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Abstract：
BACKGROUND: Radiogenomics is an emerging technology that integrates genomics and medical image-based radiomics, which is considered a promising approach toward achieving precision medicine.
OBJECTIVE: The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods.
METHODS: The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline.
RESULTS: A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including \"big data,\" \"magnetic resonance spectroscopy,\" \"renal cell carcinoma,\" \"stage,\" and \"temozolomide,\" experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence.
CONCLUSIONS: The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present.
OBJECTIVE: The aim of this study was to quantitatively analyze the research status, dynamic trends, and evolutionary trajectory in the radiogenomics field using bibliometric methods.
METHODS: The relevant literature published up to 2023 was retrieved from the Web of Science Core Collection. Excel was used to analyze the annual publication trend. VOSviewer was used for constructing the keywords co-occurrence network and the collaboration networks among countries and institutions. CiteSpace was used for citation keywords burst analysis and visualizing the references timeline.
RESULTS: A total of 3237 papers were included and exported in plain-text format. The annual number of publications showed an increasing annual trend. China and the United States have published the most papers in this field, with the highest number of citations in the United States and the highest average number per item in the Netherlands. Keywords burst analysis revealed that several keywords, including \"big data,\" \"magnetic resonance spectroscopy,\" \"renal cell carcinoma,\" \"stage,\" and \"temozolomide,\" experienced a citation burst in recent years. The timeline views demonstrated that the references can be categorized into 8 clusters: lower-grade glioma, lung cancer histology, lung adenocarcinoma, breast cancer, radiation-induced lung injury, epidermal growth factor receptor mutation, late radiotherapy toxicity, and artificial intelligence.
CONCLUSIONS: The field of radiogenomics is attracting increasing attention from researchers worldwide, with the United States and the Netherlands being the most influential countries. Exploration of artificial intelligence methods based on big data to predict the response of tumors to various treatment methods represents a hot spot research topic in this field at present.
摘要：
背景：放射基因组学是一种新兴的技术，它集成了基因组学和基于医学图像的放射组学，这被认为是实现精准医学的一种有希望的方法。
目的：本研究的目的是定量分析研究现状，动态趋势,和使用文献计量学方法在放射基因组学领域的进化轨迹。
方法：从WebofScienceCoreCollection检索到截至2023年发表的相关文献。使用Excel分析年度出版趋势。VOSviewer用于构建关键词共现网络以及国家和机构之间的合作网络。CiteSpace用于引用关键词突发分析和可视化参考时间线。
结果：共纳入3237篇论文，并以纯文本格式导出。出版物的年度数量呈逐年增长的趋势。中国和美国在这一领域发表的论文最多,在美国被引用次数最多，在荷兰被引用次数最高。关键词突发分析表明，几个关键词，包括“大数据”，“磁共振波谱”，肾细胞癌,\"\"阶段,\"和\"替莫唑胺，“近年来经历了一次引文爆发。时间线视图表明，参考文献可以分为8个簇：低级别神经胶质瘤，肺癌组织学，肺腺癌,乳腺癌,放射性肺损伤,表皮生长因子受体突变,晚期放疗毒性，和人工智能。
结论：放射基因组学领域越来越受到全世界研究人员的关注，美国和荷兰是最具影响力的国家。探索基于大数据的人工智能方法来预测肿瘤对各种治疗方法的反应是目前该领域的研究热点。
目的：本研究的目的是定量分析研究现状，动态趋势,和使用文献计量学方法在放射基因组学领域的进化轨迹。
方法：从WebofScienceCoreCollection检索到截至2023年发表的相关文献。使用Excel分析年度出版趋势。VOSviewer用于构建关键词共现网络以及国家和机构之间的合作网络。CiteSpace用于引用关键词突发分析和可视化参考时间线。
结果：共纳入3237篇论文，并以纯文本格式导出。出版物的年度数量呈逐年增长的趋势。中国和美国在这一领域发表的论文最多,在美国被引用次数最多，在荷兰被引用次数最高。关键词突发分析表明，几个关键词，包括“大数据”，“磁共振波谱”，肾细胞癌,\"\"阶段,\"和\"替莫唑胺，“近年来经历了一次引文爆发。时间线视图表明，参考文献可以分为8个簇：低级别神经胶质瘤，肺癌组织学，肺腺癌,乳腺癌,放射性肺损伤,表皮生长因子受体突变,晚期放疗毒性，和人工智能。
结论：放射基因组学领域越来越受到全世界研究人员的关注，美国和荷兰是最具影响力的国家。探索基于大数据的人工智能方法来预测肿瘤对各种治疗方法的反应是目前该领域的研究热点。
