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Updated: Aug 15, 2026
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
Published on: June 20, 2025
Bibliometric Analysis of Pulmonary Fibrosis Imaging Research: Knowledge Graph Construction Based on the Web of
Shuo Yu1, Zhiyue Li1, Yuting Zhang2
1School of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang 712046, China.
Aim:
To quantify publication trends, map international collaboration networks, and identify dominant and emerging research themes in Pulmonary fibrosis(PF) imaging (2015-2024).
Methods:
A structured Web of Science search combining PF- and imaging-related terms yielded 1,159 English-language original research articles. Analyses employed CiteSpace, VOSviewer, and Scimago Graphica for trend assessment, keyword co-occurrence, citation burst detection, and collaboration network visualization.
Results:
Annual publication volume showed sustained linear growth (R2 = 0.867). Researchers from 64 countries contributed; the United States led in output (332 publications) and citation impact (9,821 citations), while China ranked second in volume (224 publications) with lower proportional citation impact. The Western Europe-North America axis showed the densest collaborative ties. Keyword co-occurrence revealed close thematic links among idiopathic pulmonary fibrosis, high-resolution computed tomography (HRCT), usual interstitial pneumonia (UIP), survival, and mortality. Citation burst analysis identified "deep learning" as the strongest and most sustained burst (2021-2024), by which point it had shifted from exploratory method to established domain. Three overlapping research phases emerged: diagnostic framework consolidation (2015-2018), computational computed tomography (CT)-based phenotyping (2016-2022), and therapeutic expansion toward antifibrotics and progressive fibrosing interstitial lung disease (2019-2024).
Conclusion:
PF imaging research has shifted from diagnostic consensus toward quantitative CT biomarkers and artificial intelligence(AI)-driven phenotyping, driven by the need to reduce interobserver variability and enable individualized risk stratification. Geographic fragmentation and limited multicenter validation remain key barriers to AI generalizability. Future priorities include standardized imaging protocols, prospective multicenter validation cohorts, and integration of AI-driven CT phenotyping with multi-omics and circulating biomarkers for prognostic precision.
