Zhang Jiacheng, Zhu Hainan, Wu Hengzhen, Xie Huabao, Lin Dingyi, Zhu Lielie
Department of Rehabilitation, Wenzhou Ouhai District Third People's Hospital, Wenzhou, China.
Department of Rehabilitation, Taishun County Hospital of Traditional Chinese Medicine (Taishun County TCM Medical Community), Wenzhou, China.
Front Neurol. 2025 Aug 28;16:1606388. doi: 10.3389/fneur.2025.1606388. eCollection 2025.
Ischemic stroke is a leading global cause of death and disability, presenting significant challenges in diagnosis, treatment, and prognosis. Radiomics, an emerging interdisciplinary methodology, employs machine learning to extract high-dimensional features from medical imaging and has demonstrated superior predictive performance in ischemic stroke research. However, the rapidly accumulating publications lack systematic bibliometric synthesis. We therefore conducted a visual bibliometric analysis to map research evolution and emerging trends.
This study conducted a bibliometric and visual analysis of ischemic stroke radiomics research from 2004 to 2024 using tools like CiteSpace and VOSviewer. The analysis explored publication trends, research hotspots, and technological advancements, identifying collaborations and key advancements in the field.
Radiomics research in ischemic stroke has grown exponentially since its inception in 2014, with China and the United States emerging as major contributors. The primary focus has been on AIS, utilizing advanced imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). Machine learning models, particularly deep learning architectures, are being widely applied for lesion segmentation, risk assessment, and functional prognosis prediction. Despite rapid advancements, challenges persist in standardizing imaging protocols, enhancing interdisciplinary collaborations, and ensuring clinical translation.
Radiomics is transforming ischemic stroke research by enabling detailed imaging analyses and facilitating data-driven clinical decision-making. Future endeavors should prioritize addressing standardization issues, expanding multicenter collaborations, and developing interpretable models that integrate radiomics with clinical and molecular biomarkers. Such efforts will accelerate the translation of radiomics into routine ischemic stroke care and improve patient outcomes.
缺血性中风是全球主要的死亡和残疾原因，在诊断、治疗和预后方面面临重大挑战。放射组学是一种新兴的跨学科方法，利用机器学习从医学影像中提取高维特征，并在缺血性中风研究中表现出卓越的预测性能。然而，快速积累的出版物缺乏系统的文献计量学综合分析。因此，我们进行了一项可视化文献计量分析，以描绘研究的演变和新趋势。
本研究使用CiteSpace和VOSviewer等工具，对2004年至2024年缺血性中风放射组学研究进行了文献计量和可视化分析。该分析探讨了出版趋势、研究热点和技术进步，确定了该领域的合作和关键进展。
自2014年缺血性中风放射组学研究开展以来呈指数增长，中国和美国是主要贡献者。主要重点是急性缺血性中风（AIS），利用计算机断层扫描（CT）和磁共振成像（MRI）等先进成像技术。机器学习模型，特别是深度学习架构，被广泛应用于病变分割、风险评估和功能预后预测。尽管取得了快速进展，但在标准化成像方案、加强跨学科合作和确保临床转化方面仍存在挑战。
放射组学正在通过实现详细的影像分析和促进数据驱动的临床决策来改变缺血性中风研究。未来的努力应优先解决标准化问题、扩大多中心合作，并开发将放射组学与临床和分子生物标志物相结合的可解释模型。这些努力将加速放射组学转化为常规缺血性中风护理，并改善患者预后。
