探讨过去35年国内磁共振成像(MRI)在肺癌诊断中的研究热点演变与前沿趋势。

检索中国知网、万方、维普三大中文数据库1991-01-01至2025-12-31发表的相关文献。运用NoteExpress进行筛选与去重，利用CiteSpace、VOSviewer进行文献计量与可视化分析，包括年发文量、机构与作者合作网络、关键词共现与突现分析。

最终纳入文献1 588篇。年发文量总体呈现上升趋势，于2019年达到峰值(111篇)。其中海军军医大学第二附属医院(上海长征医院)是国内发文量最多的机构(34篇)。各高产作者之间存在合作关系，并形成了各自稳定的研究团队，但跨团队合作有限。在机构合作情况中院内、跨机构合作均存在，但顶尖机构之间的合作较少。关键词分析表明，近年来肺癌MRI研究热点从弥散加权成像等功能性成像逐步向病理类型、影像组学演进。

过去35年，国内肺癌MRI研究热点从功能性成像逐渐向影像组学、定量成像、肺结节精准检测、人工智能等前沿领域推进。

To explore the evolution of research hotspots and cutting-edge trends in magnetic resonance imaging (MRI) for lung cancer diagnosis in China over the past 35 years.

Literature was retrieved from three major Chinese databases: China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP, published from January 1, 1991, to December 31, 2025. The retrieved literature was screened and deduplicated using NoteExpress. Bibliometric and visual analyses, including annual publication volume, institutional and author collaboration networks, as well as keyword co-occurrence and burst analysis, were conducted using CiteSpace and VOSviewer.

A total of 1 588 articles were eventually included. The annual publication volume showed an overall upward trend, peaking in 2019 with 111 articles. Among them, the second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital) was the institution with the highest domestic publication output (34 articles). High-yield authors demonstrated collaborative relationships and formed their own stable research teams, yet cross-team collaborations remained limited. In terms of institutional collaboration, both intra-institutional and cross-institutional cooperation existed, but collaborations among top-tier institutions were relatively scarce. Keyword analysis indicated that in recent years, the research focus of MRI in lung cancer had gradually evolved from functional imaging such as diffusion-weighted imaging to pathological types and radiomics.

Over the past 35 years, research hotspots on MRI in lung cancer have gradually shifted from functional imaging to cutting-edge fields such as radiomics, quantitative imaging, precise detection of pulmonary nodules, and artificial intelligence.