Skip to main content
SpringerLink
Log in

Review of diffusion-weighted imaging and dynamic contrast–enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma)

  • Review Article
  • Published:
Skeletal Radiology Aims and scope Submit manuscript

Abstract

The last decades, increasing research has been conducted on dynamic contrast–enhanced and diffusion-weighted MRI techniques in multiple myeloma and its precursors. Apart from anatomical sequences which are prone to interpretation errors due to anatomical variants, other pathologies and subjective evaluation of signal intensities, dynamic contrast–enhanced and diffusion-weighted MRI provide additional information on microenvironmental changes in bone marrow and are helpful in the diagnosis, staging and follow-up of plasma cell dyscrasias. Diffusion-weighted imaging provides information on diffusion (restriction) of water molecules in bone marrow and in malignant infiltration. Qualitative evaluation by visually assessing images with different diffusion sensitising gradients and quantitative evaluation of the apparent diffusion coefficient are studied extensively. Dynamic contrast–enhanced imaging provides information on bone marrow vascularisation, perfusion, capillary resistance, vascular permeability and interstitial space, which are systematically altered in different disease stages and can be evaluated in a qualitative and a (semi-)quantitative manner. Both diffusion restriction and abnormal dynamic contrast–enhanced MRI parameters are early biomarkers of malignancy or disease progression in focal lesions or in regions with diffuse abnormal signal intensities. The added value for both techniques lies in better detection and/or characterisation of abnormal bone marrow otherwise missed or misdiagnosed on anatomical MRI sequences. Increased detection rates of focal lesions or diffuse bone marrow infiltration upstage patients to higher disease stages, provide earlier access to therapy and slower disease progression and allow closer monitoring of high-risk patients. Despite promising results, variations in imaging protocols, scanner types and post-processing methods are large, thus hampering universal applicability and reproducibility of quantitative imaging parameters. The myeloma response assessment and diagnosis system and the international myeloma working group provide a systematic multicentre approach on imaging and propose which parameters to use in multiple myeloma and its precursors in an attempt to overcome the pitfalls of dynamic contrast–enhanced and diffusion-weighted imaging.

Single sentence summary statement

Diffusion-weighted imaging and dynamic contrast–enhanced MRI provide important additional information to standard anatomical MRI techniques for diagnosis, staging and follow-up of patients with plasma cell dyscrasias, although some precautions should be taken on standardisation of imaging protocols to improve reproducibility and application in multiple centres.

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
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Abbreviations

3D:

Three-dimensional

ADC:

Apparent diffusion coefficient

AIF:

Arterial input function

AT:

Arrival time

A.U.:

Arbitrary units

AUC:

Area under curve

BM:

Bone marrow

BMI:

Body mass index

b-value:

Diffusion sensitising gradient

CRAB:

Calcaemia, renal failure, anaemia, bone lesions

CT:

Computed tomography

DCE:

Dynamic contrast–enhanced

DWI:

Diffusion-weighted imaging

DWIBS:

Diffusion-weighted imaging with background body signal suppression

EES:

Extravascular extracellular space

EPI:

Echo planar imaging

EPO:

Erythropoietin

FDG:

Fluoro-deoxy-glucose

FS:

Fat-suppressed/saturated

GCSF:

Granulocyte colony–stimulating factor

Gd:

Gadolinium

iAUC:

Initial area under curve

IMWG:

International Myeloma Working Group

iShim:

Integrated slice-by-slice shimming

IVIM:

Intravoxel incoherent motion

Kel:

Elimination rate constant

Kep:

Rate constant from the extravascular extracellular space to the plasma

Kin:

Input rate constant

Kpe:

Rate constant from the plasma to the extravascular extracellular space

Ktrans:

Volume transfer constant from the plasma to the extravascular extracellular space

MGUS:

Monoclonal gammopathy of undetermined significance

MIP:

Maximum intensity projection

MM:

Multiple myeloma

mm2 :

Square millimetres

M-protein:

Monoclonal protein

MRI:

Magnetic resonance imaging

MYRADS:

Myeloma Response Assessment and Diagnosis System

N/A:

Not available

PET:

Positron emission tomography

(R-)ISS:

(Revised-) International Staging System

ROI:

Region of interest

s:

Seconds

SE:

Spin echo

SI:

Signal intensity

SMM:

Smouldering myeloma

SNR:

Signal-to-noise ratio

SPAIR FS:

Spectral adiabatic inversion recovery fat saturation

STIR:

Short tau inversion recovery

T:

Tesla

TCC:

Time-concentration curve

TE:

Echo time

TIC:

Time-intensity curve

TR:

Repetition time

TTP:

Time to peak

Ve:

Extravascular extracellular space volume per unit of tissue volume

Vp:

Blood plasma volume per unit of tissue volume

WB:

Whole body

WBCT:

Whole-body computed tomography

References

  1. Biffar A, Baur-Melnyk A, Schmidt GP, Reiser MF, Dietrich O. Multiparameter MRI assessment of normal-appearing and diseased vertebral bone marrow. Eur Radiol. 2010;20:2679–89.

    PubMed  Google Scholar 

  2. Hanrahan CJ, Shah LM. MRI of spinal bone marrow: part 2, T1-weighted imaging-based differential diagnosis. Am J Roentgenol. 2011;197:1309–21.

    Google Scholar 

  3. Murphy DT, Moynagh MR, Eustace SJ, Kavanagh EC. Bone marrow. Magn Reson Imaging Clin N Am [Internet]. Elsevier Ltd; 2010;18:727–35. Available from: https://doi.org/10.1016/j.mric.2010.07.003

  4. Rana C, Sharma S, Agrawal V, Singh U. Bone marrow angiogenesis in multiple myeloma and its correlation with clinicopathological factors. Ann Hematol. 2010;89:789–94.

    PubMed  Google Scholar 

  5. Brull L. Multiple myeloma. Rev Med Liege. 1951;6:58–60.

    CAS  PubMed  Google Scholar 

  6. Rajkumar SV, Dimopoulos MA, Palumbo A, Blade J, Merlini G, Mateos MV, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol [Internet]. Elsevier Ltd; 2014;15:e538–48. Available from: https://doi.org/10.1016/S1470-2045(14)70442-5

  7. Andrulis M, Bäuerle T, Goldschmidt H, Delorme S, Landgren O, Schirmacher P, et al. Infiltration patterns in monoclonal plasma cell disorders: correlation of magnetic resonance imaging with matched bone marrow histology. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2014;83:970–4. Available from: https://doi.org/10.1016/j.ejrad.2014.03.005

  8. Mena E, Choyke P, Tan E, Landgren O, Kurdziel K. Molecular imaging in myeloma precursor disease. NIH Public Access. 2011;23:1–7.

    Google Scholar 

  9. Messiou C, Kaiser M. Whole-body imaging in multiple myeloma. Magn Reson Imaging Clin N Am [Internet]. Elsevier Inc; 2018;26:509–25. Available from: https://doi.org/10.1016/j.mric.2018.06.006

  10. Röllig C, Knop S, Bornhäuser M. Multiple myeloma. Lancet. 2015;385:2197–208.

    PubMed  Google Scholar 

  11. Mateos MV, Kumar S, Dimopoulos MA, González-Calle V, Kastritis E, Hajek R, et al. International Myeloma Working Group risk stratification model for smoldering multiple myeloma (SMM). Blood Cancer J [Internet]. Springer US; 2020;10. Available from: https://doi.org/10.1038/s41408-020-00366-3

  12. Fechtner K, Hillengass J, Delorme S, Heiss C, Neben K, Goldschmidt H, et al. Staging monoclonal plasma cell disease: comparison of the Durie-Salmon and the Durie-Salmon PLUS staging systems. Radiology. 2010;257:195–204.

    PubMed  Google Scholar 

  13. Wolf MB, Murray F, Kilk K, Hillengass J, Delorme S, Heiss C, et al. Sensitivity of whole-body CT and MRI versus projection radiography in the detection of osteolyses in patients with monoclonal plasma cell disease. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2014;83:1222–30. Available from: https://doi.org/10.1016/j.ejrad.2014.02.008

  14. Palumbo A, Avet-Loiseau H, Oliva S, Lokhorst HM, Goldschmidt H, Rosinol L, et al. Revised international staging system for multiple myeloma: a report from international myeloma working group. J Clin Oncol. 2015;33:2863–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Barwick T, Bretsztajn L, Wallitt K, Amiras D, Rockall A, Messiou C. Imaging in myeloma with focus on advanced imaging techniques. Br J Radiol. 2019;92:20180768.

  16. Sommer G, Klarhöfer M, Lenz C, Scheffler K, Bongartz G, Winter L. Signal characteristics of focal bone marrow lesions in patients with multiple myeloma using whole body T1w-TSE, T2w-STIR and diffusion-weighted imaging with background suppression. Eur Radiol. 2011;21:857–62.

    PubMed  Google Scholar 

  17. Messiou C, Kaiser M. Whole body diffusion weighted MRI - a new view of myeloma. Br J Haematol. 2015;171:29–37.

    PubMed  PubMed Central  Google Scholar 

  18. Dimopoulos MA, Hillengass J, Usmani S, Zamagni E, Lentzsch S, Davies FE, et al. Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement. J Clin Oncol. 2015;33:657–64.

    PubMed  Google Scholar 

  19. Martí-Bonmatí L, Ramirez-Fuentes C, Alberich-Bayarri Á, Ruiz-Llorca C. State-of-the-art of bone marrow imaging in multiple myeloma. Curr Opin Oncol. 2015;27:540–50.

    PubMed  Google Scholar 

  20. Lee K, Park HY, Kim KW, Lee AJ, Yoon MA, Chae EJ, et al. Advances in whole body MRI for musculoskeletal imaging: diffusion-weighted imaging. J Clin Orthop Trauma Elsevier BV 2019;10:680–6.

  21. Biffar A, Dietrich O, Sourbron S, Duerr HR, Reiser MF, Baur-Melnyk A. Diffusion and perfusion imaging of bone marrow. Eur J Radiol Elsevier Ireland Ltd. 2010;76:323–8.

    Google Scholar 

  22. Lee SY, Kim HJ, Shin YR, Park HJ, Lee YG, Oh SJ. Prognostic significance of focal lesions and diffuse infiltration on MRI for multiple myeloma: a meta-analysis. Eur Radiol European Radiology. 2017;27:2333–47.

    PubMed  Google Scholar 

  23. Moulopoulos LA, Dimopoulos MA, Kastritis E, Christoulas D, Gkotzamanidou M, Roussou M, et al. Diffuse pattern of bone marrow involvement on magnetic resonance imaging is associated with high risk cytogenetics and poor outcome in newly diagnosed, symptomatic patients with multiple myeloma: a single center experience on 228 patients. Am J Hematol. 2012;87:861–4.

    PubMed  Google Scholar 

  24. Caers J, Withofs N, Hillengass J, Simoni P, Zamagni E, Hustinx R, et al. The role of positron emission tomography-computed tomography and magnetic resonance imaging in diagnosis and follow up of multiple myeloma. Haematologica. 2014;99:629–37.

    PubMed  PubMed Central  Google Scholar 

  25. Chantry A, Kazmi M, Barrington S, Goh V, Mulholland N, Streetly M, et al. Guidelines for the use of imaging in the management of patients with myeloma. Br J Haematol Blackwell Publishing Ltd. 2017;178:380–93.

    Google Scholar 

  26. Lai AYT, Riddell A, Barwick T, Boyd K, Rockall A, Kaiser M, et al. Interobserver agreement of whole-body magnetic resonance imaging is superior to whole-body computed tomography for assessing disease burden in patients with multiple myeloma. Eur Radiol Springer. 2020;30:320–7.

    Google Scholar 

  27. Messiou C, Hillengass J, Delorme S, Lecouvet FE, Moulopoulos LA, Collins DJ, et al. Guidelines for acquisition, interpretation, and reporting of whole-body MRI in myeloma: myeloma response assessment and diagnosis system (MY-RADS). Radiology. 2019;291:5–13.

    PubMed  Google Scholar 

  28. Cuenod CA, Balvay D. Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging [Internet]. Elsevier Masson SAS; 2013;94:1187–204. Available from: https://doi.org/10.1016/j.diii.2013.10.010

  29. García-Figueiras R, Padhani AR, Beer AJ, Baleato-González S, Vilanova JC, Luna A, et al. Imaging of Tumor angiogenesis for radiologists-part 1: biological and technical basis. Curr Probl Diagn Radiol [Internet]. Elsevier; 2015;44:407–24. Available from: https://doi.org/10.1067/j.cpradiol.2015.02.010

  30. Koutoulidis V, Papanikolaou N, Moulopoulos LA. Functional and molecular MRI of the bone marrow in multiple myeloma. Br J Radiol 2018;91:20170389.

  31. Verstraele KL, Van Der Woude HJ, Hogendoorn PCW, De Deene Y, Kunnen M, Bloem JL. Dynamic contrast-enhanced MR imaging of musculoskeletal tumors: basic principles and clinical applications. J Magn Reson Imaging. 1996;6:311–21.

    Google Scholar 

  32. Dutoit JC, Claus E, Offner F, Noens L, Delanghe J, Verstraete KL. Combined evaluation of conventional MRI, dynamic contrast-enhanced MRI and diffusion weighted imaging for response evaluation of patients with multiple myeloma. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2016;85:373–82. Available from: https://doi.org/10.1016/j.ejrad.2015.11.040

  33. Dutoit JC, Vanderkerken MA, Verstraete KL. Value of whole body MRI and dynamic contrast enhanced MRI in the diagnosis, follow-up and evaluation of disease activity and extent in multiple myeloma. Eur J Radiol. 2013;82:1444–52.

    PubMed  Google Scholar 

  34. Lavini C, de Jonge MC, van de Sande MGH, Tak PP, Nederveen AJ, Maas M. Pixel-by-pixel analysis of DCE MRI curve patterns and an illustration of its application to the imaging of the musculoskeletal system. Magn Reson Imaging. 2007;25:604–12.

    PubMed  Google Scholar 

  35. Dutoit JC, Verstraete KL. MRI in multiple myeloma: a pictorial review of diagnostic and post-treatment findings. Insights Imaging [Internet]. Insights Imaging. 2016;7:553–69. https://doi.org/10.1007/s13244-016-0492-7.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Sourbron SP, Buckley DL. On the scope and interpretation of the Tofts models for DCE-MRI. Magn Reson Med. 2011;66:735–45.

    PubMed  Google Scholar 

  37. Koh TS, Bisdas S, Koh DM, Thng CH. Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2011;34:1262–76.

    PubMed  Google Scholar 

  38. Tofts PS. T1-weighted DCE imaging concepts: modelling, acquisition and analysis. MAGNETOM Flash [Internet]. 2010;3:30–9. Available from: http://www.paul-tofts-phd.org.uk/DCE-MRI_siemens.pdf. Accessed 1 Aug 2021

  39. Zwick S, Brix G, Tofts PS, Strecker R, Kopp-Schneider A, Laue H, et al. Simulation-based comparison of two approaches frequently used for dynamic contrast-enhanced MRI. Eur Radiol. 2010;20:432–42.

    PubMed  Google Scholar 

  40. Merz M, Ritsch J, Kunz C, Wagner B, Sauer S, Hose D, et al. Dynamic contrast-enhanced magnetic resonance imaging for assessment of antiangiogenic treatment effects in multiple myeloma. Clin Cancer Res. 2015;21:106–12.

    CAS  PubMed  Google Scholar 

  41. Gordon Y, Partovi S, Müller-Eschner M, Amarteifio E, Bäuerle T, Weber M-A, et al. Dynamic contrast-enhanced magnetic resonance imaging: fundamentals and application to the evaluation of the peripheral perfusion. Cardiovasc Diagn Ther. 2014;4:147–14764.

    PubMed  PubMed Central  Google Scholar 

  42. Brix G, Kiessling F, Lucht R, Darai S, Wasser K, Delorme S, et al. Microcirculation and microvasculature in breast tumors: pharmacokinetic analysis of dynamic MR image series. Magn Reson Med. 2004;52:420–9.

    PubMed  Google Scholar 

  43. Zechmann CM, Traine L, Meißner T, Wagner-Gund B, Giesel FL, Goldschmidt H, et al. Parametric Histogram analysis of dynamic contrast-enhanced MRI in multiple myeloma. A Technique to evaluate angiogenic response to therapy? Acad Radiol [Internet]. Elsevier Ltd; 2012;19:100–8. Available from: https://doi.org/10.1016/j.acra.2011.09.007

  44. Dutoit JC, Vanderkerken MA, Anthonissen J, Dochy F, Verstraete KL. The diagnostic value of SE MRI and DWI of the spine in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma. Eur Radiol. 2014;24:2754–65.

    PubMed  Google Scholar 

  45. Gariani J, Westerland O, Natas S, Verma H, Cook G, Goh V. Comparison of whole body magnetic resonance imaging (WBMRI) to whole body computed tomography (WBCT) or 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) in patients with myeloma: systematic review of diagnostic performance. Crit Rev Oncol Hematol [Internet]. Elsevier; 2018;124:66–72. Available from: https://doi.org/10.1016/j.critrevonc.2018.02.012

  46. Lang N, Sub M-Y, Yub HJ, Linb M, Hamamurab MJ, Huishu Y, et al. Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast enhanced MRI. Bone. 2014;23:1–7.

    Google Scholar 

  47. Di Giuliano F, Picchi E, Muto M, Calcagni A, Ferrazzoli V, Da Ros V, et al. Radiological imaging in multiple myeloma: review of the state-of-the-art. Neuroradiology. 2020;62:905–23.

    PubMed  Google Scholar 

  48. Terpos E, Matsaridis D, Koutoulidis V, Zagouri F, Christoulas D, Fontara S, et al. Dynamic contrast-enhanced magnetic resonance imaging parameters correlate with advanced revised-ISS and angiopoietin-1/angiopoietin-2 ratio in patients with multiple myeloma. Ann Hematol Annals of Hematology. 2017;96:1707–14.

    CAS  PubMed  Google Scholar 

  49. C. L, A. L, K. B, P. M, A. V, J.-F. D, et al. Patients with plasma cell disorders examined at whole-body dynamic contrast-enhanced MR imaging: initial experience. Radiology [Internet]. 2009;250:905–15. Available from: http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L354344061%0Ahttp://radiology.rsnajnls.org/cgi/reprint/250/3/905%0A10.1148/radiol.2503081017. Accessed 1 Aug 2021

  50. Zha Y, Li M, Yang J. Dynamic contrast enhanced magnetic resonance imaging of diffuse spinal bone marrow infiltration in patients with hematological malignancies. Korean J Radiol. 2010;11:187–94.

    PubMed  PubMed Central  Google Scholar 

  51. Nosàs-Garcia S, Moehler T, Wasser K, Kiessling F, Bartl R, Zuna I, et al. Dynamic contrast-enhanced MRI for assessing the disease activity of multiple myeloma: a comparative study with histology and clinical markers. J Magn Reson Imaging. 2005;22:154–62.

    PubMed  Google Scholar 

  52. Hillengass J, Ritsch J, Merz M, Wagner B, Kunz C, Hielscher T, et al. Increased microcirculation detected by dynamic contrast-enhanced magnetic resonance imaging is of prognostic significance in asymptomatic myeloma. Br J Haematol. 2016;174:127–35.

    PubMed  Google Scholar 

  53. Shah LM, Hanrahan CJ. MRI of spinal bone marrow: part 1, techniques and normal age-related appearances. Am J Roentgenol. 2011;197:1298–308.

    Google Scholar 

  54. Herrmann J, Krstin N, Schoennagel BP, Sornsakrin M, Derlin T, Busch JD, et al. Age-related distribution of vertebral bone-marrow diffusivity. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2012;81:4046–9. Available from: https://doi.org/10.1016/j.ejrad.2012.03.033

  55. Karampinos DC, Ruschke S, Dieckmeyer M, Diefenbach M, Franz D, Gersing AS, et al. Quantitative MRI and spectroscopy of bone marrow. J Magn Reson Imaging. 2018;47:332–53.

  56. Montazel J-L, Divine M, Lepage E, Kobeiter H, Breil S, Rahmouni A. Normal spinal bone marrow in adults: dynamic gadolinium-enhanced MR imaging. Radiology Radiological Society of North America. 2003;229:703–9.

    Google Scholar 

  57. Budzik JF, Lefebvre G, Behal H, Verclytte S, Hardouin P, Teixeira P, et al. Bone marrow perfusion measured with dynamic contrast enhanced magnetic resonance imaging is correlated to body mass index in adults. Bone [Internet]. Elsevier Inc.; 2017;99:47–52. Available from: https://doi.org/10.1016/j.bone.2017.03.048

  58. Breault SR, Heye T, Bashir MR, Dale BM, Merkle EM, Reiner CS, et al. Quantitative dynamic contrast-enhanced MRI of pelvic and lumbar bone marrow: effect of age and marrow fat content on pharmacokinetic parameter values. Am J Roentgenol. 2013;200:297–303.

    Google Scholar 

  59. Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2019;49:e101–21.

  60. Dietrich O, Geith T, Reiser MF, Baur-Melnyk A. Diffusion imaging of the vertebral bone marrow. NMR Biomed. 2017;30:e3333.

  61. Dietrich O, Biffar A, Baur-Melnyk A, Reiser MF. Technical aspects of MR diffusion imaging of the body. Eur J Radiol [Internet]. Elsevier Ireland Ltd; 2010;76:314–22. Available from: https://doi.org/10.1016/j.ejrad.2010.02.018

  62. Attariwala R, Picker W. Whole body MRI: Improved lesion detection and characterization with diffusion weighted techniques. J Magn Reson Imaging. 2013;38:253–68.

    PubMed  PubMed Central  Google Scholar 

  63. Horger M, Weisel K, Horger W, Mroue A, Fenchel M, Lichy M. Whole-body diffusion-weighted MRI with apparent diffusion coefficient mapping for early response monitoring in multiple myeloma: preliminary results. Am J Roentgenol. 2011;196:W790-95.

  64. Paternain A, García-Velloso MJ, Rosales JJ, Ezponda A, Soriano I, Elorz M, et al. The utility of ADC value in diffusion-weighted whole-body MRI in the follow-up of patients with multiple myeloma. Correlation study with 18F-FDG PET-CT. Eur J Radiol. 2020;133:109403.

  65. Le BD. Apparent diffusion coefficient and beyond : what diffusion mr imaging can tell us about tissue structure. Radiology. 2013;268:318–22.

    Google Scholar 

  66. Padhani AR, Van Ree K, Collins DJ, D’Sa S, Makris A. Assessing the relation between bone marrow signal intensity and apparent diffusion coefficient in diffusion-weighted MRI. Am J Roentgenol. 2013;200:163–70.

    Google Scholar 

  67. Lecouvet FE. Whole-body MR imaging: musculoskeletal applications. Radiology. Radiol Soc North America Inc. 2016;279:345–65.

    Google Scholar 

  68. Lecouvet FE, El Mouedden J, Collette L, Coche E, Danse E, Jamar F, et al. Can Whole-body magnetic resonance imaging with diffusion-weighted imaging replace Tc 99m bone scanning and computed tomography for single-step detection of metastases in patients with high-risk prostate cancer? Eur Urol. 2012;62:68–75.

    PubMed  Google Scholar 

  69. Suh CH, Yun SJ, Jin W, Lee SH, Park SY, Ryu CW. ADC as a useful diagnostic tool for differentiating benign and malignant vertebral bone marrow lesions and compression fractures: a systematic review and meta-analysis. Eur Radiol European Radiology. 2018;28:2890–902.

    PubMed  Google Scholar 

  70. Hillengass J, Bäuerle T, Bartl R, Andrulis M, Mcclanahan F, Laun FB, et al. Diffusion-weighted imaging for non-invasive and quantitative monitoring of bone marrow infiltration in patients with monoclonal plasma cell disease: a comparative study with histology. Br J Haematol. 2011;153:721–8.

    PubMed  Google Scholar 

  71. Berardo S, Sukhovei L, Andorno S, Carriero A, Stecco A. Quantitative bone marrow magnetic resonance imaging through apparent diffusion coefficient and fat fraction in multiple myeloma patients. Radiol Medica [Internet]. Springer Milan; 2021;126:445–52. Available from: https://doi.org/10.1007/s11547-020-01258-z

  72. Khoo MMY, Tyler PA, Saifuddin A, Padhani AR. Diffusion-weighted imaging (DWI) in musculoskeletal MRI: a critical review. Skeletal Radiol. 2011;40:665–81.

    PubMed  Google Scholar 

  73. Koutoulidis V, Fontara S, Terpos E, Zagouri F, Matsaridis D, Christoulas D, et al. Quantitative diffusion-weighted imaging of the bone marrow: an adjunct tool for the diagnosis of a diffuse MR imaging pattern in patients with multiple myeloma. Radiology. 2017;282:484–93.

    PubMed  Google Scholar 

  74. Messiou C, Collins DJ, Morgan VA, Desouza NM. Optimising diffusion weighted MRI for imaging metastatic and myeloma bone disease and assessing reproducibility. Eur Radiol. 2011;21:1713–8.

    CAS  PubMed  Google Scholar 

  75. Belhadj K, Beaussart P, Vignaud A, Zerbib P.Musculoskeletal imaging: intravoxel incoherent motion diffusion-weighted imaging of multiple myeloma lesions Bourillon, , et al. Radiol n Radiol. 2015;277:773–83.

    Google Scholar 

  76. Zhang L, Wang Q, Wu X, Zhao A, Feng J, Zhang H, et al. Baseline bone marrow ADC value of diffusion-weighted MRI: a potential independent predictor for progression and death in patients with newly diagnosed multiple myeloma. Eur Radiol European Radiology. 2021;31:1843–52.

    PubMed  Google Scholar 

  77. Chen J, Li C, Tian Y, Xiao Q, Deng M, Hu H, et al. Comparison of whole-body DWI and 18F-FDG PET/CT for detecting intramedullary and extramedullary lesions in multiple myeloma. Am J Roentgenol. 2019;213:514–23.

    Google Scholar 

  78. Mesguich C, Hulin C, Latrabe V, Lascaux A, Bordenave L, Hindié E, et al. Prospective comparison of 18-FDG PET/CT and whole-body diffusion-weighted MRI in the assessment of multiple myeloma. Ann Hematol Springer Sci Bus Med Deutschland GmbH. 2020;99:2869–80.

    Google Scholar 

  79. Squillaci E, Bolacchi F, Altobelli S, Franceschini L, Bergamini A, Cantonetti M, et al. Pre-treatment staging of multiple myeloma patients: comparison of whole-body diffusion weighted imaging with whole-body T1-weighted contrast-enhanced imaging. Acta radiol. 2015;56:733–8.

    PubMed  Google Scholar 

  80. Singh S, Pilavachi E, Dudek A, Bray TJP, Latifoltojar A, Rajesparan K, et al. Whole body MRI in multiple myeloma: optimising image acquisition and read times. PLoS One. Public Library of Science; 2020;15::e0228424.

  81. Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfalls in diffusion MRI. J Magn Reson Imaging. 2006;24:478–88.

    PubMed  Google Scholar 

  82. Wale A, Pawlyn C, Kaiser M, Messiou C. Frequency, distribution and clinical management of incidental findings and extramedullary plasmacytomas in whole body diffusion weighted magnetic resonance imaging in patients with multiple myeloma. Haematologica. 2016;101:e142–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Westerland O, Sivarasan N, Natas S, Verma H, McElroy S, Winfield JM, et al. Added value of contrast-enhanced T1-weighted and diffusion-weighted sequences for characterization of incidental findings on whole body magnetic resonance imaging in plasma cell disorders. Clin Lymphoma, Myeloma Leuk. 2018;18:822–8.

    Google Scholar 

  84. Winfield JM, Poillucci G, Blackledge MD, Collins DJ, Shah V, Tunariu N, et al. Apparent diffusion coefficient of vertebral haemangiomas allows differentiation from malignant focal deposits in whole-body diffusion-weighted MRI. Eur Radiol European Radiology. 2018;28:1687–91.

    PubMed  Google Scholar 

  85. Barnes A, Alonzi R, Blackledge M, Charles-Edwards G, Collins DJ, Cook G, et al. Guidelines & recommendations: UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer. Br J Radiol. 2018;91:1–12.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology and Medical Imaging, Ghent University Hospital and Ghent University, Ghent, Belgium

    Thomas Van Den Berghe, Koenraad L. Verstraete, Maryse Lejoly & Julie Dutoit

  2. Department of Radiology and Medical Imaging, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium

    Frédéric E. Lecouvet

  3. Department of Radiology and Medical Imaging, AZ Groeninge, Kortrijk, Belgium

    Julie Dutoit

Authors
  1. Thomas Van Den Berghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koenraad L. Verstraete
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frédéric E. Lecouvet
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maryse Lejoly
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Julie Dutoit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Van Den Berghe.

Ethics declarations

Conflicts of interest

The authors declare no competing interests.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Van Den Berghe, T., Verstraete, K.L., Lecouvet, F.E. et al. Review of diffusion-weighted imaging and dynamic contrast–enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma). Skeletal Radiol 51, 101–122 (2022). https://doi.org/10.1007/s00256-021-03903-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00256-021-03903-8

Keywords

Search

Navigation