Xiong Dan-Dan, He Rong-Quan, Huang Zhi-Guang, Wu Kun-Jun, Mo Ying-Yu, Liang Yue, Yang Da-Ping, Wu Ying-Hui, Tang Zhong-Qing, Liao Zu-Tuan, Chen Gang
Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Guangxi Zhuang Autonomous Region Engineering Research Center for Artificial Intelligence Analysis of Multimodal Tumor Images, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Digit Health. 2024 Sep 2;10:20552076241277735. doi: 10.1177/20552076241277735. eCollection 2024 Jan-Dec.
The rapid development of computer technology has led to a revolutionary transformation in artificial intelligence (AI)-assisted healthcare. The integration of whole-slide imaging technology with AI algorithms has facilitated the development of digital pathology for lung cancer (LC). However, there is a lack of comprehensive scientometric analysis in this field.
A bibliometric analysis was conducted on 197 publications related to digital pathology in LC from 502 institutions across 39 countries, published in 97 academic journals in the Web of Science Core Collection between 2004 and 2023.
Our analysis has identified the United States and China as the primary research nations in the field of digital pathology in LC. However, it is important to note that the current research primarily consists of independent studies among countries, emphasizing the necessity of strengthening academic collaboration and data sharing between nations. The current focus and challenge of research related to digital pathology in LC lie in enhancing the accuracy of classification and prediction through improved deep learning algorithms. The integration of multi-omics studies presents a promising future research direction. Additionally, researchers are increasingly exploring the application of digital pathology in immunotherapy for LC patients.
In conclusion, this study provides a comprehensive knowledge framework for digital pathology in LC, highlighting research trends, hotspots, and gaps in this field. It also provides a theoretical basis for the application of AI in clinical decision-making for LC patients.
计算机技术的快速发展已引发人工智能辅助医疗保健领域的变革性转变。全切片成像技术与人工智能算法的整合推动了肺癌数字病理学的发展。然而,该领域缺乏全面的科学计量分析。
对来自39个国家502个机构的197篇与肺癌数字病理学相关的出版物进行文献计量分析,这些出版物发表于2004年至2023年期间的97种科学引文索引核心合集中的学术期刊。
我们的分析确定美国和中国是肺癌数字病理学领域的主要研究国家。然而,需要注意的是,当前研究主要是各国之间的独立研究,这凸显了加强国家间学术合作和数据共享的必要性。肺癌数字病理学相关研究当前的重点和挑战在于通过改进深度学习算法提高分类和预测的准确性。多组学研究的整合呈现出一个有前景的未来研究方向。此外,研究人员越来越多地探索数字病理学在肺癌患者免疫治疗中的应用。
总之,本研究为肺癌数字病理学提供了一个全面的知识框架,突出了该领域的研究趋势、热点和差距。它还为人工智能在肺癌患者临床决策中的应用提供了理论基础。