To explore the focus and trends in real-world studies in Chinese through knowledge mapping method, databases CNKI, VIP, Wanfang and Sinomed were retrieved, with 1 757 relevant articles published before September 30rd, 2020 finally included, whose bibliographical records were imported into NoteExpress to avoid duplication and check relativity. VOSviewer, a bibliometric analysis tool, was used to analyze their development. It was found that real-world studies have mainly taken shape after 2010, in which traditional Chinese medicine research plays an important role. China Journal of Chinese Material Medica was the leading journal with 120 papers, the China Academy of Chinese Medical Sciences the most contribution institution with 338 papers, and Xie Yanming from the institution the most contribution author with 250 papers. This study helps clinicians and researchers in better understanding the evolution of real-world research over more than two decades in China.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early stage lung cancer-prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science Core Collection database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early stage lung cancer-prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals that published the most pulmonary nodules/early stage lung cancer-prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, separately. Chest is the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early stage lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early stage lung cancer-prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early stage lung cancer-prediction models further and reduce the global burden of lung cancer.