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A bibliometric analysis of the 100 most cited radiology papers on pancreatic diseases (1990-Present)
Uffan Zafar1, Muhammad Abdullah2, Talha Nadeem Butt2
1Department of Radiology, University of Texas Medical Branch, Galveston, Texas, USA.
Purpose:
To map the intellectual evolution of pancreatic radiology through a comprehensive bibliometric analysis of the 100 most-cited articles, identifying influential contributors and publications, geographical and institutional patterns, and delineating the thematic and technological trends shaping research from 1990 to the present.
Methods:
A systematic search of the Scopus database, conducted on May 11, 2025, identified the top 1,000 most-cited pancreatic radiology records between 1990 and 2025. Two reviewers independently screened articles for a primary focus on pancreatic imaging within core radiology journals. The final 100 articles were analyzed using the Bibliometrix R package to evaluate publication trends, contributor influence, and collaborative networks. The conceptual structure and thematic evolution of the field were mapped using VOSviewer (version 1.6.20).
Results:
The median citation count was 223 (IQR: 190.2-264.5). A significant temporal bias was evident: the most-cited article was Balthazar et al.'s 1990 study on CT in pancreatitis (1,424 citations), while a 2015 deep learning paper achieved the highest citation velocity (58.5 citations/year). Analysis of research topics revealed a strong oncologic focus, with pancreatic neoplasms accounting for 51% of all articles. CT was the predominant imaging modality (55%), followed by MRI (24%). The research ecosystem was concentrated, with the United States contributing 46 articles and Radiology publishing 44 of the top 100 papers. Leading institutions included Johns Hopkins University and the Mayo Clinic. Co-authorship network analysis identified Megibow AJ as the researcher with the highest network centrality, highlighting the importance of collaborative hubs.
Conclusion:
This bibliometric analysis charts the field's evolution from foundational CT/MRI applications to the current AI frontier. Our findings serve as a guide to the characteristics of high-impact research, highlighting a consistent focus on oncology, driven by key authors and US institutions. While historical benchmarks focused on morphological assessment, citation metrics confirm computational methods as the principal driver of future innovation.
