De La Hoz-M Javier, Montes-Escobar Karime, Pérez-Ortiz Viorkis
Faculty of Engineering, Universidad del Magdalena, Santa Marta, Colombia.
Departamento de Matemáticas y Estadística, Facultad de Ciencias Básicas, Universidad Técnica de Manabí, Portoviejo 130105, Ecuador.
Pulm Med. 2024 Dec 4;2024:5911646. doi: 10.1155/pm/5911646. eCollection 2024.
Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence (AI) has begun to play a crucial role in improving diagnostic and treatment methods. It is crucial to understand how AI has integrated into LC research and to identify the main areas of focus. The aim of the study was to provide an updated insight into the role of AI in LC research, analyzing evolving topics, geographical distribution, and contributions to journals. The study explores research trends in AI applied to LC through a novel approach combining latent Dirichlet allocation (LDA) topic modeling with the HJ-Biplot statistical technique. A growing interest in AI applications in LC oncology was observed, reflected in a significant increase in publications, especially after 2017, coinciding with the availability of computing resources. leads in publishing AI-related LC research, reflecting rigorous investigation in the field. Geographically, China and the United States lead in contributions, attributed to significant investment in R&D and corporate sector involvement. LDA analysis highlights key research areas such as pulmonary nodule detection, patient prognosis prediction, and clinical decision support systems, demonstrating the impact of AI in improving LC outcomes. DL and AI emerge as prominent trends, focusing on radiomics and feature selection, promising better decision-making in LC care. The increase in AI-driven research covers various topics, including data analysis methodologies, tumor characterization, and predictive methods, indicating a concerted effort to advance LC research. HJ-Biplot visualization reveals thematic clustering, illustrating temporal and geographical associations and highlighting the influence of high-impact journals and countries with advanced research capabilities. This multivariate approach offers insights into global collaboration dynamics and specialization, emphasizing the evolving role of AI in LC research and diagnosis.
肺癌（LC）仍然是全球癌症相关死亡的主要原因之一。随着最近技术的进步，人工智能（AI）已开始在改善诊断和治疗方法方面发挥关键作用。了解人工智能如何融入肺癌研究并确定主要关注领域至关重要。本研究的目的是提供关于人工智能在肺癌研究中作用的最新见解，分析不断演变的主题、地理分布以及对期刊的贡献。该研究通过将潜在狄利克雷分配（LDA）主题建模与HJ-双标图统计技术相结合的新颖方法，探索了应用于肺癌的人工智能研究趋势。观察到对人工智能在肺癌肿瘤学中的应用兴趣日益浓厚，这反映在出版物数量的显著增加上，尤其是在2017年之后，这与计算资源的可用性相吻合。 在发表与人工智能相关的肺癌研究方面处于领先地位，反映了该领域的严谨研究。在地理上，中国和美国在贡献方面领先，这归因于对研发的大量投资和企业部门的参与。LDA分析突出了关键研究领域，如肺结节检测、患者预后预测和临床决策支持系统，证明了人工智能在改善肺癌治疗结果方面的影响。深度学习（DL）和人工智能成为突出趋势，专注于放射组学和特征选择，有望在肺癌护理中做出更好的决策。人工智能驱动的研究增加涵盖了各种主题，包括数据分析方法、肿瘤特征描述和预测方法，表明为推进肺癌研究做出了共同努力。HJ-双标图可视化揭示了主题聚类，说明了时间和地理关联，并突出了高影响力期刊和具有先进研究能力国家的影响。这种多变量方法提供了对全球合作动态和专业化的见解，强调了人工智能在肺癌研究和诊断中不断演变的作用。
