Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA.
Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Avenue, New York, NY 10029, USA.
Tomography. 2023 Nov 1;9(6):2016-2028. doi: 10.3390/tomography9060158.
The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include "Tumor Vascularity", "Stroke Assessment", "Myocardial Perfusion", "Intracerebral Hemorrhage", "Imaging Optimization", "Reperfusion Therapy", "Postprocessing", "Carotid Artery Disease", "Seizures", "Hemorrhagic Transformation", "Artificial Intelligence", and "Moyamoya Disease". The model provided insights into the trends of the current decade, highlighting "Postprocessing" and "Artificial Intelligence" as the most trending topics.
学术文章的数量持续增加。科学产出的持续增长给研究人员带来了挑战,他们必须投入大量时间来收集和分析这些结果。主题建模方法的出现是对此需求的一种新的回应。考虑到计算机断层灌注 (CTP) 的快速发展,我们认为有必要发起一项专注于主题建模的倡议。我们对 Scopus 数据库进行了全面搜索,时间范围为 2000 年 1 月 1 日至 2023 年 8 月 16 日,以确定与 CTP 相关的文章。我们使用 BERTopic 模型得出了一组主题及其各自的代表性文章。对于 2020 年代,我们使用线性回归模型来识别和解释趋势主题。从最普遍到最不普遍,确定的主题包括“肿瘤血管生成”、“中风评估”、“心肌灌注”、“脑出血”、“成像优化”、“再灌注治疗”、“后处理”、“颈动脉疾病”、“癫痫发作”、“出血性转化”、“人工智能”和“烟雾病”。该模型深入了解了当前十年的趋势,突出了“后处理”和“人工智能”是最热门的主题。