揭开肺癌个性化免疫疗法中的病态情景:一个图书统计分析
Lei Yuan1,2,3, Zhiming Shen1,2,3, Yibo Shan1,2,3
1Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Frontiers in oncology
|July 23, 2024
概括
病理学在个性化肺癌免疫疗法方面表现有前途. 未来人工智能驱动的瘤微环境和多omics数据分析将改善治疗预测和患者的结果.
科学领域:
- 在瘤学瘤学.
- 计算病理学计算病理学
- 生物信息学是一种生物信息学.
背景情况:
- 病理学是一种正在开发的生物标志物,用于肺癌个性化免疫疗法.
- 了解病理学领域的全球研究趋势对于推动这一领域的发展至关重要.
研究的目的:
- 分析图书统计数据,并确定肺癌免疫疗法的病理学研究趋势.
- 突出这一领域的新兴前景和关键研究领域.
主要方法:
- 对2018年至2023年间发表的109篇文章进行了图书统计分析.
- 利用CiteSpace和其他用于数据可视化和分析的工具.
- 检查出版趋势,主要期刊,作者,国家,机构和关键词.
主要成果:
- 观察到肺癌病理学研究的持续上升趋势.
- 关键的研究术语包括深度学习,瘤微环境,生物标志物,图像分析,免疫治疗和生存预测.
- 分析确定了该领域的主要贡献者和协作网络.
结论:
- 未来的研究将整合人工智能和病理学来对瘤组织和微环境进行数字分析.
- 将多omics数据与病理数据结合起来,旨在加深对瘤微环境和瘤特征的理解.
- 开发多式融合模型将能够精确评估免疫疗法疗效和肺癌患者的预后.
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