3.73 – 5.21) than non-GSEP journals (2.70, IQR: 2.32) (p = 0.00). Median 2021 Journal Citation Index (JCI) scores were higher in GSEP (0.64, 0.56 – 0.73) than non-GSEP journals (0.97, 0.83 – 1.10) (p = 0.00). Cited half-life was shorter for GSEP (5.40, 4.80 – 6.50) than non-GSEP journals (6.70, 5.70 – 7.40) (p = 0.05). Elsevier published 33 of 52 of GSEP journals. Conclusion60.3% of radiology journals with a 2021 JIF do not meet a single SAGER checklist criterion in their author submission guidelines. GSEP journals had higher impact and source metrics and a shorter cited half-life. Publishers may play a significant role in promoting endorsement of the SAGER checklist in the author submission guidelines of radiology journals.

Artificial intelligence meets medical expertise: evaluating GPT-4's proficiency in generating medical article abstracts

Purpose: The advent of large language models like GPT-4 has opened new possibilities in natural language processing, with potential applications in medical literature. This study assesses GPT-4's ability to generate medical abstracts. It compares their quality to original abstracts written by human authors, aiming to understand the effectiveness of artificial intelligence in replicating complex, professional writing tasks. Materials and Methods: A total of 250 original research articles from five prominent radiology journals published between 2021 and 2023 were selected. The body of these articles, excluding the abstracts, was fed into GPT-4, which then generated new abstracts. Three experienced radiologists blindly and independently evaluated all 500 abstracts using a five-point Likert scale for quality and understandability. Statistical analysis included mean score comparison inter-rater reliability using Fleiss' Kappa and Bland-Altman plots to assess agreement levels between raters. Results: Analysis revealed no significant difference in the mean scores between original and GPT-4 generated abstracts. The inter-rater reliability yielded kappa values indicating moderate to substantial agreement: 0.497 between Observers 1 and 2, 0.753 between Observers 1 and 3, and 0.645 between Observers 2 and 3. Bland-Altman analysis showed a slight systematic bias but was within acceptable limits of agreement. Conclusion: The study demonstrates that GPT-4 can generate medical abstracts with a quality comparable to those written by human experts. This suggests a promising role for artificial intelligence in facilitating the abstract writing process and improving its quality.

Medical magnetic resonance imaging publications in Arab countries: A 25-year bibliometric analysis

Magnetic Resonance Imaging (MRI) is increasingly becoming a cornerstone in modern diagnostic healthcare, offering unparalleled capabilities in stroke, dementia, and cancer screening. Therefore, this study aims to map medical MRI literature affiliated with Arab countries, focusing on publication trends, top journals, author affiliations, study countries, and authors' collaboration, and keyword analysis. The scientific database used is the Scopus database. Microsoft Excel, VOSviewer software, and Biblioshiny for the Bibliometrix R package are the bibliometric tools used in this analysis. A total of 2592 publications were published between 1988 and 2022, with total citations of 22,115. Most of them were original articles (91,7%) and 89.9% were published in traditional journals. The number of total publications exhibited a steady increase over time, whereas total citations showed fluctuations, peaking in 2015 with 1571 citations for publications from that year. The most cited article was authored by Yaseen M. Arabi, receiving 286 citations. Saudi Arabia was the top active country. In addition, the most prolific author was Maha S Zaki, and the most prolific source was the “Egyptian Journal of Radiology and Nuclear Medicine”. The most prolific affiliation was Cairo University. The “multiple sclerosis” and “case report” were the most trending keywords. The analysis revealed a significant growth in MRI research inside Arab countries, as shown by an increase in the total number of publications and international collaborations. Despite these developments, the results of this study suggest that there is still room for MRI research in the Arab region to advance. This can be achieved through increasing international collaboration and multidisciplinary work.

Step into the era of large multimodal models: a pilot study on ChatGPT-4V(ision)'s ability to interpret radiological images.

The introduction of ChatGPT-4V's 'Chat with images' feature represents the beginning of the era of large multimodal models (LMMs), which allows ChatGPT to process and answer questions based on uploaded images. This advancement has the potential to transform how surgical teams utilize radiographic data, as radiological interpretation is crucial for surgical planning and postoperative care. However, a comprehensive evaluation of ChatGPT-4V's capabilities in interpret radiological images and formulating treatment plans remains to be explored. Three types of questions were collected: (1) 87 USMLE-style questions, submitting only the question stems and images without providing options to assess ChatGPT's diagnostic capability. For questions involving treatment plan formulations, a five-point Likert scale was used to assess ChatGPT's proposed treatment plan. The 87 questions were then adapted by removing detailed patient history to assess its contribution to diagnosis. The diagnostic performance of ChatGPT-4V was also tested when only medical history was provided. (2) We randomly selected 100 chest radiography from the ChestX-ray8 database to test the ability of ChatGPT-4V to identify abnormal chest radiography. (3) Cases from the 'Diagnose Please' section in the Radiology journal were collected to evaluate the performance of ChatGPT-4V in diagnosing complex cases. Three responses were collected for each question. ChatGPT-4V achieved a diagnostic accuracy of 77.01% for USMLE-style questions. The average score of ChatGPT-4V's treatment plans was 3.97 (Interquartile Range: 3.33-4.67). Removing detailed patient history dropped the diagnostic accuracy to 19.54% (P<0.0001). ChatGPT-4V achieved an AUC of 0.768 (95% CI: 0.684-0.851) in detecting abnormalities in chest radiography, but could not specify the exact disease due to the lack of detailed patient history. For cases from 'Diagnose Please' ChatGPT provided diagnoses consistent with or very similar to the reference answers. ChatGPT-4V demonstrated an impressive ability to combine patient history with radiological images to make diagnoses and directly design treatment plans based on images, suggesting its potential for future application in clinical practice.

Publication pressure in medical imaging

PurposeTo investigate the degree of perceived publication pressure in medical imaging. MethodCorresponding authors who published an article in one of the top 12 general radiology journals were invited to complete a survey about publication pressure. The revised Publication Pressure Questionnaire (PPQr) was used. Higher PPQr scores (5-point Likert scale) indicate a more negative view towards the various domains of publication pressure. Results203 corresponding authors participated. Median PPQr scores in the domains “publication stress”, “publication attitude”, and “publication resources” were 3.33, 3.50, and 3.67, respectively. Age 25–34 years (β coefficient 0.366, P = 0.047), female gender (β coefficient 0.293, P = 0.020), and 5–10 years of research experience (β coefficient 0.370, P = 0.033) were associated with a higher level of perceived publication stress, whereas age ≥ 65 years was negatively associated with perceived publication stress (β coefficient −0.846, P < 0.001). Age 55–64 years and age > 65 years were associated with a more positive view towards the publication climate (β coefficients −0.391 and −0.663, P = 0.018 and P = 0.002, respectively). Age 45–54 years was associated with a perception of fewer factors available to alleviate publication pressure (β coefficient 0.301, P = 0.014), whereas age 25–34 years was associated with a perception of more factors available to alleviate publication pressure (β coefficient −0.352, P = 0.012). ConclusionPerceived publication pressure among medical imaging researchers appears to be appreciable and is associated with several (academic) demographics.

Accuracy of Financial Disclosures in Radiology Journals

PurposeThe accuracy and completeness of self-disclosures of the value of industry payments by authors publishing in radiology journals are not well known. The aim of this study was to assess the accuracy of financial disclosures by US authors in five prominent radiology journals. MethodsFinancial disclosures provided by US-based authors in five prominent radiology journals from original research and review articles published in 2021 were reviewed. For each author, payment reports were extracted from the Open Payments Database (OPD) in the previous 36 months related to general, research, and ownership payments. Each author was analyzed individually to determine if the reported disclosures matched results from the OPD. ResultsA total of 4,076 authorships, including 3,406 unique authors, were selected from 643 articles across the five journals; 1,388 (1,032 unique authors) received industry payments within the previous 36 months, with a median total amount received per authorship of $6,650 (interquartile range, $355-$87,725). Sixty-one authors (4.4%) disclosed all industry relationships, 205 (14.8%) disclosed some of the OPD-reported relationships, and 1,122 (80.8%) failed to disclose any relationships. Undisclosed payments totaled $186,578,350, representing 67.2% of all payments. Radiology had the highest proportion of authorships disclosing some or all OPD-reported relationships (32.3%), compared with the Journal of Vascular and Interventional Radiology (18.2%), the American Journal of Neuroradiology (17.3%), JACR (13.1%), and the American Journal of Roentgenology (10.3%). ConclusionsFinancial relationships with industry are common among US physician authors in prominent radiology journals, and nondisclosure rates are high.

The Role of Imaging for GI Bleeding: ACG and SAR Consensus Recommendations.

Gastrointestinal (GI) bleeding is the most common GI diagnosis leading to hospitalization within the United States. Prompt diagnosis and treatment of GI bleeding is critical to improving patient outcomes and reducing high health care utilization and costs. Radiologic techniques including CT angiography, catheter angiography, CT enterography, MR enterography, nuclear medicine red blood cell scan, and technetium-99m pertechnetate scintigraphy (Meckel scan) are frequently used to evaluate patients with GI bleeding and are complementary to GI endoscopy. However, multiple management guidelines exist, which differ in the recommended utilization of these radiologic examinations. This variability can lead to confusion as to how these tests should be used in the evaluation of GI bleeding. In this document, a panel of experts from the American College of Gastroenterology and Society of Abdominal Radiology provide a review of the radiologic examinations used to evaluate for GI bleeding including nomenclature, technique, performance, advantages, and limitations. A comparison of advantages and limitations relative to endoscopic examinations is also included. Finally, consensus statements and recommendations on technical parameters and utilization of radiologic techniques for GI bleeding are provided. © Radiological Society of North America and the American College of Gastroenterology, 2024. Supplemental material is available for this article. This article is being published concurrently in American Journal of Gastroenterology and Radiology. The articles are identical except for minor stylistic and spelling differences in keeping with each journal's style. Citations from either journal can be used when citing this article. See also the editorial by Lockhart in this issue.

The shift to online publishing and promoting innovation in the Hong Kong Journal of Radiology

The shift to online publishing and promoting innovation in the Hong Kong Journal of Radiology

Meta-research on reporting guidelines for artificial intelligence: are authors and reviewers encouraged enough in radiology, nuclear medicine, and medical imaging journals?

To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artificial intelligence (AI) in their author and reviewer instructions. The primary source of journal information and associated citation data used was the Journal Citation Reports (June 2023 release for 2022 citation data; Clarivate Analytics, UK). The first- and second-quartile journals indexed in the Science Citation Index Expanded and the Emerging Sources Citation Index were included. The author and reviewer instructions were evaluated by two independent readers, followed by an additional reader for consensus, with the assistance of automatic annotation. Encouragement and submission requirements were systematically analyzed. The reporting guidelines were grouped as AI-specific, related to modeling, and unrelated to modeling. Out of 102 journals, 98 were included in this study, and all of them had author instructions. Only five journals (5%) encouraged the authors to follow AI-specific reporting guidelines. Among these, three required a filled-out checklist. Reviewer instructions were found in 16 journals (16%), among which one journal (6%) encouraged the reviewers to follow AI-specific reporting guidelines without submission requirements. The proportions of author and reviewer encouragement for AI-specific reporting guidelines were statistically significantly lower compared with those for other types of guidelines (P < 0.05 for all). The findings indicate that AI-specific guidelines are not commonly encouraged and mandated (i.e., requiring a filled-out checklist) by these journals, compared with guidelines related to modeling and unrelated to modeling, leaving vast space for improvement. This meta-research study hopes to contribute to the awareness of the imaging community for AI reporting guidelines and ignite large-scale group efforts by all stakeholders, making AI research less wasteful. This meta-research highlights the need for improved encouragement of AI-specific guidelines in radiology, nuclear medicine, and medical imaging journals. This can potentially foster greater awareness among the AI community and motivate various stakeholders to collaborate to promote more efficient and responsible AI research reporting practices.

Promotional language in radiology publications: increasing use of "excellent", "favorable", "promising", "robust", and "unique".

To investigate if radiology researchers are increasingly promoting their scientific findings by more frequently using positive words in their publications. This study included all articles that were published in 14 general radiology journals between 2003 and 2022. The title and abstract of each article were assessed for the presence of positive, negative, neutral, and random words, according to predefined sets of words for each category. Usage of positive, negative, neutral, and random words was calculated for each year and corrected for the total number of articles in each year. Temporal trends between 2002 and 2023 and the relationship between positive word usage and journal impact factor (IF) were assessed. Positive word usage (Mann-Kendall tau of 0.895, P< 0.001) and neutral word usage (Mann-Kendall tau of 0.463, P = 0.005) showed significant upward temporal trends. Negative word usage and random word usage did not show any significant temporal trends. Five positive words showed significantly increased usage over time and were present in more than 1 % of titles/abstracts in at least one year: "excellent" (Mann-Kendall tau of 0.800, P< 0.001), "favorable" (Mann-Kendall tau of 0.547, P< 0.001), "promising" (Mann-Kendall tau of 0.607, P< 0.001), "robust" (Mann-Kendall tau of 0.737, P< 0.001), and "unique" (Mann-Kendall tau of 0.747, P< 0.001). There was no significant association between positive word usage and journal IF. Radiology researchers appear to increasingly promote their scientific findings by more frequently using positive words in their publications over the past two decades. · Positive word usage in titles/abstracts has strongly increased between 2003-2022. · "Excellent", "favorable", "promising", "robust", and "unique" were most often used. · This trend occurred in all general radiology journals, regardless of impact factor. · Kwee T, Kwee R. Promotional language in radiology publications: increasing use of "excellent", "favorable", "promising", "robust", and "unique". Fortschr Röntgenstr 2024; 196: 945 - 955.

Dilemma with implant placement in patients with florid cemento-osseous dysplasia: A literature review.

An electronic search was done in PUBMED, SCOPUS, and a hand search was done in radiology, periodontology, and oral surgery journals. The search yielded 428 results, from which only 6 articles were selected for this literature review. Both prospective and retrospective studies were included. Clinical studies with information on the pre-implant condition of the site, detailed implant procedure, and follow-up after implant placement of more than 6 months were only considered for this review. Limited clinical studies, shorter follow-up periods were the shortcomings of this review. However, it can be summarized that dental implants should not be placed at the site of FCOD, however can be placed at adjacent sites. Variations in implant type or the implant length had no bearing on the survival of implants at the sites of FCOD.

Impact analysis of primary and secondary research in radiology journals

IntroductionPrimary and secondary studies are considered the two major research categories. In this study, we examined the scientific and social media impact of primary and secondary publication types in papers published radiological journals during 2010–2020. Materials and methodsPubMed publication type tags were used to filter original articles and systematic review and meta-analysis (SR/MA) articles. Clarivate Web of Science was utilized to obtain a list of all radiology journals from the category “Radiology, Nuclear Medicine and Imaging” in Science Citation Index Expanded (SCIE). Automated approach was developed for programmatic extraction of bibliometric and Altmetric yearly citations of each included article using Dimensions API and Altmetric API with Python. Statistical analysis was performed to compare the citation rates between primary and secondary research articles. ResultsA total of 96,684 published articles from 2010 to 2020 were identified and their meta-data collected. The mean 2-year citation count following publication year was 5.8 for primary research and 10.2 for SR/MA articles (p < 0.001). Between 2010 and 2020, the mean number of citations per SR/MA article was 51.3 compared to 30.5 per primary research article (p < 0.001). Mean Altmetric score was 8.2 in SR/MA compared to 3.7 for primary research articles (p < 0.001). ConclusionSecondary research studies have been increasing in impact in both academia and social media compared to primary research. Our results highlight the importance and impact of systematic reviews and meta-analysis articles as a scientifically influential study type in radiology.

Effects of polyphenol supplementation on hepatic steatosis, intima-media thickness and non-invasive vascular elastography in obese adolescents: a pilot study protocol

IntroductionNon-alcoholic fatty liver disease (NAFLD) is increasingly prevalent in obese adolescents. Increased systemic inflammation and decreased gut microbial diversity linked to obesity affect the liver and are also associated with...

Thanks to the Reviewers of the Korean Journal of Radiology

Thanks to the Reviewers of the <i>Korean Journal of Radiology</i>

The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study

BackgroundComplete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables.MethodsThis meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: “active strong”, “active weak”, “passive moderate”, “passive weak”, and “none”. The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis.ResultsWe included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals.ConclusionsThe general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.