Skip to main content

Advertisement

SpringerLink
Log in

18F-FDG PET/CT and whole-body MRI diagnostic performance in M staging for non–small cell lung cancer: a systematic review and meta-analysis

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the diagnostic test accuracy of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT), whole-body magnetic resonance imaging (WB-MRI), and whole-body diffusion-weighted imaging (WB-DWI) for the detection of metastases in patients with non–small cell lung cancer (NSCLC).

Methods

MEDLINE, Embase, and Cochrane Library databases were searched up to June 2019. Studies were selected if they reported data that could be used to construct contingency tables to compare 18F-FDG PET/CT, WB-MRI, and WB-DWI. Two authors independently extracted data on study characteristics and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies. Forest plots were generated for sensitivity and specificity of 18F-FDG PET/CT, WB-MRI, and whole-body diffusion-weighted imaging (WB-DWI). Summary receiver operating characteristic plots were created.

Results

The 4 studies meeting inclusion criteria had a total of 564 patients and 559 lesions, 233 of which were metastases. In studies of 18F-FDG PET/CT, the pooled estimates of sensitivity and specificity were 0.83 (95% confidence interval [CI], 0.54–0.95) and 0.93 (95% CI, 0.87–0.96), respectively. For WB-MRI, pooled sensitivity was 0.92 (95% CI, 0.18–1.00) and pooled specificity was 0.93 (95% CI, 0.85–0.95). Pooled sensitivity and specificity for WB-DWI were 0.78 (95% CI, 0.46–0.93) and 0.91 (95% CI, 0.79–0.96), respectively. There was no statistical difference between the diagnostic odds ratio of WB-MRI and WB-DWI compared with that of PET/CT (p = 0.186 for WB-DWI; p = 0.638 for WB-MRI).

Conclusion

WB-MRI and DWI are radiation-free alternatives with comparable diagnostic performance to 18F-FDG PET/CT for M staging of NSCLC.

Key Points

• Whole-body MRI with or without diffusion-weighted imaging has a high accuracy for the diagnostic evaluation of metastases in patients with non–small cell lung cancer.

• Whole-body MRI may be used as a non-invasive and radiation-free alternative to positron emission tomography with CT with similar diagnostic performance.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

18F-FDG PET/CT:

18F-Fluorodeoxyglucose positron emission tomography/computed tomography

ADC:

Apparent diffusion coefficient

DWI:

Diffusion-weighted imaging

EQUATOR:

Enhancing the Quality and Transparency of Health Research

FP:

False positive

NSCLC:

Non–small cell lung cancer

PRISMA:

Preferred Reporting Items for Systematic Reviews

QUADAS:

Quality Assessment of Diagnostic Accuracy Studies

SUV:

Standardized uptake value

TN:

True positive

WB-DWI:

Whole-body diffusion-weighted imaging

WB-MRI:

Whole-body magnetic resonance imaging

References

  1. World Health Organization (2016) Fact sheets. Cancer. World Health Organization, Geneva. Available via http://www.who.int/news-room/fact-sheets/detail/cancer. Accessed 1 Feb 2018

  2. Xiang D, Zhang B, Doll D, Shen K, Kloecker G, Freter C (2013) Lung cancer screening: from imaging to biomarker. Biomark Res 1:1–9. https://doi.org/10.1186/2050-7771-1-4

  3. Liam CK, Andarini S, Lee P, Ho JC, Chau NQ, Tscheikuna J (2015) Lung cancer staging now and in the future. Respirology 20:526–534. https://doi.org/10.1111/resp.12489

  4. Bruzzi JF, Munden RF (2006) PET/CT imaging of lung cancer. J Thorac Imaging 21:123–136

    Article  PubMed  Google Scholar 

  5. Berger A (2002) Magnetic resonance imaging. BMJ 324:35

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lee MH, Kim SR, Park SY et al (2012) Application of whole-body MRI to detect the recurrence of lung cancer. Magn Reson Imaging 30:1439–1445. https://doi.org/10.1016/j.mri.2012.04.014

    Article  PubMed  Google Scholar 

  7. Li B, Li Q, Nie W, Liu S (2014) Diagnostic value of whole-body diffusion-weighted magnetic resonance imaging for detection of primary and metastatic malignancies: a meta-analysis. Eur J Radiol 83:338–344. https://doi.org/10.1016/j.ejrad.2013.11.017

    Article  PubMed  Google Scholar 

  8. Dias AB, Zanon M, Altmayer S et al (2019) Fluorine 18–FDG PET/CT and diffusion-weighted MRI for malignant versus benign pulmonary lesions: a meta-analysis. Radiology 290:525–534. https://doi.org/10.1148/radiol.2018181159

    Article  Google Scholar 

  9. Taylor SA, Mallett S, Ball S et al (2019) 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 7:523–532. https://doi.org/10.1016/S2213-2600(19)30090-6

    Article  PubMed  PubMed Central  Google Scholar 

  10. Whiting PF, Rutjes AWS, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

    Article  PubMed  Google Scholar 

  11. Moher D, Liberati A, Tetzlaff J, Altman DG (2010) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg 8:336–341. https://doi.org/10.1016/j.ijsu.2010.02.007

    Article  PubMed  Google Scholar 

  12. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58:982–990. https://doi.org/10.1016/J.JCLINEPI.2005.02.022

  13. Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148(3):839–843. https://doi.org/10.1148/radiology.148.3.6878708

    Article  CAS  PubMed  Google Scholar 

  14. Kiewiet JJ, Leeuwenburgh MM, Bipat S, Bossuyt PM, Stoker J, Boermeester MA (2012) A systematic review and meta-analysis of diagnostic performance of imaging in acute cholecystitis. Radiology 264(3):708–720. https://doi.org/10.1148/radiol.12111561

  15. Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM (2003) The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 56(11):1129–1135. https://doi.org/10.1016/s0895-4356(03)00177-x

  16. Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58:882–893. https://doi.org/10.1016/j.jclinepi.2005.01.016

    Article  PubMed  Google Scholar 

  17. Ohno Y, Koyama H, Onishi Y et al (2008) Non-small cell lung cancer: whole-body MR examination for M-stage assessment-utility for whole-body diffusion-weighted imaging compared with integrated FDG PET/CT. Radiology 248:643–654. https://doi.org/10.1148/radiol.2482072039

    Article  PubMed  Google Scholar 

  18. Chen W, Jian W, Li HT et al (2010) Whole-body diffusion-weighted imaging vs. FDG-PET for the detection of non-small-cell lung cancer. How do they measure up? Magn Reson Imaging 28:613–620. https://doi.org/10.1016/j.mri.2010.02.009

    Article  CAS  PubMed  Google Scholar 

  19. Ohno Y, Koyama H, Yoshikawa T et al (2015) Three-way comparison of whole-body MR, coregistered whole-body FDG PET/MR, and integrated whole-body FDG PET/CT imaging: TNM and stage assessment capability for non–small cell lung cancer patients. Radiology 275:849–861. https://doi.org/10.1148/radiol.14140936

    Article  PubMed  Google Scholar 

  20. Yi CA, Shin KM, Lee KS et al (2008) Non–small cell lung cancer staging: efficacy comparison of integrated PET/CT versus 3.0-T whole-body MR imaging. Radiology 248:632–642. https://doi.org/10.1148/radiol.2482071822

    Article  PubMed  Google Scholar 

  21. Morone M, Bali MA, Tunariu N et al (2017) Whole-body MRI: current applications in oncology. AJR Am J Roentgenol 209:W336–W349. https://doi.org/10.2214/AJR.17.17984

    Article  PubMed  Google Scholar 

  22. Usuda K, Funazaki A, Maeda R et al (2017) Economic benefits and diagnostic quality of diffusion-weighted magnetic resonance imaging for primary lung cancer. Ann Thorac Cardiovasc Surg 23:275–280. https://doi.org/10.5761/atcs.ra.17-00097

    Article  PubMed  PubMed Central  Google Scholar 

  23. Plathow C, Walz M, Lichy MP et al (2008) Kostenüberlegungen zur Ganzkörper-MRT und PET-CT im Rahmen des onkologischen Stagings. Radiologe 48:384–396. https://doi.org/10.1007/s00117-007-1547-z

    Article  CAS  PubMed  Google Scholar 

  24. Shen G, Lan Y, Zhang K, Ren P, Jia Z (2017) Comparison of 18F-FDG PET/CT and DWI for detection of mediastinal nodal metastasis in non-small cell lung cancer: a meta-analysis. PLoS One 12:1–18. https://doi.org/10.1371/journal.pone.0173104

  25. Wu LM, Xu JR, Gu HY et al (2012) Preoperative mediastinal and hilar nodal staging with diffusion-weighted magnetic resonance imaging and fluorodeoxyglucose positron emission tomography/computed tomography in patients with non-small-cell lung cancer: which is better? J Surg Res 178:304–314. https://doi.org/10.1016/j.jss.2012.03.074

    Article  PubMed  Google Scholar 

  26. Devillé WL, Buntinx F, Bouter LM et al (2002) Conducting systematic reviews of diagnostic studies: didactic guidelines. BMC Med Res Methodol 2:9. https://doi.org/10.1186/1471-2288-2-9

    Article  PubMed  PubMed Central  Google Scholar 

  27. Deppen SA, Blume JD, Kensinger CD et al (2014) Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis. JAMA 312:1227–1236. https://doi.org/10.1001/jama.2014.11488

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. World Health Organization (2018) Global tuberculosis report 2018. World Health Organization, Geneva Available at https://apps.who.int/iris/handle/10665/274453

  29. Sun D, Hu L, Cai Y et al (2014) A systematic review of risk factors for brain metastases and value of prophylactic cranial irradiation in non-small cell lung cancer. Asian Pac J Cancer Prev 15:1233–1239. https://doi.org/10.7314/APJCP.2014.15.3.1233

    Article  PubMed  Google Scholar 

  30. Ettinger DS, Wood DE, Akerley W et al (2014) Non–small cell lung cancer, version 1.2015. J Natl Compr Canc Netw 12:1738–1176

    Article  PubMed  Google Scholar 

  31. Lee HY, Lee KS, Kim B-T et al (2009) Diagnostic efficacy of PET/CT plus brain MR imaging for detection of extrathoracic metastases in patients with lung adenocarcinoma. J Korean Med Sci 24:1132. https://doi.org/10.3346/jkms.2009.24.6.1132

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yang J, Zhang Y, Sun X, et al (2018) The prognostic value of multiorgan metastases in patients with non-small cell lung cancer and its variants: a SEER-based study. J Cancer Res Clin Oncol 0:0. https://doi.org/10.1007/s00432-018-2702-9

  33. Takenaka D, Ohno Y, Matsumoto K et al (2009) Detection of bone metastases in non-small cell lung cancer patients: comparison of whole-body diffusion-weighted imaging (DWI), whole-body MR imaging without and with DWI, whole-body FDGPET/CT, and bone scintigraphy. J Magn Reson Imaging 30:298–308. https://doi.org/10.1002/jmri.21858

    Article  PubMed  Google Scholar 

  34. Cronin P, Kelly AM, Altaee D, Foerster B, Petrou M, Dwamena BA (2018) How to perform a systematic review and meta-analysis of diagnostic imaging studies. Acad Radiol 25:573–593. https://doi.org/10.1016/j.acra.2017.12.007

Download references

Acknowledgments

We acknowledge the contribution of the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brazil (CAPES).

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Postgraduate Program in Medicine and Health Sciences, Pontificia Universidade Catolica do Rio Grande do Sul, Av. Ipiranga, 6690, Porto Alegre, 90619900, Brazil

    Tássia Machado Medeiros, Stephan Altmayer, Guilherme Watte, Julia Hoefel Paes, Rita Mattiello, Francisco de Souza Santos & Bruno Hochhegger

  2. Medical Imaging Research Lab, LABIMED, Department of Radiology, Pavilhão Pereira Filho Hospital, Irmandade Santa Casa de Misericórdia de Porto Alegre, Av. Independência, 75, Porto Alegre, 90020160, Brazil

    Stephan Altmayer, Matheus Zanon, Adriano Basso Dias & Bruno Hochhegger

  3. Department of Radiology, Federal University of Health Sciences of Porto Alegre, R. Sarmento Leite, 245, Porto Alegre, 90050170, Brazil

    Matheus Zanon & Bruno Hochhegger

  4. Department of Radiology, Hospital de Clínicas de Porto Alegre, R. Ramiro Barcelos, 2350, Porto Alegre, 90035903, Brazil

    Natália Henz Concatto

  5. Department of Radiology, College of Medicine - University of Florida, 1600 SW Archer Rd, Gainesville, FL, 32611, USA

    Tan-Lucien Mohammed & Nupur Verma

Authors
  1. Tássia Machado Medeiros
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephan Altmayer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guilherme Watte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matheus Zanon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adriano Basso Dias
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Natália Henz Concatto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julia Hoefel Paes
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Rita Mattiello
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Francisco de Souza Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tan-Lucien Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nupur Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Bruno Hochhegger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bruno Hochhegger.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Bruno Hochhegger, MD, PhD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was not required for this study because this represents a literature review.

Ethical approval

Institutional Review Board approval was not required because this represents a literature review and meta-analysis.

Methodology

• Diagnostic or prognostic study

• Multicenter study

Additional information

Publisher’s note

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

Electronic supplementary material

ESM 1

(DOCX 431 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Machado Medeiros, T., Altmayer, S., Watte, G. et al. 18F-FDG PET/CT and whole-body MRI diagnostic performance in M staging for non–small cell lung cancer: a systematic review and meta-analysis. Eur Radiol 30, 3641–3649 (2020). https://doi.org/10.1007/s00330-020-06703-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-06703-1

Keywords

Search

Navigation