Ma Qiong, Jiang Hua, Tan Shiyan, You Fengming, Zheng Chuan, Wang Qian, Ren Yifeng
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
Ann Med Surg (Lond). 2024 Oct 18;86(12):7178-7192. doi: 10.1097/MS9.0000000000002648. eCollection 2024 Dec.
In recent years, lung cancer-prediction models have become popular. However, few bibliometric analyses have been performed in this field.
This study aimed to reveal the scientific output and trends in lung cancer-prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends.
A marked increase in the number of publications related to lung cancer-prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer-prediction models.
Over the last two decades, research on risk-prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer-prediction models and reduce the global burden of lung cancer.
近年来，肺癌预测模型已变得流行。然而，该领域很少进行文献计量分析。
本研究旨在从全球视角揭示肺癌预测模型的科研产出和趋势。在本研究中，从科学引文索引核心合集（WoSCC）数据库中检索并提取出版物。使用CiteSpace 6.1.R3和VOSviewer 1.6.18分析热点和主题趋势。
观察到与肺癌预测模型相关的出版物数量显著增加。来自64个国家/地区的2711个机构在566种学术期刊上发表了2139篇文献。中国和美国是肺癌预测模型领域的领先国家。复旦大学代表的机构在该领域具有显著的学术影响力。关键词分析表明，长链非编码RNA、肿瘤微环境、免疫、癌症统计、癌症基因组图谱、列线图和机器学习是肺癌预测模型当前的研究重点。
在过去二十年中，肺癌风险预测模型的研究受到越来越多的关注。预后、机器学习和多组学技术既是该领域当前的热点，也是未来的趋势。未来，利用不同组学进行深入探索应能提高肺癌预测模型的敏感性和准确性，减轻全球肺癌负担。
