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.
Objective This study aimed to analyze the differences between the distribution of Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without. Additionally, it seeked to explore the potential correlation between the distribution of Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of Traditional Chinese Medicine (TCM) syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between Traditional Chinese Medicine (TCM) syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and liver, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease location were the lung and spleen, and the primary Traditional Chinese Medicine (TCM) syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of Traditional Chinese Medicine (TCM) syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there were significant differences in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of Traditional Chinese Medicine (TCM) syndrome elements and the species abundance and composition of salivary microbiota between patients with pulmonary nodules and healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.