Skip to main content

Advertisement

SpringerLink
Log in

Marrow uptake on FDG PET/CT is associated with progression from smoldering to symptomatic multiple myeloma

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

Abstract

Objective

To determine association of body composition measurements on CT and PET with progression of smoldering myeloma to multiple myeloma.

Methods

A retrospective cohort study in 65 patients with smoldering myeloma and PET/CT at diagnosis was performed at a tertiary cancer center. Subjects were between 38 and 87 years of age (mean 64) and included 37 males. Primary outcome was progression-free survival as a function of bone, fat, and muscle metrics on CT and PET (measured at the level of L4 pedicles) and clinical confounders. CT metrics included attenuation of L4 and retroperitoneal fat and various indices derived from the psoas muscle. PET measures included SUVmax and SUVmean of L4, retroperitoneal fat, and psoas. Cox proportional hazards modeling was performed with entry and retention criteria of p < 0.1 and p < 0.05, respectively.

Results

SUVmax and SUVmean were associated for each compartment (R2 = 0.78–0.84), and SUVmean (SUV) was used for subsequent analyses. SUV of the L4 vertebral body was associated with attenuation of the L4 vertebral body (p = 0.0032). There was no association between SUV and CT for muscle and fat compartments. In the subset of patients with bone marrow biopsy results (n = 43), there was no association between SUV of L4 and plasma cell concentration on core biopsy or flow cytometry (p = 0.089 and 0.072, respectively). The final Cox model showed association with albumin (HR 0.29, 95%CI 0.088–0.93, p = 0.038), M protein (HR 1.31, 95%CI 1.021–1.68, p = 0.034), and SUV of L4 (HR 1.99, 95%CI 1.037–3.82, p = 0.039).

Conclusion

SUV of L4 is a prognostic indicator in patients with smoldering myeloma.

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

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7–34.

    Article  Google Scholar 

  2. Landgren O, Kyle RA, Pfeiffer RM, Katzmann JA, Caporaso NE, Hayes RB, et al. Monoclonal gammopathy of undetermined significance (MGUS) consistently precedes multiple myeloma: a prospective study. Blood. 2009;113(22):5412–7.

    Article  CAS  Google Scholar 

  3. Rajkumar SV, Landgren O, Mateos MV. Smoldering multiple myeloma. Blood. 2015;125(20):3069–75.

    Article  CAS  Google Scholar 

  4. Wu V, Moshier E, Leng S, Barlogie B, Cho HJ, Jagannath S, et al. Risk stratification of smoldering multiple myeloma: predictive value of free light chains and group-based trajectory modeling. Blood Adv. 2018;2(12):1470–9.

    Article  CAS  Google Scholar 

  5. Kunacheewa C, Manasanch EE. High-risk smoldering myeloma versus early detection of m myeloma: current models, goals of therapy, and clinical implications. Best Pract Res Clin Haematol. 2020;33(1):101152.

    Article  Google Scholar 

  6. Cavo M, Terpos E, Nanni C, Moreau P, Lentzsch S, Zweegman S, et al. Role of (18)F-FDG PET/CT in the diagnosis and management of multiple myeloma and other plasma cell disorders: a consensus statement by the International Myeloma Working Group. Lancet Oncol. 2017;18(4):e206–17.

    Article  Google Scholar 

  7. Kapoor P, Rajkumar SV. Smoldering multiple myeloma: to treat or not to treat. Cancer J. 2019;25(1):65–71.

    Article  Google Scholar 

  8. Boutin RD, Bamrungchart S, Bateni CP, Beavers DP, Beavers KM, Meehan JP, et al. CT of patients with hip fracture: muscle size and attenuation help predict mortality. AJR Am J Roentgenol. 2017;208(6):W208–15.

    Article  Google Scholar 

  9. Joglekar S, Nau PN, Mezhir JJ. The impact of sarcopenia on survival and complications in surgical oncology: a review of the current literature. J Surg Oncol. 2015;112(5):503–9.

    Article  Google Scholar 

  10. Zamagni E, Nanni C, Gay F, Pezzi A, Patriarca F, Bello M, 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.

    Article  CAS  Google Scholar 

  11. Siontis B, Kumar S, Dispenzieri A, Drake MT, Lacy MQ, Buadi F, et al. Positron emission tomography-computed tomography in the diagnostic evaluation of smoldering multiple myeloma: identification of patients needing therapy. Blood Cancer J. 2015;5:e364.

    Article  CAS  Google Scholar 

  12. Anderson KC, Alsina M, Atanackovic D, Biermann JS, Chandler JC, Costello C, et al. NCCN guidelines insights: multiple myeloma, version 3.2016. J Natl Compr Cancer Netw. 2016;14(4):389–400.

    Article  CAS  Google Scholar 

  13. 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. 2014;15(12):e538–48.

    Article  Google Scholar 

  14. Kalichman L, Klindukhov A, Li L, Linov L. Indices of paraspinal muscles degeneration: reliability and association with facet joint osteoarthritis: feasibility study. Clin Spine Surg. 2016;29(9):465–70.

    Article  Google Scholar 

  15. Miller BS, Ignatoski KM, Daignault S, Lindland C, Gauger PG, Doherty GM, et al. A quantitative tool to assess degree of sarcopenia objectively in patients with hypercortisolism. Surgery. 2011;150(6):1178–85.

    Article  Google Scholar 

  16. Prado CM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L, et al. Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol. 2008;9(7):629–35.

    Article  Google Scholar 

  17. Shachar SS, Deal AM, Weinberg M, Nyrop KA, Williams GR, Nishijima TF, et al. Skeletal muscle measures as predictors of toxicity, hospitalization, and survival in patients with metastatic breast cancer receiving taxane-based chemotherapy. Clin Cancer Res. 2017;23(3):658–65.

    Article  CAS  Google Scholar 

  18. Shachar SS, Deal AM, Weinberg M, Williams GR, Nyrop KA, Popuri K, et al. Body composition as a predictor of toxicity in patients receiving anthracycline and taxane-based chemotherapy for early-stage breast cancer. Clin Cancer Res. 2017;23(14):3537–43.

    Article  CAS  Google Scholar 

  19. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing 2017.

  20. Therneau T. A package for survival analysis in S. 2.3.8 ed 2015.

  21. Cherry BM, Korde N, Kwok M, Manasanch EE, Bhutani M, Mulquin M, et al. Modeling progression risk for smoldering multiple myeloma: results from a prospective clinical study. Leuk Lymphoma. 2013;54(10):2215–8.

    Article  Google Scholar 

  22. Miguel JS, Mateos M-V, Gonzalez V, Dimopoulos MA, Kastritis E, Hajek R, et al. Updated risk stratification model for smoldering multiple myeloma (SMM) incorporating the revised IMWG diagnostic criteria. J Clin Oncol. 2019;37(15_suppl):8000.

    Article  Google Scholar 

  23. Mateos MV, Hernandez MT, Giraldo P, de la Rubia J, de Arriba F, Corral LL, et al. Lenalidomide plus dexamethasone versus observation in patients with high-risk smouldering multiple myeloma (QuiRedex): long-term follow-up of a randomised, controlled, phase 3 trial. Lancet Oncol. 2016;17(8):1127–36.

    Article  CAS  Google Scholar 

  24. Lonial S, Jacobus S, Fonseca R, Weiss M, Kumar S, Orlowski RZ, et al. Randomized trial of lenalidomide versus observation in smoldering multiple myeloma. J Clin Oncol. 2020;38(11):1126–37.

    Article  CAS  Google Scholar 

  25. Veld J, O'Donnell EK, Reagan MR, Yee AJ, Torriani M, Rosen CJ, et al. Abdominal adipose tissue in MGUS and multiple myeloma. Skelet Radiol. 2016;45(9):1277–83.

    Article  Google Scholar 

  26. Takahashi MES, Mosci C, Souza EM, Brunetto SQ, Etchebehere E, Santos AO, et al. Proposal for a quantitative (18)F-FDG PET/CT metabolic parameter to assess the intensity of bone involvement in multiple myeloma. Sci Rep. 2019;9(1):16429.

    Article  Google Scholar 

  27. Cengiz A, Arda HU, Doger F, Yavasoglu I, Yurekli Y, Bolaman AZ. Correlation between baseline 18F-FDG PET/CT findings and CD38- and CD138-expressing myeloma cells in bone marrow and clinical parameters in patients with multiple myeloma. Turk J Haematol. 2018;35(3):175–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Manasanch EE, Salem DA, Yuan CM, Tageja N, Bhutani M, Kwok M, et al. Flow cytometric sensitivity and characteristics of plasma cells in patients with multiple myeloma or its precursor disease: influence of biopsy site and anticoagulation method. Leuk Lymphoma. 2015;56(5):1416–24.

    Article  CAS  Google Scholar 

  29. Zhou C, Foster B, Hagge R, Foster C, Lenchik L, Chaudhari AJ, et al. Opportunistic body composition evaluation in patients with esophageal adenocarcinoma: association of survival with (18)F-FDG PET/CT muscle metrics. Ann Nucl Med. 2020;34(3):174–81.

    Article  CAS  Google Scholar 

  30. Kjonigsen LJ, Harneshaug M, Flotten AM, Karterud LK, Petterson K, Skjolde G, et al. Reproducibility of semiautomated body composition segmentation of abdominal computed tomography: a multiobserver study. Eur Radiol Exp. 2019;3(1):42.

    Article  Google Scholar 

  31. Ozola-Zalite I, Mark EB, Gudauskas T, Lyadov V, Olesen SS, Drewes AM, et al. Reliability and validity of the new VikingSlice software for computed tomography body composition analysis. Eur J Clin Nutr. 2019;73(1):54–61.

    Article  Google Scholar 

  32. van Vugt JL, Levolger S, Gharbharan A, Koek M, Niessen WJ, Burger JW, et al. A comparative study of software programmes for cross-sectional skeletal muscle and adipose tissue measurements on abdominal computed tomography scans of rectal cancer patients. J Cachexia Sarcopenia Muscle. 2017;8(2):285–97.

    Article  Google Scholar 

  33. Boutin RD, Katz JR, Chaudhari AJ, Yabes JG, Hirschbein JS, Nakache Y-P, et al. Association of adipose tissue and skeletal muscle metrics with overall survival and postoperative complications in soft tissue sarcoma patients: an opportunistic study using computed tomography. Quant Imaging Med Surg. 2020. http://qims.amegroups.com/article/view/38772

Download references

Author information

Authors and Affiliations

  1. Department of Musculoskeletal Imaging, The University of Texas, MD Anderson Cancer Center, 1515 Holcombe Blvd, Unit 1475, Houston, TX, 77030, USA

    Behrang Amini

  2. Department of Radiology, The University of California, Davis, Davis, CA, USA

    Yves-Paul N. Nakache & Lorenzo Nardo

  3. Department of Lymphoma/Myeloma, The University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA

    Elisabet E. Manasanch

  4. Department of Biostatistics, The University of Texas, M.D. Anderson Cancer Center, Houston, TX, USA

    Jia Sun

  5. Department of Diagnostic Radiology, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Leon Lenchik

  6. Department of Diagnostic Radiology, Stanford University, Stanford, CA, USA

    Robert D. Boutin

Authors
  1. Behrang Amini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yves-Paul N. Nakache
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lorenzo Nardo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elisabet E. Manasanch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jia Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Leon Lenchik
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert D. Boutin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Behrang Amini.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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

Amini, B., Nakache, YP.N., Nardo, L. et al. Marrow uptake on FDG PET/CT is associated with progression from smoldering to symptomatic multiple myeloma. Skeletal Radiol 50, 79–85 (2021). https://doi.org/10.1007/s00256-020-03529-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00256-020-03529-2

Keywords

Search

Navigation