Skip to main content

Advertisement

Log in

Current investigative modalities for detecting and staging lung cancers: a comprehensive summary

  • Review Article
  • Published:
Indian Journal of Thoracic and Cardiovascular Surgery Aims and scope Submit manuscript

Abstract

This narrative review compares the advantages and drawbacks of imaging and other investigation modalities which currently assist with lung cancer diagnosis and staging, as well as those which are not routinely indicated for this. We examine plain film radiography, computed tomography (CT) (alone, as well as in conjunction with positron emission tomography (PET)), magnetic resonance imaging (MRI), ultrasound, and newer techniques such as image-guided bronchoscopy (IGB) and robotic bronchoscopy (RB). While a chest X-ray is the first-line imaging investigation in patients presenting with symptoms suggestive of lung cancer, it has a high positive predictive value (PPV) even after negative X-ray findings, which calls into question its value as part of a potential national screening programme. CT lowers the mortality for high-risk patients when compared to X-ray and certain scoring systems, such as the Brock model can guide the need for further imaging, like PET-CT, which has high sensitivity and specificity for diagnosing solitary pulmonary nodules as malignant, as well as for assessing small cell lung cancer spread. In practice, PET-CT is offered to everyone whose lung cancer is to be treated with a curative intent. In contrast, MRI is only recommended for isolated distant metastases. Similarly, ultrasound imaging is not used for diagnosis of lung cancer but can be useful when there is suspicion of intrathoracic lymph node involvement. Ultrasound imaging in the form of endobronchial ultrasonography (EBUS) is often used to aid tissue sampling, yet the diagnostic value of this technique varies widely between studies. RB is another novel technique that offers an alternative way to biopsy lesions, but further research on it is necessary. Lastly, thoracic surgical biopsies, particularly minimally invasive video-assisted techniques, have been used increasingly to aid in diagnosis and staging.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. World Health Organization International Agency for Research on Cancer, 2020. GLOBOCAN 2020: estimated cancer incidence, mortality and prevalence. 2020. https://gco.iarc.fr/today/data/factsheets/cancers/39-All-cancers-fact-sheet.pdf. Accessed 14 December 2021

  2. Samet JM, Brenner D, Brooks AL, et al. Health effects of exposure to radon. Washington, D.C.: National Academy Press; 1999.

    Google Scholar 

  3. Gilham C, Rake C, Burdett G, et al. Pleural mesothelioma and lung cancer risks in relation to occupational history and asbestos lung burden. Occup Environ Med. 2016;73:290–9. https://doi.org/10.1136/oemed-2015-103074.

    Article  Google Scholar 

  4. American Cancer Society. Lung cancer survival rates. 2019. https://www.cancer.org/content/dam/CRC/PDF/Public/8705.00.pdf. Accessed 14 December 2021.

  5. Howington JA, Blum MG, Chang AC, Balekian AA, Murthy SC. Treatment of stage I and II non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed. American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143:e278S–e313s. https://doi.org/10.1378/chest.12-2359.

    Article  Google Scholar 

  6. Royal College of Physicians. National Lung Cancer Audit annual report 2016. 2017. https://www.rcplondon.ac.uk/file/5794/download. Accessed 14 December 2021.

  7. Thandra KC, Barsouk A, Saginala K, Aluru JS, Barsouk A. Epidemiology of lung cancer. Contemp Oncol 2021;25:45–52. https://doi.org/10.5114/wo.2021.103829.

    Article  CAS  Google Scholar 

  8. Krist AH, Davidson KW, Mangione CM, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325:962–70. https://doi.org/10.1001/jama.2021.1117.

    Article  Google Scholar 

  9. NHS England- National Cancer Programme. Targeted screening for lung cancer with low radiation dose computed tomography. 2019. https://www.england.nhs.uk/wp-content/uploads/2019/02/targeted-lung-health-checks-standard-protocol-v1.pdf. Accessed 9 December 2021.

  10. Scenario: Referral for suspected lung or pleural cancer | Management | Lung and pleural cancers - recognition and referral | CKS | NICE [Internet]. Cks.nice.org.uk. 2021. https://cks.nice.org.uk/topics/lung-pleural-cancers-recognition-referral/management/referral-for-suspected-lung-or-pleural-cancer/. Accessed 13 December 2021.

  11. Bradley SH, Abraham S, Callister ME, et al. Sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms: a systematic review. Br J Gen Pract. 2019;69:e827–e835. https://doi.org/10.3399/bjgp19X706853.

    Article  Google Scholar 

  12. Bradley SH, Hatton NLF, Aslam R, et al. Estimating lung cancer risk from chest X-ray and symptoms: a prospective cohort study. Br J Gen Pract. 2021;71:e280–e286. https://doi.org/10.3399/bjgp20X713993.

    Article  Google Scholar 

  13. Silvestri GA, Gonzalez AV, Jantz MA, et al. Methods for staging non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest. 2013;143:e211S-e250S. https://doi.org/10.1378/chest.12-2355.

    Article  Google Scholar 

  14. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382:503–13. https://doi.org/10.1056/NEJMoa1911793.

    Article  Google Scholar 

  15. Aberle DR, Adams AM, Berg CD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395–409. https://doi.org/10.1056/NEJMoa1102873.

    Article  Google Scholar 

  16. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013;369:910–9. https://doi.org/10.1056/NEJMoa1214726.

    Article  CAS  Google Scholar 

  17. Winkler Wille MM, van Riel SJ, Saghir Z, et al. Predictive accuracy of the PanCan Lung cancer risk prediction model -external validation based on CT from the Danish Lung Cancer Screening Trial. Eur Radiol. 2015;25:3093–9. https://doi.org/10.1007/s00330-015-3689-0.

    Article  Google Scholar 

  18. Birchard KR. Transthoracic needle biopsy. Semin Intervent Radiol. 2011;28:87–97. https://doi.org/10.1055/s-0031-1273943.

    Article  Google Scholar 

  19. Tsai P-C, Yeh Y-C, Hsu P-K, Chen C-K, Chou T-Y, Wu Y-C. CT-guided core biopsy for peripheral sub-solid pulmonary nodules to predict predominant histological and aggressive subtypes of lung adenocarcinoma. Ann Surg Oncol. 2020;27:4405–12. https://doi.org/10.1245/s10434-020-08511-9.

  20. Fu Y-F, Li G-C, Cao W, Wang T, Shi Y-B. Computed tomography fluoroscopy-guided versus conventional computed tomography-guided lung biopsy: A systematic review and meta-analysis. J Comput Assist Tomogr. 2020;44:571–7. https://doi.org/10.1097/RCT.0000000000001044.

  21. Sabatino V, Russo U, D’Amuri F, et al. Pneumothorax and pulmonary hemorrhage after CT-guided lung biopsy: incidence, clinical significance and correlation. Radiol Med. 2021;126:170–7. https://doi.org/10.1007/s11547-020-01211-0.

    Article  Google Scholar 

  22. Appel E, Dommaraju S, Camacho A, et al. Dependent lesion positioning at CT-guided lung biopsy to reduce risk of pneumothorax. Eur Radiol. 2020;30:6369–6375. https://doi.org/10.1007/s00330-020-07025-y.

    Article  Google Scholar 

  23. Nour-Eldin NE, Alsubhi M, Naguib NN, et al. Risk factor analysis of pulmonary hemorrhage complicating CT-guided lung biopsy in coaxial and non-coaxial core biopsy techniques in 650 patients. Eur J Radiol. 2014;83:1945–52. https://doi.org/10.1016/j.ejrad.2014.06.023.

    Article  Google Scholar 

  24. Firmino M, Angelo G, Morais H, Dantas RM, Valentim R. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online. 2016;15:2. https://doi.org/10.1186/s12938-015-0120-7.

    Article  Google Scholar 

  25. Gu Y, Chi J, Liu J, et al. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput Biol Med. 2021;137:104806. https://doi.org/10.1016/j.compbiomed.2021.104806.

    Article  Google Scholar 

  26. Al-Jahdali H, Khan AN, Loutfi S, Al-Harbi AS. Guidelines for the role of FDG-PET/CT in lung cancer management. J Infect Public Health. 2012;5:S35–40. https://doi.org/10.1016/j.jiph.2012.09.003.

    Article  Google Scholar 

  27. Farwell MD, Pryma DA, Mankoff DA. PET/CT imaging in cancer: current applications and future directions. Cancer. 2014;120:3433–45. https://doi.org/10.1002/cncr.28860.

    Article  CAS  Google Scholar 

  28. Liberti MV, Locasale JW. The Warburg effect: how does it benefit cancer cells? Trends Biochem Sci. 2016;41:211–8. https://doi.org/10.1016/j.tibs.2015.12.001.

    Article  CAS  Google Scholar 

  29. Ruilong Z, Daohai X, Li G, Xiaohong W, Chunjie W, Lei T. Diagnostic value of 18F-FDG-PET/CT for the evaluation of solitary pulmonary nodules: a systematic review and meta-analysis. Nucl Med Commun. 2017;38:67–75. https://doi.org/10.1097/MNM.0000000000000605.

    Article  Google Scholar 

  30. Lu Y-Y, Chen J-H, Liang J-A, Chu S, Lin W-Y, Kao C-H. 18F-FDG PET or PET/CT for detecting extensive disease in small-cell lung cancer: A systematic review and meta-analysis. Nucl Med Commun. 2014;35:697–703. https://doi.org/10.1097/MNM.0000000000000122.

  31. Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997;157:849–55. https://doi.org/10.1001/archinte.1997.00440290031002.

    Article  CAS  Google Scholar 

  32. Herder GJ, van Tinteren H, Golding RP, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest. 2005;128:2490–6. https://doi.org/10.1378/chest.128.4.2490.

    Article  Google Scholar 

  33. Long NM, Smith CS. Causes and imaging features of false positives and false negatives on F-PET/CT in oncologic imaging. Insights Imaging. 2011;2:679–98. https://doi.org/10.1007/s13244-010-0062-3.

    Article  Google Scholar 

  34. Deppen SA, Blume JD, Kensinger CD, et al. Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis. JAMA. 2014;312:1227–36. https://doi.org/10.1001/jama.2014.11488.

    Article  CAS  Google Scholar 

  35. Verboom P, van Tinteren H, Hoekstra OS, et al. Cost-effectiveness of FDG-PET in staging non-small cell lung cancer: The PLUS study. Eur J Nucl Med Mol Imaging. 2003;30:1444–9. https://doi.org/10.1007/s00259-003-1199-9.

    Article  Google Scholar 

  36. Sim AJ, Kaza E, Singer L, Rosenberg SA. A review of the role of MRI in diagnosis and treatment of early stage lung cancer. Clin Transl Radiat Oncol. 2020;24:16–22. https://doi.org/10.1016/j.ctro.2020.06.002.

    Article  Google Scholar 

  37. Zhang Y, Qin Q, Li B, Wang J, Zhang K. Magnetic resonance imaging for N staging in non-small cell lung cancer: a systematic review and meta-analysis. Thorac Cancer. 2015;6:123–32. https://doi.org/10.1111/1759-7714.12203.

    Article  Google Scholar 

  38. Taylor SA, Mallett S, Ball S, et al. Diagnostic accuracy of whole-body MRI versus standard imaging pathways for metastatic disease in newly diagnosed non-small-cell lung cancer: the prospective streamline L trial. Lancet Respir Med. 2019;7:523–32. https://doi.org/10.1016/S2213-2600(19)30090-6.

    Article  Google Scholar 

  39. Koyama H, Ohno Y, Seki S, et al. Magnetic resonance imaging for lung cancer. J Thorac Imaging. 2013;28:138–50. https://doi.org/10.1097/RTI.0b013e31828d4234.

    Article  Google Scholar 

  40. Raptis CA, McWilliams SR, Ratkowski KL, Broncano J, Green DB, Bhalla S. Mediastinal and pleural MR imaging: practical approach for daily practice. Radiographics. 2018;38:37–55. https://doi.org/10.1148/rg.2018170091.

    Article  Google Scholar 

  41. Zhang X, Fu Z, Gong G, et al. Implementation of diffusion-weighted magnetic resonance imaging in target delineation of central lung cancer accompanied with atelectasis in precision radiotherapy. Oncol Lett. 2017;14:2677–82. https://doi.org/10.3892/ol.2017.6479.

    Article  Google Scholar 

  42. Rami-Porta R, Call S, Dooms C, et al. Lung cancer staging: a concise update. Eur Respir J. 2018;51:1800190. https://doi.org/10.1183/13993003.00190-2018.

  43. Korevaar DA, Crombag LM, Cohen JF, Spijker R, Bossuyt PM, Annema JT. Added value of combined endobronchial and oesophageal endosonography for mediastinal nodal staging in lung cancer: a systematic review and meta-analysis. Lancet Respir Med. 2016;4:960–8. https://doi.org/10.1016/S2213-2600(16)30317-4.

    Article  Google Scholar 

  44. Patrucco F, Gavelli F, Daverio M, et al. Electromagnetic navigation bronchoscopy: where are we now? Five years of a single-center experience. Lung. 2018;196:721–7. https://doi.org/10.1007/s00408-018-0161-3.

    Article  Google Scholar 

  45. Gex G, Pralong JA, Combescure C, Seijo L, Rochat T, Soccal PM. Diagnostic yield and safety of electromagnetic navigation bronchoscopy for lung nodules: a systematic review and meta-analysis. Respiration. 2014;87:165–76. https://doi.org/10.1159/000355710.

    Article  Google Scholar 

  46. Folch EE, Pritchett MA, Nead MA, et al. Electromagnetic navigation bronchoscopy for peripheral pulmonary lesions: one-year results of the prospective, multicenter NAVIGATE study. J Thorac Oncol. 2019;14:445–58. https://doi.org/10.1016/j.jtho.2018.11.013.

    Article  Google Scholar 

  47. Yarmus L, Akulian J, Wahidi M, et al. A prospective randomized comparative study of three guided bronchoscopic approaches for investigating pulmonary nodules: The PRECISION-1 study. Chest. 2020;157:694–701. https://doi.org/10.1016/j.chest.2019.10.016.

    Article  Google Scholar 

  48. Chen AC, Pastis NJ, Mahajan AK, et al. Robotic bronchoscopy for peripheral pulmonary lesions: a multicenter pilot and feasibility study (BENEFIT). Chest. 2021;159:845–52. https://doi.org/10.1016/j.chest.2020.08.2047.

    Article  Google Scholar 

  49. Kumar A, Caceres JD, Vaithilingam S, Sandhu G, Meena NK. Robotic bronchoscopy for peripheral pulmonary lesion biopsy: evidence-based review of the two platforms. Diagnostics (Basel). 2021;11:1479. https://doi.org/10.3390/diagnostics11081479.

    Article  Google Scholar 

  50. Hansen HJ, Petersen RH. Video-assisted thoracoscopic lobectomy using a standardized three-port anterior approach - the Copenhagen experience. Ann Cardiothorac Surg. 2012;1:70–6.

    Google Scholar 

  51. Wang L, Liu D, Lu J, Zhang S, Yang X. The feasibility and advantage of uniportal video-assisted thoracoscopic surgery (VATS) in pulmonary lobectomy. BMC Cancer. 2017;17:75.

  52. Detterbeck FC, Lewis SZ, Diekemper R, Addrizzo-Harris D, Alberts WM. Executive Summary: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143:7s–37s.

  53. Sihoe ADL, Hiranandani R, Wong H, Yeung ESL. Operating on a suspicious lung mass without a preoperative tissue diagnosis: pros and cons. Eur J Cardiothorac Surg. 2013;44:231–7.

  54. Al-Ameri M, Bergman P, Franco-Cereceda A, Sartipy U. Video-assisted thoracoscopic versus open thoracotomy lobectomy: a Swedish nationwide cohort study. J Thorac Dis. 2018;10:3499–506.

    Article  Google Scholar 

  55. Bendixen M, Jørgensen OD, Kronborg C, Andersen C, Licht PB. Postoperative pain and quality of life after lobectomy via video-assisted thoracoscopic surgery or anterolateral thoracotomy for early stage lung cancer: a randomised controlled trial. Lancet Oncol. 2016;17:836–44.

    Article  Google Scholar 

  56. Lim EKS, Batchelor TJP, Dunning J, et al. Video-assisted thoracoscopic versus open lobectomy in patients with early-stage lung cancer: One-year results from a randomized controlled trial (VIOLET). J Clin Oncol. 2021;39:8504.

  57. Vilmann P, Clementsen PF, Colella S, et al. Combined endobronchial and oesophageal endosonography for the diagnosis and staging of lung cancer. Eur Respir J. 2015;46:40–60.

Download references

Funding

None.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bejoy Philip.

Ethics declarations

Ethical statement

No ethical approval needed as it is a review article.

Conflict of interest

All authors declare they have no conflicts of interest relevant to this article.

Informed consent

No patient consents were needed for this review study.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Philip, B., Jain, A., Wojtowicz, M. et al. Current investigative modalities for detecting and staging lung cancers: a comprehensive summary. Indian J Thorac Cardiovasc Surg 39, 42–52 (2023). https://doi.org/10.1007/s12055-022-01430-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12055-022-01430-2

Keywords

Navigation