Li Siyu, Li Wenrui, Wang Xiaoxiao, Chen Wanyi
Department of Pharmacy, Chongqing University Cancer Hospital, Chongqing, China.
Front Oncol. 2025 Jul 8;15:1556521. doi: 10.3389/fonc.2025.1556521. eCollection 2025.
Prediction models, which estimate disease or outcome probabilities, are widely used in cancer research. This study aims to identify hotspots and future directions of cancer-related prediction models using bibliometrics.
A comprehensive literature search was conducted in the Science Citation Index Expanded (SCIE) from the Web of Science Core Collection (WoSCC) up to November 15, 2024, focusing on cancer-related prediction models research. Co-occurrence analyses of countries, institutions, authors, journals, and keywords were conducted using VOSviewer 1.6.20. Additionally, keyword clustering, timeline visualization, and burst term analysis were performed with CiteSpace 6.3.
A total of 1,661 records were retrieved from the SCIE. After deduplication and eligibility screening, 1,556 publications were included in the analysis. The bibliometric analysis revealed a consistent annual increase in cancer-related prediction model research, with China and the United States emerging as the leading contributors. The United States, England, and the Netherlands had the strongest collaborative networks. The most frequent keywords, excluding "prediction model" and "predictive model", included nomogram (frequency=192), survival (191), risk (121), prognosis (112), breast cancer (103), carcinoma (93), validation (87), surgery (85), diagnosis (83), chemotherapy (80), and machine learning (77). Besides, the timeline view analysis indicated that the "#7 machine learning" cluster was experiencing vigorous growth.
Cancer-related prediction models are rapidly advancing, especially in prognostic models. Emerging modeling techniques, such as neural networks and deep learning algorithms, are likely to play a pivotal role in current and future cancer-related prediction model research. Systematic reviews of cancer-related predictive models, which could help clinicians select the optimal model for specific clinical conditions may emerge as potential research directions in this field.
预测模型用于估计疾病或预后概率，在癌症研究中广泛应用。本研究旨在利用文献计量学确定癌症相关预测模型的热点和未来方向。
截至2024年11月15日，在科学网核心合集（WoSCC）的科学引文索引扩展版（SCIE）中进行了全面的文献检索，重点关注癌症相关预测模型研究。使用VOSviewer 1.6.20对国家、机构、作者、期刊和关键词进行共现分析。此外，使用CiteSpace 6.3进行关键词聚类、时间线可视化和突现词分析。
从SCIE中检索到1661条记录。经过去重和合格性筛选后，1556篇出版物纳入分析。文献计量分析显示，癌症相关预测模型研究每年持续增加，中国和美国是主要贡献者。美国、英国和荷兰拥有最强大的合作网络。除“预测模型”和“预测性模型”外最常见关键词包括列线图（出现频率=192）、生存（191）、风险（121）、预后（112）、乳腺癌（103）、癌（93）、验证（87）、手术（85）、诊断（83）、化疗（80）和机器学习（77）。此外时间线视图分析表明“#7机器学习”聚类正经历蓬勃发展期
癌症相关预测模型正在迅速发展，尤其是在预后模型方面进展显著。新兴建模技术，如神经网络和深度学习算法，可能在当前及未来癌症相关预测模型研究中发挥关键作用。对癌症相关预测模型进行系统评价可能成为该领域潜在研究方向之一，可以帮助临床医生针对特定临床情况选择最佳模型用于临床实践。
