PDF(Pubmed)
Abstract：
UNASSIGNED: Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.
UNASSIGNED: The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.
UNASSIGNED: A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms \"deep learning\", \"tumor microenvironment\", \"biomarkers\", \"image analysis\", \"immunotherapy\", and \"survival prediction\", etc. are hot keywords in this field.
UNASSIGNED: In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.
UNASSIGNED: The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.
UNASSIGNED: A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms \"deep learning\", \"tumor microenvironment\", \"biomarkers\", \"image analysis\", \"immunotherapy\", and \"survival prediction\", etc. are hot keywords in this field.
UNASSIGNED: In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.
摘要：
■Pathomics已成为一种有前途的生物标志物，可以促进肺癌的个性化免疫治疗。阐明该领域的全球研究趋势和新兴前景至关重要。
■年度分布，期刊,作者,国家,机构,使用CiteSpace和其他文献计量工具对2018年至2023年之间发表的文章的关键词进行可视化和分析。
■共收录109篇相关文章或评论，显示出整体上升趋势；术语“深度学习”，“肿瘤微环境”，“生物标志物”，\"图像分析\",“免疫疗法”，和“生存预测”，等。是该字段中的热门关键字。
■在未来的研究工作中，涉及人工智能和病理组学的先进方法将用于肺癌患者的肿瘤组织和肿瘤微环境的数字分析，利用组织病理学组织切片。通过整合综合的多组数据,这项战略旨在提高评估的深度，表征，以及对肿瘤微环境的理解，从而阐明了更广泛的肿瘤特征。因此,多模态融合模型的发展将随之而来，能够精确评估肺癌患者的个性化免疫治疗疗效和预后，可能在这个调查领域建立一个关键的前沿。
■年度分布，期刊,作者,国家,机构,使用CiteSpace和其他文献计量工具对2018年至2023年之间发表的文章的关键词进行可视化和分析。
■共收录109篇相关文章或评论，显示出整体上升趋势；术语“深度学习”，“肿瘤微环境”，“生物标志物”，\"图像分析\",“免疫疗法”，和“生存预测”，等。是该字段中的热门关键字。
■在未来的研究工作中，涉及人工智能和病理组学的先进方法将用于肺癌患者的肿瘤组织和肿瘤微环境的数字分析，利用组织病理学组织切片。通过整合综合的多组数据,这项战略旨在提高评估的深度，表征，以及对肿瘤微环境的理解，从而阐明了更广泛的肿瘤特征。因此,多模态融合模型的发展将随之而来，能够精确评估肺癌患者的个性化免疫治疗疗效和预后，可能在这个调查领域建立一个关键的前沿。
