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肿瘤免疫治疗研究的文献计量分析。

Bibliometric Analysis of Tumor Immunotherapy Studies.

机构信息

Department of Urology, The First Hospital of China Medical University, Shenyang, Liaoning, China (mainland).

Joint Laboratory of Artificial Intelligence and Precision Medicine of China Medical University and Northeastern University, Shenyang, Liaoning, China (mainland).

出版信息

Med Sci Monit. 2018 May 23;24:3405-3414. doi: 10.12659/MSM.910724.

DOI:10.12659/MSM.910724
PMID:29790485
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5994141/
Abstract

BACKGROUND Cancer immunotherapy is the use of the immune system to treat cancer. After years of research, there have been a significant number of publications in this field. We analyzed the literature and performed a hotspot analysis to identify important areas of future scientific research. MATERIAL AND METHODS Articles (2945) related to cancer immunotherapy published in the past 3 years were selected as the research sample. BICOMB software was then used to retrieve the high-frequency words and construct a text/co-word matrix. Next, gCLUTO software was used to analyze the matrix by double-clustering and visual analysis, in a strategy of hotspot identification. RESULTS We constructed a text and co-word matrix composed of 40 high-frequency words and 2945 articles and generated a hotspot "peak map" based on double-clustering analysis. The strategic coordinates were set by use of a co-word matrix and clustering analysis. The distribution of organs or disease and the subclass of cancer immunotherapy were analyzed. CONCLUSIONS In this study, we classified the hot-spots of "tumor immunotherapy" into 6 categories and 8 aspects. Calculation and analysis revealed that the field of tumor immunotherapy shows a slight trend of polarization, and the immune checkpoint inhibitor PD1 blocker shows the greatest potential for future development.

摘要

背景

癌症免疫疗法是利用免疫系统来治疗癌症。经过多年的研究，该领域已经有了大量的出版物。我们分析了文献并进行了热点分析，以确定未来科学研究的重要领域。

材料和方法

选择过去 3 年中与癌症免疫疗法相关的 2945 篇文章作为研究样本。然后使用 BICOMB 软件检索高频词，并构建文本/共词矩阵。接下来，使用 gCLUTO 软件通过双聚类和可视化分析对矩阵进行分析，以确定热点。

结果

我们构建了一个由 40 个高频词和 2945 篇文章组成的文本和共词矩阵，并根据双聚类分析生成了一个热点“峰图”。通过共词矩阵和聚类分析设置了战略坐标。分析了器官或疾病的分布和癌症免疫疗法的子类。

结论

在这项研究中，我们将“肿瘤免疫疗法”的热点分为 6 类和 8 个方面。计算和分析表明，肿瘤免疫疗法领域呈现出轻微的极化趋势，免疫检查点抑制剂 PD1 阻滞剂显示出最大的发展潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/e6f7fff0f656/medscimonit-24-3405-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/ceac6feecf6f/medscimonit-24-3405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/f173e13b9559/medscimonit-24-3405-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/fa621c0cb665/medscimonit-24-3405-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/ababc7a0e199/medscimonit-24-3405-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/7552bb2ebff7/medscimonit-24-3405-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/e6f7fff0f656/medscimonit-24-3405-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/ceac6feecf6f/medscimonit-24-3405-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/f173e13b9559/medscimonit-24-3405-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/fa621c0cb665/medscimonit-24-3405-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d54f/5994141/ababc7a0e199/medscimonit-24-3405-g004.jpg
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