Karabacak Mert, Jagtiani Pemla, Carrasquilla Alejandro, Jain Ankita, Germano Isabelle M, Margetis Konstantinos
Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Avenue, Annenberg 8-42, New York, NY, 10029, USA.
School of Medicine, SUNY Downstate Health Sciences University, New York, NY, 11203, USA.
J Neurooncol. 2024 Sep;169(3):601-611. doi: 10.1007/s11060-024-04762-8. Epub 2024 Jul 11.
Our study aims to discover the leading topics within glioblastoma (GB) research, and to examine if these topics have "hot" or "cold" trends. Additionally, we aim to showcase the potential of natural language processing (NLP) in facilitating research syntheses, offering an efficient strategy to dissect the landscape of academic literature in the realm of GB research.
The Scopus database was queried using "glioblastoma" as the search term, in the "TITLE" and "KEY" fields. BERTopic, an NLP-based topic modeling (TM) method, was used for probabilistic TM. We specified a minimum topic size of 300 documents and 5% probability cutoff for outlier detection. We labeled topics based on keywords and representative documents and visualized them with word clouds. Linear regression models were utilized to identify "hot" and "cold" topic trends per decade.
Our TM analysis categorized 43,329 articles into 15 distinct topics. The most common topics were Genomics, Survival, Drug Delivery, and Imaging, while the least common topics were Surgical Resection, MGMT Methylation, and Exosomes. The hottest topics over the 2020s were Viruses and Oncolytic Therapy, Anticancer Compounds, and Exosomes, while the cold topics were Surgical Resection, Angiogenesis, and Tumor Metabolism.
Our NLP methodology provided an extensive analysis of GB literature, revealing valuable insights about historical and contemporary patterns difficult to discern with traditional techniques. The outcomes offer guidance for research directions, policy, and identifying emerging trends. Our approach could be applied across research disciplines to summarize and examine scholarly literature, guiding future exploration.
我们的研究旨在发现胶质母细胞瘤（GB）研究中的主要主题，并研究这些主题是否存在“热门”或“冷门”趋势。此外，我们旨在展示自然语言处理（NLP）在促进研究综合方面的潜力，提供一种有效的策略来剖析GB研究领域的学术文献格局。
在Scopus数据库的“标题”和“关键词”字段中使用“胶质母细胞瘤”作为搜索词进行查询。基于NLP的主题建模（TM）方法BERTopic用于概率主题建模。我们指定了最小主题规模为300篇文献，并设定了5%的概率截止值用于异常值检测。我们根据关键词和代表性文献对主题进行标注，并用词云进行可视化展示。利用线性回归模型来识别每个十年的“热门”和“冷门”主题趋势。
我们的主题建模分析将43329篇文章分为15个不同的主题。最常见的主题是基因组学、生存、药物递送和成像，而最不常见的主题是手术切除、MGMT甲基化和外泌体。2020年代最热门的主题是病毒与溶瘤疗法、抗癌化合物和外泌体，而冷门主题是手术切除、血管生成和肿瘤代谢。
我们的NLP方法对GB文献进行了广泛分析，揭示了传统技术难以察觉的有关历史和当代模式的宝贵见解。这些结果为研究方向、政策和识别新兴趋势提供了指导。我们的方法可应用于各个研究学科，以总结和审视学术文献，指导未来的探索。
