基于CiteSpace的国内肺结节CT影像研究热点与趋势分析

Hotspots and Trend Analysis of Domestic Research on Pulmonary Nodule CT Imaging Based on CiteSpace

摘要: 本研究利用文献计量学方法，以中国知网(CNKI) 2000年至2026年收录的关于肺结节CT影像的1932篇文献为数据源，运用CiteSpace软件进行可视化分析。研究系统梳理了该领域在国内的发展脉络，将其划分为萌芽、发展和繁荣三个阶段。通过对关键词共现、聚类、突现以及机构合作网络的分析，论文识别出当前的研究热点为人工智能和影像组学，并指出了未来发展的核心方向。研究发现，该领域的研究热点经历了从传统影像技术(如螺旋CT、灌注成像)向智能化诊断(如深度学习、影像组学)的演变。同时，分析也揭示了国内科研机构间合作网络尚不紧密，存在较大拓展空间的问题。最后，论文对未来研究提出了规范影像标准、优化成像技术和推进多中心跨学科研究的建议。

Abstract: This study employs bibliometric methods to conduct a visual analysis using CiteSpace software, based on 1932 articles on CT imaging of pulmonary nodules indexed in the China National Knowledge Infrastructure (CNKI) database from 2000 to 2026. The research systematically reviews the development trajectory of this field in China, dividing it into three stages: budding, development, and prosperity. Through analyses of keyword co-occurrence, clustering, burst detection, and institutional collaboration networks, the paper identifies current research hotspots as artificial intelligence and radiomics, and pinpoints the core directions for future development. The findings reveal that research hotspots in this field have evolved from traditional imaging techniques (e.g., spiral CT, perfusion imaging) toward intelligent diagnostic approaches (e.g., deep learning, radiomics). Meanwhile, the analysis also uncovers that collaboration networks among domestic research institutions remain relatively loose, with considerable room for expansion. Finally, the paper offers recommendations for future research, including standardizing imaging protocols, optimizing imaging technologies, and promoting multicenter, interdisciplinary studies.