Skip to main content
Log in

Advanced imaging in evaluation of bone disease of multiple myeloma

  • Review
  • Published:
Chinese Journal of Academic Radiology Aims and scope Submit manuscript

Abstract

Multiple myeloma is defined as excessive proliferation and infiltration of malignant plasma cell in the bone marrow. Imaging plays an important role in the evaluation of bone lesions of MM. The current paper mainly discusses the diagnostic criteria, distribution of bone lesions in MM using different imaging modalities, the risk factors and future direction of MM.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

MM:

Multiple myeloma

MRD:

Minimal residual disease

CRAB:

Hypercalcemia, renal impairment, anemia and bone disease

IMWG:

The International Myeloma Working Group

OS:

Overall survival

DFS:

Disease-free survival

SMM:

Smoldering MM

WBLDCT:

Whole-body low dose CT

WB-DWI:

Whole-body diffuse weighted imaging

BMPC:

Bone marrow plasma cell

ADC:

Apparent diffusion coefficient

MGUS:

Monoclonal gammopathy of undetermined significance

ASCT:

Autologous stem cell transplantation

PD:

Progressive disease

CR:

Complete response

PR:

Partial response

R-ISS:

Revised-international staging system

References

  1. Ziogas DC, Dimopoulos MA, Kastritis E. Prognostic factors for multiple myeloma in the era of novel therapies. Expert Rev Hematol. 2018;11(11):863–79.

    CAS  PubMed  Google Scholar 

  2. Nakaya A, Fujita S, Satake A, et al. Impact of CRAB symptoms in survival of patients with symptomatic myeloma in novel agent era. Hematol Rep. 2017;9(1):6887.

    PubMed  PubMed Central  Google Scholar 

  3. Caers J, Garderet L, Kortüm KM, et al. European Myeloma Network recommendations on tools for the diagnosis and monitoring of multiple myeloma: what to use and when. Haematologica. 2018;103(11):1772–844.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Plummer C, Driessen C, Szabo Z, Mateos MV. Management of cardiovascular risk in patients with multiple myeloma. Blood Cancer J. 2019;9(3):26.

    PubMed  PubMed Central  Google Scholar 

  5. Hansford BG, Silbermann R. Advanced imaging of multiple myeloma bone disease. Front Endocrinol (Lausanne). 2018;9:436.

    Google Scholar 

  6. Zamagni E, Cavo M, Fakhri B, Vij R, Roodman D. Bones in multiple myeloma: imaging and therapy. Am Soc Clin Oncol Educ Book. 2018;38:638–46.

    PubMed  Google Scholar 

  7. Kastritis E, Terpos E, Roussou M, et al. Evaluation of the Revised International Staging System in an independent cohort of unselected patients with multiple myeloma. Haematologica. 2017;102(3):593–9.

    PubMed  PubMed Central  Google Scholar 

  8. Sonneveld P, Avet-Loiseau H, Lonial S, et al. Treatment of multiple myeloma with high-risk cytogenetics: a consensus of the International Myeloma Working Group. Blood. 2016;127(24):2955–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Joseph NS, Gentili S, Kaufman JL, Lonial S, Nooka AK. High-risk multiple myeloma: definition and management. Clin Lymphoma Myeloma Leuk. 2017;17S:S80–S8787.

    PubMed  Google Scholar 

  10. Nooka AK, Lonial S. Is maintenance therapy for everyone. Clin Lymphoma Myeloma Leuk. 2016;16(Suppl):S139–S144144.

    PubMed  Google Scholar 

  11. Augustson BM, Begum G, Dunn JA, et al. Early mortality after diagnosis of multiple myeloma: analysis of patients entered onto the United Kingdom Medical Research Council trials between 1980 and 2002–Medical Research Council Adult Leukaemia Working Party. J Clin Oncol. 2005;23(36):9219–26.

    PubMed  Google Scholar 

  12. Palumbo A, Bringhen S, Mateos MV, et al. Geriatric assessment predicts survival and toxicities in elderly myeloma patients: an International Myeloma Working Group report. Blood. 2015;125(13):2068–74.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Hsu P, Lin TW, Gau JP, et al. Risk of early mortality in patients with newly diagnosed multiple myeloma. Medicine (Baltimore). 2015;94(50):e2305.

    Google Scholar 

  14. Koutoulidis V, Fontara S, Terpos E, 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(2):484–93.

    PubMed  Google Scholar 

  15. Moreau P, Facon T, Leleu X, et al. Recurrent 14q32 translocations determine the prognosis of multiple myeloma, especially in patients receiving intensive chemotherapy. Blood. 2002;100(5):1579–83.

    CAS  PubMed  Google Scholar 

  16. Pandey S, Kyle RA. Unusual myelomas: a review of IgD and IgE variants. Oncology (Williston Park). 2013;27(8):798–803.

    Google Scholar 

  17. Avivi I, Cohen YC, Joffe E, et al. Serum free immunoglobulin light chain fingerprint identifies a subset of newly diagnosed multiple myeloma patients with worse outcome. Hematol Oncol. 2017;35(4):734–40.

    CAS  PubMed  Google Scholar 

  18. Usmani SZ, Heuck C, Mitchell A, et al. Extramedullary disease portends poor prognosis in multiple myeloma and is over-represented in high-risk disease even in the era of novel agents. Haematologica. 2012;97(11):1761–7.

    PubMed  PubMed Central  Google Scholar 

  19. Palumbo A, Avet-Loiseau H, Oliva S, et al. Revised international staging system for multiple Myeloma: a report from International Myeloma Working Group. J Clin Oncol. 2015;33(26):2863–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Terpos E, Christoulas D, Kastritis E, et al. High levels of periostin correlate with increased fracture rate, diffuse MRI pattern, abnormal bone remodeling and advanced disease stage in patients with newly diagnosed symptomatic multiple myeloma. Blood Cancer J. 2016;6(10):e482.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Koppula B, Kaptuch J, Hanrahan CJ. Imaging of multiple myeloma: usefulness of MRI and PET/CT. Semin Ultrasound CT MR. 2013;34(6):566–77.

    PubMed  Google Scholar 

  22. Messiou C, Kaiser M. Whole body diffusion weighted MRI–a new view of myeloma. Br J Haematol. 2015;171(1):29–37.

    PubMed  PubMed Central  Google Scholar 

  23. Zamagni E, Tacchetti P, Cavo M. Imaging in multiple myeloma: How? When Blood. 2019;133(7):644–51.

    CAS  PubMed  Google Scholar 

  24. Cretti F, Perugini G. Patient dose evaluation for the whole-body low-dose multidetector CT (WBLDMDCT) skeleton study in multiple myeloma (MM). Radiol Med. 2016;121(2):93–105.

    PubMed  Google Scholar 

  25. Dimopoulos MA, Hillengass J, Usmani S, et al. Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement. J Clin Oncol. 2015;33(6):657–64.

    PubMed  Google Scholar 

  26. Caldarella C, Treglia G, Isgrò MA, Treglia I, Giordano A. The role of fluorine-18-fluorodeoxyglucose positron emission tomography in evaluating the response to treatment in patients with multiple myeloma. Int J Mol Imaging. 2012;2012:175803.

    PubMed  PubMed Central  Google Scholar 

  27. Pratt G, Morris TC. Review of the NICE guidelines for multiple myeloma. Int J Lab Hematol. 2017;39(1):3–13.

    CAS  PubMed  Google Scholar 

  28. Rajkumar SV, Dimopoulos MA, Palumbo A, et al. International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol. 2014;15(12):e538–e548548.

    PubMed  Google Scholar 

  29. Lacognata C, Crimì F, Guolo A, et al. Diffusion-weighted whole-body MRI for evaluation of early response in multiple myeloma. Clin Radiol. 2017;72(10):850–7.

    CAS  PubMed  Google Scholar 

  30. Mouhieddine TH, Weeks LD, Ghobrial IM. Monoclonal gammopathy of undetermined significance (MGUS). Blood. 2019;133(23):2484–94.

    CAS  PubMed  Google Scholar 

  31. Cocito F, Mangiacavalli S, Ferretti VV, et al. Smoldering multiple myeloma: the role of different scoring systems in identifying high-risk patients in real-life practice. Leuk Lymphoma. 2019;60(12):2968–74.

    PubMed  Google Scholar 

  32. Hillengass J, Ayyaz S, Kilk K, et al. Changes in magnetic resonance imaging before and after autologous stem cell transplantation correlate with response and survival in multiple myeloma. Haematologica. 2012;97(11):1757–60.

    PubMed  PubMed Central  Google Scholar 

  33. Dutoit JC, Verstraete KL. MRI in multiple myeloma: a pictorial review of diagnostic and post-treatment findings. Insights Imaging. 2016;7(4):553–69.

    PubMed  PubMed Central  Google Scholar 

  34. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11(2):102–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Moulopoulos LA, Dimopoulos MA, Christoulas D, et al. Diffuse MRI marrow pattern correlates with increased angiogenesis, advanced disease features and poor prognosis in newly diagnosed myeloma treated with novel agents. Leukemia. 2010;24(6):1206–12.

    CAS  PubMed  Google Scholar 

  36. Latifoltojar A, Hall-Craggs M, Rabin N, et al. Whole body magnetic resonance imaging in newly diagnosed multiple myeloma: early changes in lesional signal fat fraction predict disease response. Br J Haematol. 2017;176(2):222–33.

    PubMed  Google Scholar 

  37. 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. 2016;85(2):373–82.

    PubMed  Google Scholar 

  38. 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(4):e142–e144144.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Pawlyn C, Fowkes L, Otero S, et al. Whole-body diffusion-weighted MRI: a new gold standard for assessing disease burden in patients with multiple myeloma. Leukemia. 2016;30(6):1446–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Cascini GL, Falcone C, Console D, et al. Whole-body MRI and PET/CT in multiple myeloma patients during staging and after treatment: personal experience in a longitudinal study. Radiol Med. 2013;118(6):930–48.

    PubMed  Google Scholar 

  41. Kumar S, Paiva B, Anderson KC, et al. International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol. 2016;17(8):e328–e346346.

    PubMed  Google Scholar 

  42. Rawstron AC, Child JA, de Tute RM, et al. Minimal residual disease assessed by multiparameter flow cytometry in multiple myeloma: impact on outcome in the Medical Research Council Myeloma IX Study. J Clin Oncol. 2013;31(20):2540–7.

    PubMed  Google Scholar 

  43. Rasche L, Alapat D, Kumar M, et al. Combination of flow cytometry and functional imaging for monitoring of residual disease in myeloma. Leukemia. 2019;33(7):1713–22.

    CAS  PubMed  Google Scholar 

  44. Lecouvet FE, Larbi A, Pasoglou V, et al. MRI for response assessment in metastatic bone disease. Eur Radiol. 2013;23(7):1986–97.

    CAS  PubMed  Google Scholar 

  45. Hillengass J, Usmani S, Rajkumar SV, et al. International myeloma working group consensus recommendations on imaging in monoclonal plasma cell disorders. Lancet Oncol. 2019;20(6):e302–e312312.

    PubMed  Google Scholar 

  46. Bonaffini PA, Ippolito D, Casiraghi A, Besostri V, Franzesi CT, Sironi S. Apparent diffusion coefficient maps integrated in whole-body MRI examination for the evaluation of tumor response to chemotherapy in patients with multiple myeloma. Acad Radiol. 2015;22(9):1163–71.

    PubMed  Google Scholar 

  47. Dimopoulos MA, Terpos E, Niesvizky R, Palumbo A. Clinical characteristics of patients with relapsed multiple myeloma. Cancer Treat Rev. 2015;41(10):827–35.

    PubMed  Google Scholar 

  48. Larbi A, Omoumi P, Pasoglou V, et al. Comparison of bone lesion distribution between prostate cancer and multiple myeloma with whole-body MRI. Diagn Interv Imaging. 2019;100(5):295–302.

    CAS  PubMed  Google Scholar 

  49. Piraud M, Wennmann M, Kintzelé L, et al. Towards quantitative imaging biomarkers of tumor dissemination: a multi-scale parametric modeling of multiple myeloma. Med Image Anal. 2019;57:214–25.

    PubMed  Google Scholar 

  50. Pérez-Persona E, Mateo G, García-Sanz R, et al. Risk of progression in smouldering myeloma and monoclonal gammopathies of unknown significance: comparative analysis of the evolution of monoclonal component and multiparameter flow cytometry of bone marrow plasma cells. Br J Haematol. 2010;148(1):110–4.

    PubMed  Google Scholar 

  51. Ng AC, Khosla S, Charatcharoenwitthaya N, et al. Bone microstructural changes revealed by high-resolution peripheral quantitative computed tomography imaging and elevated DKK1 and MIP-1α levels in patients with MGUS. Blood. 2011;118(25):6529–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Kastritis E, Moulopoulos LA, Terpos E, Koutoulidis V, Dimopoulos MA. The prognostic importance of the presence of more than one focal lesion in spine MRI of patients with asymptomatic (smoldering) multiple myeloma. Leukemia. 2014;28(12):2402–3.

    CAS  PubMed  Google Scholar 

  53. Merz M, Hielscher T, Wagner B, et al. Predictive value of longitudinal whole-body magnetic resonance imaging in patients with smoldering multiple myeloma. Leukemia. 2014;28(9):1902–8.

    CAS  PubMed  Google Scholar 

  54. Kyle RA, Larson DR, Therneau TM, et al. Long-term follow-up of monoclonal gammopathy of undetermined significance. N Engl J Med. 2018;378(3):241–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Zamagni E, Nanni C, Gay F, et al. 18F-FDG PET/CT focal, but not osteolytic, lesions predict the progression of smoldering myeloma to active disease. Leukemia. 2016;30(2):417–22.

    CAS  PubMed  Google Scholar 

  56. 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(8):1713–8.

    CAS  PubMed  Google Scholar 

  57. Melton LJ 3rd, Kyle RA, Achenbach SJ, Oberg AL, Rajkumar SV. Fracture risk with multiple myeloma: a population-based study. J Bone Miner Res. 2005;20(3):487–93.

    PubMed  Google Scholar 

  58. Ormond Filho AG, Carneiro BC, Pastore D, et al. Whole-body imaging of multiple myeloma: diagnostic criteria. Radiographics. 2019;39(4):1077–97.

    PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shuang Xia.

Ethics declarations

Conflict of interest

All authors declare no personal or professional conflicts of interest, and no financial support from the companies that produce and/or distribute the drugs, devices, or materials described in this report.

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

Huang, W., Dong, H., Ji, X. et al. Advanced imaging in evaluation of bone disease of multiple myeloma. Chin J Acad Radiol 3, 76–83 (2020). https://doi.org/10.1007/s42058-020-00038-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42058-020-00038-y

Keywords

Navigation