Knowledge map and visualization analysis of pulmonary nodule/early-stage lung cancer prediction models_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

REN Yifeng ¹ , MA Qiong ¹ , JIANG Hua ¹ , FU Xi ¹ , LI Xueke ^1,2 , SHI Wei ³ ,  YOU Fengming ^1,2

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;

Corresponding author：

YOU Fengming, Email: yfmdoc@163.com

Keywords：

Pulmonary nodules; lung cancer; prediction model; knowledge map

DOI：

10.7507/1007-4848.202304026

Video：

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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 Core Collection 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 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. 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.

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2.	Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin, 2022, 72(1): 7-33.
3.	Jonas DE, Reuland DS, Reddy SM, et al. Screening for lung cancer with low-dose computed tomography: Updated evidence report and systematic review for the US Preventive Services Task Force. JAMA, 2021, 325(10): 971-987.
4.	Lovly CM. Expanding horizons for treatment of early-stage lung cancer. N Engl J Med, 2022, 386(21): 2050-2051.
5.	Yee J, Sadar MD, Sin DD, et al. Connective tissue-activating peptideⅢ: A novel blood biomarker for early lung cancer detection. J Clin Oncol, 2009, 27(17): 2787-2792.
6.	Pan J, Fang S, Tian H, et al. lncRNA JPX/miR-33a-5p/Twist1 axis regulates tumorigenesis and metastasis of lung cancer by activating Wnt/β-catenin signaling. Mol Cancer, 2020, 19(1): 9.
7.	Li N, Wang L, Hu Y, et al. Global evolution of research on pulmonary nodules: A bibliometric analysis. Future Oncol, 2021, 17(20): 2631-2645.
8.	Li N, Tan F, Chen W, et al. One-off low-dose CT for lung cancer screening in China: A multicentre, population-based, prospective cohort study. Lancet Respir Med, 2022, 10(4): 378-391.
9.	Chan MH, Huang WT, Wang J, et al. Next-generation cancer-specific hybrid theranostic nanomaterials: MAGE-A3 NIR persistent luminescence nanoparticles conjugated to afatinib for in situ suppression of lung adenocarcinoma growth and metastasis. Adv Sci (Weinh), 2020, 7(9): 1903741.
10.	Oudkerk M, Liu S, Heuvelmans MA, et al. Lung cancer LDCT screening and mortality reduction : Evidence, pitfalls and future perspectives. Nat Rev Clin Oncol, 2021, 18(3): 135-151.
11.	Fehlmann T, Kahraman M, Ludwig N, et al. Evaluating the use of circulating microRNA profiles for lung cancer detection in symptomatic patients. JAMA Oncol, 2020, 6(5): 714-723.
12.	Ten Haaf K, Tammemägi MC, Bondy SJ, et al. Performance and cost-effectiveness of computed tomography lung cancer screening scenarios in a population-based setting: A microsimulation modeling analysis in Ontario, Canada. PLoS Med, 2017, 14(2): e1002225.
13.	Lu MT, Raghu VK, Mayrhofer T, et al. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: Development and validation of a prediction model. Ann Intern Med, 2020, 173(9): 704-713.
14.	Muller DC, Johansson M, Brennan P. Lung cancer risk prediction model incorporating lung function: Development and validation in the UK Biobank prospective cohort study. J Clin Oncol, 2017, 35(8): 861-869.
15.	Cassidy A, Duffy SW, Myles JP, et al. Lung cancer risk prediction: A tool for early detection. Int J Cancer, 2007, 120(1): 1-6.
16.	van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 2010, 84(2): 523-538.
17.	Chen C, Song M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS One, 2019, 14(10): e0223994.
18.	Chen C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc Natl Acad Sci U S A, 2004, 101 Suppl 1(Suppl 1): 5303-5310.
19.	Jung JH, Chiang B, Grossniklaus HE, et al. Ocular drug delivery targeted by iontophoresis in the suprachoroidal space using a microneedle. J Control Release, 2018, 277: 14-22.
20.	Xie L, Chen Z, Wang H, et al. Bibliometric and visualized analysis of scientific publications on atlantoaxial spine surgery based on Web of Science and VOSviewer. World Neurosurg, 2020, 137: 435-442.
21.	Du Y, Duan C, Yang Y, et al. Heart transplantation: A bibliometric review from 1990-2021. Curr Probl Cardiol, 2022, 47(8): 101176.
22.	Toumazis I, Bastani M, Han SS, et al. Risk-Based lung cancer screening: A systematic review. Lung Cancer, 2020, 147: 154-186.
23.	Gray EP, Teare MD, Stevens J, et al. Risk prediction models for lung cancer: A systematic review. Clin Lung Cancer, 2016, 17(2): 95-106.
24.	Zhang M, Zhou Y, Lu Y, et al. The 100 most-cited articles on prenatal diagnosis: A bibliometric analysis. Medicine (Baltimore), 2019, 98(38): e17236.
25.	MacMahon H, Li F, Jiang Y, et al. Accuracy of the vancouver lung cancer risk prediction model compared with that of radiologists. Chest, 2019, 156(1): 112-119.
26.	Qiu YL, Zheng H, Devos A, et al. A meta-learning approach for genomic survival analysis. Nat Commun, 2020, 11(1): 6350.
27.	Shi L, Magee P, Fassan M, et al. A KRAS-responsive long non-coding RNA controls microRNA processing. Nat Commun, 2021, 12(1): 2038.
28.	Swensen S J, Silverstein M D, Ilstrup D M, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8): 849-855.
29.	Spitz MR, Hong WK, Amos CI, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst, 2007, 99(9): 715-726.
30.	Zhang Y, Yang M, Ng DM, et al. Multi-omics data analyses construct TME and identify the immune-related prognosis signatures in human LUAD. Mol Ther Nucleic Acids, 2020, 21: 860-873.
31.	Takahashi S, Asada K, Takasawa K, et al. Predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data. Biomolecules, 2020, 10(10): 1460.
32.	Li W, Liu B, Wang W, et al. Lung cancer stage prediction using multi-omics data. Comput Math Methods Med, 2022, 2022: 2279044.
33.	Wang T, Shao W, Huang Z, et al. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun, 2021, 12(1): 3445.
34.	Xing W, Sun H, Yan C, et al. A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules. BMC Cancer, 2021, 21(1): 263.
35.	Hu F, Huang H, Jiang Y, et al. Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: A multi-parameter prediction model. J Thorac Dis, 2021, 13(9): 5383-5394.
36.	Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
37.	Chen K, Sun J, Zhao H, et al. Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy. Mol Cancer, 2021, 20(1): 23.
38.	Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
39.	Moyer VA, . Screening for lung cancer: U. S. Preventive Services Task Force recommendation statement. Ann Intern Med, 2014, 160(5): 330-338.
40.	Carrillo-Perez F, Morales JC, Castillo-Secilla D, et al. Machine-learning-based late fusion on multi-omics and multi-scale data for non-small-cell lung cancer diagnosis. J Pers Med, 2022, 12(4): 601.
41.	Li W, Liu B, Wang W, et al. Lung cancer stage prediction using multi-omics data. Comput Math Methods Med, 2022, 2022: 2279044.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin, 2022, 72(1): 7-33.
3. Jonas DE, Reuland DS, Reddy SM, et al. Screening for lung cancer with low-dose computed tomography: Updated evidence report and systematic review for the US Preventive Services Task Force. JAMA, 2021, 325(10): 971-987.
4. Lovly CM. Expanding horizons for treatment of early-stage lung cancer. N Engl J Med, 2022, 386(21): 2050-2051.
5. Yee J, Sadar MD, Sin DD, et al. Connective tissue-activating peptideⅢ: A novel blood biomarker for early lung cancer detection. J Clin Oncol, 2009, 27(17): 2787-2792.
6. Pan J, Fang S, Tian H, et al. lncRNA JPX/miR-33a-5p/Twist1 axis regulates tumorigenesis and metastasis of lung cancer by activating Wnt/β-catenin signaling. Mol Cancer, 2020, 19(1): 9.
7. Li N, Wang L, Hu Y, et al. Global evolution of research on pulmonary nodules: A bibliometric analysis. Future Oncol, 2021, 17(20): 2631-2645.
8. Li N, Tan F, Chen W, et al. One-off low-dose CT for lung cancer screening in China: A multicentre, population-based, prospective cohort study. Lancet Respir Med, 2022, 10(4): 378-391.
9. Chan MH, Huang WT, Wang J, et al. Next-generation cancer-specific hybrid theranostic nanomaterials: MAGE-A3 NIR persistent luminescence nanoparticles conjugated to afatinib for in situ suppression of lung adenocarcinoma growth and metastasis. Adv Sci (Weinh), 2020, 7(9): 1903741.
10. Oudkerk M, Liu S, Heuvelmans MA, et al. Lung cancer LDCT screening and mortality reduction : Evidence, pitfalls and future perspectives. Nat Rev Clin Oncol, 2021, 18(3): 135-151.
11. Fehlmann T, Kahraman M, Ludwig N, et al. Evaluating the use of circulating microRNA profiles for lung cancer detection in symptomatic patients. JAMA Oncol, 2020, 6(5): 714-723.
12. Ten Haaf K, Tammemägi MC, Bondy SJ, et al. Performance and cost-effectiveness of computed tomography lung cancer screening scenarios in a population-based setting: A microsimulation modeling analysis in Ontario, Canada. PLoS Med, 2017, 14(2): e1002225.
13. Lu MT, Raghu VK, Mayrhofer T, et al. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening computed tomography: Development and validation of a prediction model. Ann Intern Med, 2020, 173(9): 704-713.
14. Muller DC, Johansson M, Brennan P. Lung cancer risk prediction model incorporating lung function: Development and validation in the UK Biobank prospective cohort study. J Clin Oncol, 2017, 35(8): 861-869.
15. Cassidy A, Duffy SW, Myles JP, et al. Lung cancer risk prediction: A tool for early detection. Int J Cancer, 2007, 120(1): 1-6.
16. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 2010, 84(2): 523-538.
17. Chen C, Song M. Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS One, 2019, 14(10): e0223994.
18. Chen C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc Natl Acad Sci U S A, 2004, 101 Suppl 1(Suppl 1): 5303-5310.
19. Jung JH, Chiang B, Grossniklaus HE, et al. Ocular drug delivery targeted by iontophoresis in the suprachoroidal space using a microneedle. J Control Release, 2018, 277: 14-22.
20. Xie L, Chen Z, Wang H, et al. Bibliometric and visualized analysis of scientific publications on atlantoaxial spine surgery based on Web of Science and VOSviewer. World Neurosurg, 2020, 137: 435-442.
21. Du Y, Duan C, Yang Y, et al. Heart transplantation: A bibliometric review from 1990-2021. Curr Probl Cardiol, 2022, 47(8): 101176.
22. Toumazis I, Bastani M, Han SS, et al. Risk-Based lung cancer screening: A systematic review. Lung Cancer, 2020, 147: 154-186.
23. Gray EP, Teare MD, Stevens J, et al. Risk prediction models for lung cancer: A systematic review. Clin Lung Cancer, 2016, 17(2): 95-106.
24. Zhang M, Zhou Y, Lu Y, et al. The 100 most-cited articles on prenatal diagnosis: A bibliometric analysis. Medicine (Baltimore), 2019, 98(38): e17236.
25. MacMahon H, Li F, Jiang Y, et al. Accuracy of the vancouver lung cancer risk prediction model compared with that of radiologists. Chest, 2019, 156(1): 112-119.
26. Qiu YL, Zheng H, Devos A, et al. A meta-learning approach for genomic survival analysis. Nat Commun, 2020, 11(1): 6350.
27. Shi L, Magee P, Fassan M, et al. A KRAS-responsive long non-coding RNA controls microRNA processing. Nat Commun, 2021, 12(1): 2038.
28. Swensen S J, Silverstein M D, Ilstrup D M, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med, 1997, 157(8): 849-855.
29. Spitz MR, Hong WK, Amos CI, et al. A risk model for prediction of lung cancer. J Natl Cancer Inst, 2007, 99(9): 715-726.
30. Zhang Y, Yang M, Ng DM, et al. Multi-omics data analyses construct TME and identify the immune-related prognosis signatures in human LUAD. Mol Ther Nucleic Acids, 2020, 21: 860-873.
31. Takahashi S, Asada K, Takasawa K, et al. Predicting deep learning based multi-omics parallel integration survival subtypes in lung cancer using reverse phase protein array data. Biomolecules, 2020, 10(10): 1460.
32. Li W, Liu B, Wang W, et al. Lung cancer stage prediction using multi-omics data. Comput Math Methods Med, 2022, 2022: 2279044.
33. Wang T, Shao W, Huang Z, et al. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification. Nat Commun, 2021, 12(1): 3445.
34. Xing W, Sun H, Yan C, et al. A prediction model based on DNA methylation biomarkers and radiological characteristics for identifying malignant from benign pulmonary nodules. BMC Cancer, 2021, 21(1): 263.
35. Hu F, Huang H, Jiang Y, et al. Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: A multi-parameter prediction model. J Thorac Dis, 2021, 13(9): 5383-5394.
36. Hosny A, Parmar C, Coroller TP, et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med, 2018, 15(11): e1002711.
37. Chen K, Sun J, Zhao H, et al. Non-invasive lung cancer diagnosis and prognosis based on multi-analyte liquid biopsy. Mol Cancer, 2021, 20(1): 23.
38. Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med, 2011, 365(5): 395-409.
39. Moyer VA, . Screening for lung cancer: U. S. Preventive Services Task Force recommendation statement. Ann Intern Med, 2014, 160(5): 330-338.
40. Carrillo-Perez F, Morales JC, Castillo-Secilla D, et al. Machine-learning-based late fusion on multi-omics and multi-scale data for non-small-cell lung cancer diagnosis. J Pers Med, 2022, 12(4): 601.
41. Li W, Liu B, Wang W, et al. Lung cancer stage prediction using multi-omics data. Comput Math Methods Med, 2022, 2022: 2279044.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesKnowledge map and visualization analysis of pulmonary nodule/early-stage lung cancer prediction models

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