Skip to main content

Advertisement

SpringerLink
Log in

Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Purpose

To determine the test-retest repeatability of Apparent Diffusion Coefficient (ADC) measurements across institutions and MRI vendors, plus investigate the effect of post-processing methodology on measurement precision.

Methods

Thirty malignant lung lesions >2 cm in size (23 patients) were scanned on two occasions, using echo-planar-Diffusion-Weighted (DW)-MRI to derive whole-tumour ADC (b = 100, 500 and 800smm-2). Scanning was performed at 4 institutions (3 MRI vendors). Whole-tumour volumes-of-interest were copied from first visit onto second visit images and from one post-processing platform to an open-source platform, to assess ADC repeatability and cross-platform reproducibility.

Results

Whole-tumour ADC values ranged from 0.66-1.94x10-3mm2s-1 (mean = 1.14). Within-patient coefficient-of-variation (wCV) was 7.1% (95% CI 5.7–9.6%), limits-of-agreement (LoA) -18.0 to 21.9%. Lesions >3 cm had improved repeatability: wCV 3.9% (95% CI 2.9–5.9%); and LoA -10.2 to 11.4%. Variability for lesions <3 cm was 2.46 times higher. ADC reproducibility across different post-processing platforms was excellent: Pearson’s R2 = 0.99; CoV 2.8% (95% CI 2.3-3.4%); and LoA -7.4 to 8.0%.

Conclusion

A free-breathing DW-MRI protocol for imaging malignant lung tumours achieved satisfactory within-patient repeatability and was robust to changes in post-processing software, justifying its use in multi-centre trials. For response evaluation in individual patients, a change in ADC >21.9% will reflect treatment-related change.

Key Points

• In lung cancer, free-breathing DWI-MRI produces acceptable images with evaluable ADC measurement.

• ADC repeatability coefficient-of-variation is 7.1% for lung tumours >2 cm.

• ADC repeatability coefficient-of-variation is 3.9% for lung tumours >3 cm.

• ADC measurement precision is unaffected by the post-processing software used.

• In multicentre trials, 22% increase in ADC indicates positive treatment response.

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
Fig. 4

Similar content being viewed by others

Abbreviations

DW-MRI:

Diffusion-weighted magnetic resonance imaging

ADC:

Apparent diffusion coefficient

NSCLC:

Non small-cell lung cancer

SCLC:

Small-cell lung cancer

EORTC:

European Organization for Research and Treatment of Cancer

CRUK:

Cancer Research UK

UK:

United Kingdom

GE:

General Electric

STIR:

Short-tau inversion recovery

NSA:

Number of signal averages

LoA:

Limits of Agreement

wCV:

Within subject Coefficient of Variation

ICC:

Intra-class correlation

CCC:

Concordance correlation coefficient

IDL:

Interactive digital language

DICOM:

Digital Imaging and Communications in Medicine

EPSRC:

Engineering and Physical Sciences Research Council

NIHR:

National Institute for Health Research (UK)

NHS:

National Health Service

ICR:

Institute of Cancer Research (UK)

RMH:

Royal Marsden Hospital (UK)

MRC:

Medical Research Council (UK)

References

  1. O'Flynn EA, DeSouza NM (2011) Functional magnetic resonance: biomarkers of response in breast cancer. Breast Cancer Res 13(1):204

    Article  PubMed  PubMed Central  Google Scholar 

  2. Kyriazi S, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL et al (2011) Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology 261(1):182–192

    Article  PubMed  Google Scholar 

  3. Blackledge MD, Collins DJ, Tunariu N, Orton MR, Padhani AR, Leach MO et al (2014) Assessment of treatment response by total tumor volume and global apparent diffusion coefficient using diffusion-weighted MRI in patients with metastatic bone disease: a feasibility study. PLoS One 9(4), e91779

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Concatto NH, Watte G, Marchiori E, Irion K, Felicetti JC, Camargo JJ et al (2016) Magnetic resonance imaging of pulmonary nodules: accuracy in a granulomatous disease–endemic region. Eur Radiol 26(9):2915–2920

    Article  Google Scholar 

  5. Shen G, Jia Z, Deng H (2016) Apparent diffusion coefficient values of diffusion-weighted imaging for distinguishing focal pulmonary lesions and characterizing the subtype of lung cancer: a meta-analysis. Eur Radiol 26(2):556–566

    Article  PubMed  Google Scholar 

  6. Kessler LG, Barnhart HX, Buckler AJ, Choudhury KR, Kondratovich MV, Toledano A et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24(1):9–26

    Article  PubMed  Google Scholar 

  7. Sullivan DC, Obuchowski NA, Kessler LG, Raunig DL, Gatsonis C, Huang EP et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277(3):813–825

    Article  PubMed  PubMed Central  Google Scholar 

  8. Raunig DL, McShane LM, Pennello G, Gatsonis C, Carson PL, Voyvodic JT et al (2015) Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res 24(1):27–67

    Article  PubMed  Google Scholar 

  9. Bernardin L, Douglas NH, Collins DJ, Giles SL, O'Flynn EA, Orton M et al (2014) Diffusion-weighted magnetic resonance imaging for assessment of lung lesions: repeatability of the apparent diffusion coefficient measurement. Eur Radiol 24(2):502–511

    Article  CAS  PubMed  Google Scholar 

  10. Cui L, Yin J-B, Hu C-H, Gong S-C, Xu J-F, Yang J-S (2016) Inter-and intraobserver agreement of ADC measurements of lung cancer in free breathing, breath-hold and respiratory triggered diffusion-weighted MRI. Clin Imaging 40(5):892–896

    Article  PubMed  Google Scholar 

  11. Reischauer C, Froehlich JM, Pless M, Binkert CA, Koh DM, Gutzeit A (2014) Early treatment response in non-small cell lung cancer patients using diffusion-weighted imaging and functional diffusion maps--a feasibility study. PLoS One 9(10), e108052

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Yu J, Li W, Zhang Z, Yu T, Li D (2014) Prediction of early response to chemotherapy in lung cancer by using diffusion-weighted MR imaging. Sci World J 2014:135841

    Google Scholar 

  13. Tsuchida T, Morikawa M, Demura Y, Umeda Y, Okazawa H, Kimura H (2013) Imaging the early response to chemotherapy in advanced lung cancer with diffusion-weighted magnetic resonance imaging compared to fluorine-18 fluorodeoxyglucose positron emission tomography and computed tomography. J Magnet Resonance Imaging: JMRI 38(1):80–88

    Article  Google Scholar 

  14. Yabuuchi H, Hatakenaka M, Takayama K, Matsuo Y, Sunami S, Kamitani T et al (2011) Non-small cell lung cancer: detection of early response to chemotherapy by using contrast-enhanced dynamic and diffusion-weighted MR imaging. Radiology 261(2):598–604

    Article  PubMed  Google Scholar 

  15. Sun YS, Cui Y, Tang L, Qi LP, Wang N, Zhang XY et al (2011) Early evaluation of cancer response by a new functional biomarker: apparent diffusion coefficient. AJR Am J Roentgenol 197(1):W23–W29

    Article  PubMed  Google Scholar 

  16. Okuma T, Matsuoka T, Yamamoto A, Hamamoto S, Nakamura K, Inoue Y (2009) Assessment of early treatment response after CT-guided radiofrequency ablation of unresectable lung tumours by diffusion-weighted MRI: a pilot study. Br J Radiol 82(984):989–994

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chang Q, Wu N, Ouyang H, Huang Y (2012) Diffusion-weighted magnetic resonance imaging of lung cancer at 3.0 T: a preliminary study on monitoring diffusion changes during chemoradiation therapy. Clin Imaging 36(2):98–103

    Article  PubMed  Google Scholar 

  18. Ohno Y, Koyama H, Yoshikawa T, Matsumoto K, Aoyama N, Onishi Y et al (2012) Diffusion-weighted MRI versus 18F-FDG PET/CT: performance as predictors of tumor treatment response and patient survival in patients with non-small cell lung cancer receiving chemoradiotherapy. AJR Am J Roentgenol 198(1):75–82

    Article  PubMed  Google Scholar 

  19. Regier M, Derlin T, Schwarz D, Laqmani A, Henes FO, Groth M et al (2012) Diffusion weighted MRI and 18F-FDG PET/CT in non-small cell lung cancer (NSCLC): does the apparent diffusion coefficient (ADC) correlate with tracer uptake (SUV)? Eur J Radiol 81(10):2913–2918

    Article  CAS  PubMed  Google Scholar 

  20. Weiss E, Ford JC, Olsen KM, Karki K, Saraiya S, Groves R et al (2016) Apparent diffusion coefficient (ADC) change on repeated diffusion-weighted magnetic resonance imaging during radiochemotherapy for non-small cell lung cancer: a pilot study. Lung Cancer 96:113–119

    Article  PubMed  Google Scholar 

  21. O’Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al (2016) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol

  22. Douglas N, Winfield J, deSouza NM, Collins DJ, Orton MO (2013) Development of a phantom for quality assurance in multicentre clinical trials with diffusion-weighted MRI. Proc Int Soc Magnet Resonance Med. Presentation number 3114

  23. Satoh S, Kitazume Y, Ohdama S, Kimula Y, Taura S, Endo Y (2008) Can malignant and benign pulmonary nodules be differentiated with diffusion-weighted MRI? AJR Am J Roentgenol 191(2):464–470

    Article  PubMed  Google Scholar 

  24. Blackledge MD, Leach MO, Collins DJ, Koh DM (2011) Computed diffusion-weighted MR imaging may improve tumor detection. Radiology 261(2):573–581

    Article  PubMed  Google Scholar 

  25. Forkman J (2009) Estimator and tests for common coefficients of variation in normal distributions. Comm Stat—Theory Methods 38(2):233–251

    Article  Google Scholar 

  26. Weller A, O'Brien ME, Ahmed M, Popat S, Bhosle J, McDonald F et al (2016) Mechanism and non-mechanism based imaging biomarkers for assessing biological response to treatment in non-small cell lung cancer. Eur J Cancer 59:65–78

    Article  CAS  PubMed  Google Scholar 

  27. Liu Y, de Souza NM, Shankar LK, Kauczor HU, Trattnig S, Collette S et al (2015) A risk management approach for imaging biomarker-driven clinical trials in oncology. Lancet Oncol 16(16):e622–e628

    Article  PubMed  Google Scholar 

  28. Wade O (1954) Movements of the thoracic cage and diaphragm in respiration. J Physiol 124(2):193–212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Plathow C, Fink C, Ley S, Puderbach M, Eichinger M, Zuna I et al (2004) Measurement of tumor diameter-dependent mobility of lung tumors by dynamic MRI. Radiother Oncol: J Eur Soc Ther Radiol Oncol 73(3):349–354

    Article  Google Scholar 

  30. Hogg N, Winfield J, Collins DJ, de Souza NM, Orton M (2012) Development of a perfusion insensitivemeasurement of the apparent diffusion coefficient: a simulation. ESMRMB 2012, 29th Annual Scientific Meeting, Lisbon. Book of Abstracts (57)

  31. Taouli B, Beer AJ, Chenevert T, Collins D, Lehman C, Matos C et al (2016) Diffusion-weighted imaging outside the brain: Consensus statement from an ISMRM-sponsored workshop. J Magnet Resonance Imaging: JMRI 44(3):521–540

    Article  Google Scholar 

  32. Brihuega‐Moreno O, Heese FP, Hall LD (2003) Optimization of diffusion measurements using Cramer‐Rao lower bound theory and its application to articular cartilage. Magn Reson Med 50(5):1069–1076

    Article  PubMed  Google Scholar 

  33. Saritas EU, Lee JH, Nishimura DG (2011) SNR dependence of optimal parameters for apparent diffusion coefficient measurements. IEEE Trans Med Imaging 30(2):424–437

    Article  PubMed  Google Scholar 

  34. Tan ET, Marinelli L, Slavens ZW, King KF, Hardy CJ (2013) Improved correction for gradient nonlinearity effects in diffusion-weighted imaging. J Magnet Resonance Imaging: JMRI 38(2):448–453

    Article  Google Scholar 

  35. Rata M, Collins DJ, Darcy J, Messiou C, Tunariu N, Desouza N et al (2015) Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions. Eur Radiol

  36. Ashton E, Raunig D, Ng C, Kelcz F, McShane T, Evelhoch J (2008) Scan-rescan variability in perfusion assessment of tumors in MRI using both model and data-derived arterial input functions. J Magnet Resonance Imaging: JMRI 28(3):791–796

    Article  Google Scholar 

  37. Rata M, Collins DJ, Darcy J, Messiou C, Tunariu N, Desouza N et al (2016) Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions. Eur Radiol 26(7):1991–1998

    Article  PubMed  Google Scholar 

  38. Scaranelo AM, Eiada R, Jacks LM, Kulkarni SR, Crystal P (2012) Accuracy of unenhanced MR imaging in the detection of axillary lymph node metastasis: study of reproducibility and reliability. Radiology 262(2):425–434

    Article  PubMed  Google Scholar 

  39. Padhani AR, Liu G, Koh DM, Chenevert TL, Thoeny HC, Takahara T et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia (New York, NY) 11(2):102–125

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge CRUK and EPSRC support to the Cancer Imaging Centre at ICR and RMH in association with MRC & Dept of Health C1060/A10334, C1060/A16464 and NHS funding to the NIHR Biomedical Research Centre and the Clinical Research Facility in Imaging. AW and M-VP were funded by Innovative Medicines Initiative Joint Undertaking under grant agreement number 115151.

Author information

Authors and Affiliations

  1. CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT, UK

    Alex Weller, Marianthi Vasiliki Papoutsaki, Matthew Blackledge & Nandita M deSouza

  2. University of Manchester, Manchester, UK

    John C. Waterton

  3. Humanitas University, Milan, Italy

    Arturo Chiti

  4. Universiteit Antwerpen, Antwerpen, Belgium

    Sigrid Stroobants

  5. Vrije Universiteit Medisch Centrum, Amsterdam, The Netherlands

    Joost Kuijer

  6. Department of Medicine, Royal Marsden NHS Foundation Trust, London, UK

    Veronica Morgan

Authors
  1. Alex Weller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marianthi Vasiliki Papoutsaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John C. Waterton
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arturo Chiti
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sigrid Stroobants
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joost Kuijer
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Matthew Blackledge
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Veronica Morgan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nandita M deSouza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alex Weller.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Professor Nandita de-Souza

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Alderley Imaging Ltd (Waterton: Director; Stockholder); AstraZeneca (Waterton: Former employee; Stockholder).

Funding

The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking (www.imi.europa.eu) under grant agreement number 115151, the resources of which are composed of financial contributions from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The authors are members of the QuIC-ConCePT Consortium whose participants include: AstraZeneca, European Organisation for Research and Treatment of Cancer (EORTC), University of Cambridge, University of Manchester, Westfälische Wilhelms-Universität Münster, Radboud University Nijmegen Medical Center, Institut National de la Santé et de la Recherche Médical, Stichting Maastricht Radiation Oncology “Maastro Clinic”, VUmc Amsterdam, King’s College London, Universitair Ziekenhuis Antwerpen, Institute of Cancer Research – Royal Cancer Hospital, Erasmus Universitair Medisch Centrum Rotterdam, Imperial College of Science Technology and Medicine, Keosys S.A.S., Eidgenössische Technische Hochschule Zürich, Amgen NV, Eli Lilly and Company Ltd, GlaxoSmithKline Research & Development Limited, Merck KGa, Pfizer Limited, F.Hoffmann - La Roche Ltd, Sanofi-Aventis Research and Development.

Statistics and biometry

Dr Matthew Orton (The Institute of Cancer Research, London, UK) kindly provided statistical advice for this manuscript.

Ethical approval

Institutional Review Board approval was obtained (UK REC reference 15/LO/0882).

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Methodology

• prospective

• cross-sectional study

• multicentre study

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Weller, A., Papoutsaki, M.V., Waterton, J.C. et al. Diffusion-weighted (DW) MRI in lung cancers: ADC test-retest repeatability. Eur Radiol 27, 4552–4562 (2017). https://doi.org/10.1007/s00330-017-4828-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-017-4828-6

Keywords

Search

Navigation