Diagnostic-therapeutic management of pulmonary nodules

Česká verzia

Authors: V. Červeňák ¹; Z. Chovanec ²; A. Berková ²; J. Resler ²; T. Hanslík ²; M. Kelblová ¹; K. Novosádová ¹; V. Weiss ³; O. Bílek ⁴; J. Vaníček ¹
Authors place of work: Klinika zobrazovacích metod LF MU a FN u sv. Anny v Brně ¹; I. chirurgická klinika LF MU a FN u sv. Anny v Brně ²; I. neurologická klinika LF MU a FN u sv. Anny a v Brně ³; Klinika komplexní onkologické péče LF MU a MOÚ Brno ⁴
Published in the journal: Klin Onkol 2024; 39(6): 408-418
Category: Reviews
doi: https://doi.org/10.48095/ ccko2024408

Summary

Background: Lung cancer is one of the leading causes of death worldwide, with incidence and mortality significantly affected by population ageing and changes in the prevalence of risk factors. Lung nodules, which are often detected incidentally on imaging studies, pose a significant diagnostic challenge as they may indicate both benign and malignant processes. Correct diagnosis and management of these nodules is therefore essential to optimize clinical outcomes. Purpose: This article provides a comprehensive review of diagnostic and therapeutic approaches to pulmonary nodules, focusing on the assessment of malignant potential based on nodule morphology, size and growth potential. Risk factors influencing the decision-making process such as smoking, age and exposure to carcinogens are also discussed. In addition, key recommendations from the Fleischner Society and the British Thoracic Society are discussed in detail. The article analyses the benefits of modern imaging techniques, including the use of artificial intelligence (AI) in the diagnosis of lung nodules. AI technologies, particularly deep learning techniques, have shown high accuracy in detecting and assessing malignancy risk, and their use is increasingly complementary to expert clinical judgement. Finally, the article highlights the importance of a multidisciplinary approach to the diagnosis and management of lung nodules, and also mentions the implementation of a pilot lung cancer screening programme in the Czech Republic aimed at early detection of the disease. This programme has the potential to significantly reduce lung cancer mortality and improve patient prognosis.

Keywords:

lung cancer – CT – pulmonary nodule – pulmonary nodule management – dispensary

Zdroje

1. World Health Organization. Global health estimates: leading causes of death. Cause-specific mortality, 2000–2001. [online]. Dostupné z: https: //www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-leading-causes-of-death.

2. Sung H, Ferlay J, Siegel RL et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality eorldwide for 36 vancers in 185 vountries. CA Cancer J Clin 2021; 71 (3): 209–249. doi: 10.3322/caac.21660.

3. Allemani C, Matsuda T, Di Carlo V et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 2018; 391 (10125): 1023–1075. doi: 10.1016/S0140-6736 (17) 33326-3.

4. Krejčí D, Mužík J, Dušek L. Novotvary 2019–2021 ČR. Současné epidemiologické trendy novotvarů v České republice. [online]. Dostupné z: https: //www.uzis.cz/res/f/008447/novotvary2019-2021.pdf.

5. Ferlay J, Colombet M, Soerjomataram I et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 2019; 144 (8): 1941–1953. doi: 10.1002/ijc.31937.

6. Frank L, Quint LE. Chest CT incidentalomas: thyroid lesions, enlarged mediastinal lymph nodes, and lung nodules. Cancer Imaging 2012; 12 (1): 41–48. doi: 10.1102/1470-7330.2012.0006.

7. Good CA, Wilson TW. The solitary circumscribed pulmonary nodule; study of seven hundred five cases encountered roentgenologically in a period of three and one-half years. J Am Med Assoc 1958; 166 (3): 210–215. doi: 10.1001/jama.1958.02990030008003.

8. Gould MK, Tang T, Liu IL et al. Recent trends in the identification of incidental pulmonary nodules. Am J Respir Crit Care Med 2015; 192 (10): 1208–1214. doi: 10.1164/rccm.201505-0990OC.

9. MacMahon H, Naidich DP, Goo JM et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner society 2017. Radiology 2017; 284 (1): 228–243. doi: 10.1148/radiol.2017161659.

10. Callister ME, Baldwin DR, Akram AR et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 2015; 70 (Suppl 2): ii1–ii54. doi: 10.1136/thoraxjnl-2015-207168.

11. Gould MK, Donington J, Lynch WR et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 2013; 143 (Suppl 5): e93S–e120S. doi: 10.1378/chest.12-2351.

12. Heuvelmans MA, Walter JE, Peters RB et al. Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: the NELSON study. Lung Cancer 2017; 113: 45–50. doi: 10.1016/j.lungcan.2017.08.023.

13. Gould MK, Ananth L, Barnett PG et al. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest 2007; 131 (2): 383–388. doi: 10.1378/chest.06-1261.

14. Tindle HA, Stevenson Duncan M, Greevy RA et al. Lifetime smoking history and risk of lung cancer: results from the Framingham heart study. J Natl Cancer Inst 2018; 110 (10): 1153. doi: 10.1093/jnci/djy041.

15. Hamann SL, Kungskulniti N, Charoenca N et al. Electronic cigarette harms: aggregate evidence shows damage to biological systems. Int J Environ Res Public Health 2023; 20 (19): 6808. doi: 10.3390/ijerph20196808.

16. Nielsen LS, Bælum J, Rasmussen J et al. Occupational asbestos exposure and lung cancer – a systematic review of the literature. Arch Environ Occup Health 2014; 69 (4): 191–206. doi: 10.1080/19338244.2013.863752.

17. Ngamwong Y, Tangamornsuksan W, Lohitnavy O et al. Additive synergism between asbestos and smoking in lung cancer risk: a systematic review and meta-analysis. PLoS One 2015; 10 (8): e0135798. doi: 10.1371/journal.pone.0135798.

18. Johnson BE. Second lung cancers in patients after treatment for an initial lung cancer. J Natl Cancer Inst 1998; 90 (18): 1335–1345. doi: 10.1093/jnci/90.18.1335.

19. Surapaneni R, Singh P, Rajagopalan K et al. Stage I lung cancer survivorship: risk of second malignancies and need for individualized care plan. J Thorac Oncol 2012; 7 (8): 1252–1256. doi: 10.1097/JTO.0b013e31825 82a79.

20. Milano MT, Peterson CR 3rd, Zhang H et al. Second primary lung cancer after head and neck squamous cell cancer: population-based study of risk factors. Head Neck 2012; 34 (12): 1782–1788. doi: 10.1002/hed.22006.

21. Lorigan P, Radford J, Howell A et al. Lung cancer after treatment for Hodgkin‘s lymphoma: a systematic review. Lancet Oncol 2005; 6 (10): 773–779. doi: 10.1016/S1470-2045 (05) 70387-9.

22. Pirani M, Marcheselli R, Marcheselli L et al. Risk for second malignancies in non-Hodgkin‘s lymphoma survivors: a meta-analysis. Ann Oncol 2011; 22 (8): 1845–1858. doi: 10.1093/annonc/mdq697.

23. Nitadori J, Inoue M, Iwasaki M et al. Association between lung cancer incidence and family history of lung cancer: data from a large-scale population-based cohort study, the JPHC study. Chest 2006; 130 (4): 968–975. doi: 10.1378/chest.130.4.968.

24. Wilson DO, Weissfeld JL, Balkan A et al. Association of radiographic emphysema and airflow obstruction with lung cancer. Am J Respir Crit Care Med 2008; 178 (7): 738–744. doi: 10.1164/rccm.200803-435OC.

25. Young RP, Duan F, Chiles C et al. Airflow limitation and histology shift in the National Lung Screening Trial. The NLST-ACRIN cohort substudy. Am J Respir Crit Care Med 2015; 192 (9): 1060–1067. doi: 10.1164/rccm.201505-0894OC.

26. Antoniou KM, Tomassetti S, Tsitoura E et al. Idiopathic pulmonary fibrosis and lung cancer: a clinical and pathogenesis update. Curr Opin Pulm Med 2015; 21 (6): 626–633. doi: 10.1097/MCP.0000000000000217.

27. Gruden JF, Ouanounou S, Tigges S et al. Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT. AJR Am J Roentgenol 2002; 179 (1): 149–157. doi: 10.2214/ajr.179.1.1790149.

28. Karnatapu SC, Ekechukwu K, Doke S et al. Application of radiology in management of incidental lung nodule. Ann Case Report 2022; 7: 868. doi: 10.29011/2574-7754.100868.

29. van Klaveren RJ, Oudkerk M, Prokop M et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009; 361 (23): 2221–2229. doi: 10.1056/NEJMoa0906085.

30. Travis WD, Brambilla E, Noguchi M et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011; 6 (2): 244–285. doi: 10.1097/JTO.0b013e318206a221.

31. Henschke CI, Yankelevitz DF, Yip R et al. Lung cancers diagnosed at annual CT screening: volume doubling times. Radiology 2012; 264 (1): 306. doi: 10.1148/radiol.12102489.

32. Horeweg N, van Rosmalen J, Heuvelmans MA et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol 2014; 15 (12): 1332–1341. doi: 10.1016/S1470-2045 (14) 70389-4.

33. McWilliams A, Tammemagi MC, Mayo JR et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med 2013; 369 (10): 910–919. doi: 10.1056/NEJMoa1214726.

34. Swensen SJ, Jett JR, Hartman TE et al. CT screening for lung cancer: five-year prospective experience. Radiology 2005; 235 (1): 259–265. doi: 10.1148/radiol.2351041662.

35. Walter JE, Heuvelmans MA, de Jong PA et al. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: analysis of data from the randomised, controlled NELSON trial. Lancet Oncol 2016; 17 (7): 907–916. doi: 10.1016/S1470-2045 (16) 30069-9.

36. Henschke CI, Yip R, Smith JP et al. CT screening for lung cancer: part-solid nodules in baseline and annual repeat rounds. AJR Am J Roentgenol 2016; 207 (6): 1176–1184. doi: 10.2214/AJR.16.16043.

37. Walter JE, Heuvelmans MA, Yousaf-Khan U et al. New subsolid pulmonary nodules in lung cancer screening: the NELSON trial. J Thorac Oncol 2018; 13 (9): 1410–1414. doi: 10.1016/j.jtho.2018.05.006.

38. Nambu A, Araki T, Taguchi Y et al. Focal area of ground-glass opacity and ground-glass opacity predominance on thin-section CT: discrimination between neoplastic and non-neoplastic lesions. Clin Radiol 2005; 60 (9): 1006–1017. doi: 10.1016/j.crad.2005.06.006.

39. Grewal RG, Austin JH. CT demonstration of calcification in carcinoma of the lung. J Comput Assist Tomogr 1994; 18 (6): 867–871. doi: 10.1097/00004728-199411000-00004.

40. Truong MT, Ko JP, Rossi SE et al. Update in the evaluation of the solitary pulmonary nodule. Radiographics 2014; 34 (6): 1658–1679. doi: 10.1148/rg.346130092.

41. Wang CW, Teng YH, Huang CC et al. Intrapulmonary lymph nodes: computed tomography findings with histopathologic correlations. Clin Imaging 2013; 37 (3): 487–492. doi: 10.1016/j.clinimag.2012.09.010.

42. Balata H, Ruparel M, O‘Dowd E et al. Analysis of the baseline performance of five UK lung cancer screening programmes. Lung Cancer 2021; 161: 136–140. doi: 10.1016/j.lungcan.2021.09.012.

43. Swensen SJ, Silverstein MD, Edell ES et al. Solitary pulmonary nodules: clinical prediction model versus physicians. Mayo Clin Proc 1999; 74 (4): 319–329. doi: 10.4065/74.4.319.

44. Shin KE, Lee KS, Yi CA et al. Subcentimeter lung nodules stable for 2 years at LDCT: long-term follow-up using volumetry. Respirology 2014; 19 (6): 921–928. doi: 10.1111/resp.12337.

45. Loverdos K, Fotiadis A, Kontogianni C et al. Lung nodules: a comprehensive review on current approach and management. Ann Thorac Med 2019; 14 (4): 226–238. doi: 10.4103/atm.ATM_110_19.

46. Song SH, Ahn JH, Lee HY et al. Prognostic impact of nomogram based on whole tumour size, tumour disappearance ratio on CT and SUVmax on PET in lung adenocarcinoma. Eur Radiol 2016; 26 (6): 1538–1546. doi: 10.1007/s00330-015-4029-0.

47. Lee KH, Goo JM, Park SJ et al. Correlation between the size of the solid component on thin-section CT and the invasive component on pathology in small lung adenocarcinomas manifesting as ground-glass nodules. J Thorac Oncol 2014; 9 (1): 74–82. doi: 10.1097/JTO.0000000000000019.

48. Cho J, Ko SJ, Kim SJ et al. Surgical resection of nodular ground-glass opacities without percutaneous needle aspiration or biopsy. BMC Cancer 2014; 14: 838. doi: 10.1186/1471-2407-14-838.

49. Cohen JG, Reymond E, Lederlin M et al. Differentiating pre-and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients. Eur J Radiol 2015; 84 (4): 738–744. doi: 10.1016/j.ejrad.2014.12.031.

50. Kim TJ, Park CM, Goo JM et al. Is there a role for FDG PET in the management of lung cancer manifesting predominantly as ground-glass opacity? AJR Am J Roentgenol 2012; 198 (1): 83–88. doi: 10.2214/AJR.11.6862.

51. Ministerstvo zdravotnictví České republiky. Věstník č. 3/2021. [online]. Dostupné z: https: //www.mzcr.cz/vestnik/vestnik-c-3-2021/.

52. Mírka H, Vašáková MK, Čierná-Peterová I et al. Populační pilotní program časného záchytu karcinomu plic v ČR, výsledky prvního datového auditu. Onkologie 2022; 16 (6): 327–331. doi: 10.36290/xon.2022.069.

53. RSNA. AI improves lung nodule detection on chest X-rays. [online]. Dostupné z: https: //www.rsna.org/news/2023/february/ai-improves-lung-nodule-detection.

54. Mao LT, Chen H, Liang MZ et al. Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening. Quant Imaging Med Surg 2019; 9 (2): 263–272. doi: 10.21037/qims.2019.02.02.