基于Pubmed数据库近6年卵巢癌分子成像研究的文献计量学分析

目的了解近6年卵巢癌分子成像领域的研究状况,为进一步研究提供参考信息。方法检索PubMed数据库中2012年1月1日—2017年12月31日发表的卵巢癌分子成像研究文献,采用Bicomb2.0软件统计分析文献的发表年代、来源期刊及期刊所属国家、第一作者、高频主题词分布情况、不同分子成像技术及主要示踪剂的发文情况。将出现频次≥4次的主要主题词+副主题词作为高频主题词,建立词篇矩阵,使用SPSS 22.0软件进行聚类分析,获得该领域高频主题词的共词聚类分析树图,分析该领域的研究热点。结果筛选出相关文章171篇,分别发表在88种期刊上,其中54篇(31.58%)发表在核心区的7种期刊上。有15人发表相关文献至少2篇。PET分子成像方面的文章共97篇(56.7%)。主要用于临床研究的分子示踪剂为~(18)F-FDG和~(99)Tc~m。高频主题词共37个,经聚类分析得出近6年卵巢癌的分子成像研究热点主要集中在卵巢癌的分子成像诊断、疗效监测及肿瘤代谢、肿瘤复发及转移灶的诊断、病理诊断4个方面。结论通过对卵巢癌分子成像领域的文献计量分析,可以揭示该领域的研究热点,为进一步研究提供一定参考。

Bibliometric analysis of molecular imaging in ovarian cancer over the past 6 years based on the Pubmed database

Objective To investigate the current situation of molecular imaging on ovarian cancer in recent 6 years in order to provide reference information for further research. Methods The literatures of molecular imaging on ovarian cancer were searched in PubMed database. The literatures were published from January 1, 2012 to December 31, 2017. Bicomb 2.0 software was used to extract the publication years, journals, countries of publication, the first authors, the frequency of subject headings and subheadings, and the techniques of molecular imaging and main tracers. The subjects headings and subheading appeared more than four-times were used as high frequency subjects and then formed high frequency headings co-occurrence matrix, which were analysed by SPSS 22.0 statistical software. Topic hotspots were got by clustering analysis. Results A total of 171 literatures were enrolled. The related literatures were published in the 88 journals, 7 of 88 journals (54 literatures were published, 31.58%) were in the core group of journals according to the Bradford law. There were 15 authors who published at least 2 literatures. 97 papers were about PET molecular imaging technique (56.7%). The main tracer which used in clinical were 18F-FDG and 99Tcm. There were 37 high frequency subject, and 4 research hotspots. The field of molecular imaging on ovarian cancer focused on molecular imaging diagnosis of ovarian cancer, diagnosis of tumor recurrence and metastasis, tumor metabolism and treatment, and pathological diagnosis. Conclusions The bibliometric analysis of the field of ovarian cancer molecular imaging could reveal hotpoints and provide a reference for further research.