Multiple Myeloma and Dual-Energy CT: Diagnostic Accuracy of Virtual Noncalcium Technique for Detection of Bone Marrow Infiltration of the Spine and Pelvis

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Dual-energy virtual noncalcium CT images provide excellent diagnostic performance in visual and region of interest–based evaluation of bone marrow infiltration in patients with multiple myeloma.


To determine the diagnostic performance of dual-energy computed tomography (CT) for detection of bone marrow (BM) infiltration in patients with multiple myeloma by using a virtual noncalcium (VNCa) technique.

Materials and Methods

In this prospective study, 34 consecutive patients with multiple myeloma or monoclonal gammopathy of unknown significance sequentially underwent dual-energy CT and magnetic resonance (MR) imaging of the axial skeleton. Two independent readers visually evaluated standard CT and color-coded VNCa images for the presence of BM involvement. MR imaging served as the reference standard. Analysis on the basis of the region of interest (ROI) of VNCa CT numbers of infiltrated (n = 75) and normal (n = 170) BM was performed and CT numbers were subjected to receiver operating characteristic analysis to calculate cutoff values.


In the visual analysis, VNCa images had an overall sensitivity of 91.3% (21 of 23), specificity of 90.9% (10 of 11), accuracy of 91.2% (31 of 34), positive predictive value of 95.5% (21 of 22), and a negative predictive value of 83.3% (10 of 12). ROI-based analysis of VNCa CT numbers showed a significant difference between infiltrated and normal BM (P < .001). Receiver operating characteristic analysis revealed an area under the curve of 0.978. A cutoff of −44.9 HU provided a sensitivity of 93.3% (70 of 75), specificity of 92.4% (157 of 170), accuracy of 92.7% (227 of 245), positive predictive value of 84.3% (70 of 83), and negative predictive value of 96.9% (157 of 162) for the detection of BM infiltration.


Visual and ROI-based analyses of dual-energy VNCa images had excellent diagnostic performance for assessing BM infiltration in patients with multiple myeloma with precision comparable to that of MR imaging.

© RSNA, 2017

Online supplemental material is available for this article.


  • 1. Raab MS, Podar K, Breitkreutz I, Richardson PG, Anderson KC. Multiple myeloma. Lancet 2009;374(9686):324–339. Crossref, MedlineGoogle Scholar
  • 2. Dimopoulos M, Terpos E, Comenzo RL, et al. International myeloma working group consensus statement and guidelines regarding the current role of imaging techniques in the diagnosis and monitoring of multiple Myeloma. Leukemia 2009;23(9):1545–1556. Crossref, MedlineGoogle Scholar
  • 3. Bataille R, Harousseau JL. Multiple myeloma. N Engl J Med 1997;336(23):1657–1664. Crossref, MedlineGoogle Scholar
  • 4. Kyle RA, Gertz MA, Witzig TE, et al. Review of 1027 patients with newly diagnosed multiple myeloma. Mayo Clin Proc 2003;78(1):21–33. Crossref, MedlineGoogle Scholar
  • 5. Terpos E, Dimopoulos MA. Myeloma bone disease: pathophysiology and management. Ann Oncol 2005;16(8):1223–1231. Crossref, MedlineGoogle Scholar
  • 6. Rubini G, Niccoli-Asabella A, Ferrari C, Racanelli V, Maggialetti N, Dammacco F. Myeloma bone and extra-medullary disease: Role of PET/CT and other whole-body imaging techniques. Crit Rev Oncol Hematol 2016;101:169–183. Crossref, MedlineGoogle Scholar
  • 7. 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–e548. Crossref, MedlineGoogle Scholar
  • 8. 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):657–664. Crossref, MedlineGoogle Scholar
  • 9. Hur J, Yoon CS, Ryu YH, Yun MJ, Suh JS. Efficacy of multidetector row computed tomography of the spine in patients with multiple myeloma: comparison with magnetic resonance imaging and fluorodeoxyglucose-positron emission tomography. J Comput Assist Tomogr 2007;31(3):342–347. Crossref, MedlineGoogle Scholar
  • 10. Horger M, Pereira P, Claussen CD, et al. Hyperattenuating bone marrow abnormalities in myeloma patients using whole-body non-enhanced low-dose MDCT: correlation with haematological parameters. Br J Radiol 2008;81(965):386–396. Crossref, MedlineGoogle Scholar
  • 11. Healy CF, Murray JG, Eustace SJ, Madewell J, O’Gorman PJ, O’Sullivan P. Multiple myeloma: a review of imaging features and radiological techniques. Bone Marrow Res 2011;2011:583439. Crossref, MedlineGoogle Scholar
  • 12. Johnson TR. Dual-energy CT: general principles. AJR Am J Roentgenol 2012;199(5 Suppl):S3–S8. Crossref, MedlineGoogle Scholar
  • 13. Johnson TR, Krauss B, Sedlmair M, et al. Material differentiation by dual energy CT: initial experience. Eur Radiol 2007;17(6):1510–1517. Crossref, MedlineGoogle Scholar
  • 14. McCollough CH, Leng S, Yu L, Fletcher JG. Dual- and multi-energy CT: principles, technical approaches, and clinical applications. Radiology 2015;276(3):637–653. LinkGoogle Scholar
  • 15. Pache G, Krauss B, Strohm P, et al. Dual-energy CT virtual noncalcium technique: detecting posttraumatic bone marrow lesions–feasibility study. Radiology 2010;256(2):617–624. LinkGoogle Scholar
  • 16. Guggenberger R, Gnannt R, Hodler J, et al. Diagnostic performance of dual-energy CT for the detection of traumatic bone marrow lesions in the ankle: comparison with MR imaging. Radiology 2012;264(1):164–173. LinkGoogle Scholar
  • 17. Wang CK, Tsai JM, Chuang MT, Wang MT, Huang KY, Lin RM. Bone marrow edema in vertebral compression fractures: detection with dual-energy CT. Radiology 2013;269(2):525–533. LinkGoogle Scholar
  • 18. Kaup M, Wichmann JL, Scholtz JE, et al. Dual-energy CT-based display of bone marrow edema in osteoporotic vertebral compression fractures: impact on diagnostic accuracy of radiologists with varying levels of experience in correlation to MR imaging. Radiology 2016;280(2):510–519. LinkGoogle Scholar
  • 19. Bierry G, Venkatasamy A, Kremer S, Dosch JC, Dietemann JL. Dual-energy CT in vertebral compression fractures: performance of visual and quantitative analysis for bone marrow edema demonstration with comparison to MRI. Skeletal Radiol 2014;43(4):485–492. Crossref, MedlineGoogle Scholar
  • 20. Karaca L, Yuceler Z, Kantarci M, et al. The feasibility of dual-energy CT in differentiation of vertebral compression fractures. Br J Radiol 2016;89(1057):20150300. Crossref, MedlineGoogle Scholar
  • 21. Thomas C, Schabel C, Krauss B, et al. Dual-energy CT: virtual calcium subtraction for assessment of bone marrow involvement of the spine in multiple myeloma. AJR Am J Roentgenol 2015;204(3):W324–W331. Crossref, MedlineGoogle Scholar
  • 22. Gordic S, Desbiolles L, Stolzmann P, et al. Advanced modelled iterative reconstruction for abdominal CT: qualitative and quantitative evaluation. Clin Radiol 2014;69(12):e497–e504. Crossref, MedlineGoogle Scholar
  • 23. Nickoloff EL, Feldman F, Atherton JV. Bone mineral assessment: new dual-energy CT approach. Radiology 1988;168(1):223–228. LinkGoogle Scholar
  • 24. Liu X, Yu L, Primak AN, McCollough CH. Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition. Med Phys 2009;36(5):1602–1609. Crossref, MedlineGoogle Scholar
  • 25. Petritsch B, Kosmala A, Weng AM, et al. Vertebral compression fractures: third-generation dual-energy CT for detection of bone marrow edema at visual and quantitative analyses. Radiology 2017 Feb 27:162165. [Epub ahead of print] Google Scholar
  • 26. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33(1):159–174. Crossref, MedlineGoogle Scholar
  • 27. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143(1):29–36. LinkGoogle Scholar
  • 28. Baur-Melnyk A, Buhmann S, Becker C, et al. Whole-body MRI versus whole-body MDCT for staging of multiple myeloma. AJR Am J Roentgenol 2008;190(4):1097–1104. Crossref, MedlineGoogle Scholar
  • 29. Yu L, Primak AN, Liu X, McCollough CH. Image quality optimization and evaluation of linearly mixed images in dual-source, dual-energy CT. Med Phys 2009;36(3):1019–1024. Crossref, MedlineGoogle Scholar
  • 30. Mangold S, Thomas C, Fenchel M, et al. Virtual nonenhanced dual-energy CT urography with tin-filter technology: determinants of detection of urinary calculi in the renal collecting system. Radiology 2012;264(1):119–125. LinkGoogle Scholar
  • 31. Thomas C, Krauss B, Ketelsen D, et al. Differentiation of urinary calculi with dual energy CT: effect of spectral shaping by high energy tin filtration. Invest Radiol 2010;45(7):393–398. Crossref, MedlineGoogle Scholar
  • 32. Toepker M, Moritz T, Krauss B, et al. Virtual non-contrast in second-generation, dual-energy computed tomography: reliability of attenuation values. Eur J Radiol 2012;81(3):e398–e405. Crossref, MedlineGoogle Scholar
  • 33. Bolan PJ, Arentsen L, Sueblinvong T, et al. Water-fat MRI for assessing changes in bone marrow composition due to radiation and chemotherapy in gynecologic cancer patients. J Magn Reson Imaging 2013;38(6):1578–1584. Crossref, MedlineGoogle Scholar
  • 34. 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):1902–1908. Crossref, MedlineGoogle Scholar
  • 35. Hillengass J, Fechtner K, Weber MA, et al. Prognostic significance of focal lesions in whole-body magnetic resonance imaging in patients with asymptomatic multiple myeloma. J Clin Oncol 2010;28(9):1606–1610. Crossref, MedlineGoogle Scholar

Article History

Received February 3, 2017; revision requested April 6; revision received April 26; accepted May 11; final version accepted May 24.
Published online: Aug 11 2017
Published in print: Jan 2018