Knowledge map and visualization analysis of pulmonary nodule/early-stage lung cancer prediction models
- VernacularTitle:肺结节/早期肺癌预测模型的知识图谱与可视化分析
- Author:
Yifeng REN
1
;
Qiong MA
1
;
Hua JIANG
1
;
Xi FU
1
;
Xueke LI
2
;
Wei SHI
3
;
Fengming YOU
2
Author Information
1. TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China
2. 1. TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China 2. Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, P. R. China
3. Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
- Publication Type:Journal Article
- Keywords:
Pulmonary nodules;
lung cancer;
prediction model;
knowledge map
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(01):100-107
- CountryChina
- Language:Chinese
-
Abstract:
Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications 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 database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A 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 which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was 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. Conclusion Over 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.
