Advertisement

European Radiology

, Volume 27, Issue 7, pp 2969–2977 | Cite as

Improved MDCT monitoring of pelvic myeloma bone disease through the use of a novel longitudinal bone subtraction post-processing algorithm

  • Marius Horger
  • Wolfgang M. Thaiss
  • Hendrik Ditt
  • Katja Weisel
  • Jan Fritz
  • Konstantin Nikolaou
  • Shu Liao
  • Christopher Kloth
Computed Tomography

Abstract

Purpose

To evaluate the diagnostic performance of a novel CT post-processing software that generates subtraction maps of baseline and follow-up CT examinations in the course of myeloma bone lesions.

Materials and methods

This study included 61 consecutive myeloma patients who underwent repeated whole-body reduced-dose MDCT at our institution between November 2013 and June 2015. CT subtraction maps classified a progressive disease (PD) vs. stable disease (SD)/remission. Bone subtraction maps (BSMs) only and in combination with 1-mm (BSM+) source images were compared with 5-mm axial/MPR scans.

Results

Haematological response categories at follow-up were: complete remission (n = 9), very good partial remission (n = 2), partial remission (n = 17) and SDh (n = 19) vs. PDh (n = 14). Five-millimetre CT scan yielded PD (n = 14) and SD/remission (n = 47) whereas bone subtraction + 1-mm axial scans (BSM+) reading resulted in PD (n = 18) and SD/remission (n = 43). Sensitivity/ specificity/accuracy for 5-mm/1-mm/BSM(alone)/BSM + in "lesion-by-lesion" reading was 89.4 %/98.9 %/98.3 %/ 99.5 %; 69.1 %/96.9 %/72 %/92.1 % and 83.8 %/98.4 %/92.1 %/98.3 %, respectively. The use of BSM+ resulted in a change of response classification in 9.8 % patients (n = 6) from SD to PD.

Conclusion

BSM reading is more accurate for monitoring myeloma compared to axial scans whereas BSM+ yields similar results with 1-mm reading (gold standard) but by significantly reduced reading time.

Key points

CT evaluation of myeloma bone disease using a longitudinal bone subtraction post-processing algorithm.

Bone subtraction post-processing algorithm is more accurate for assessment of therapy.

Bone subtraction allowed improved and more efficient detection of myeloma bone lesions.

Post-processing tool demonstrating a change in response classification in 9.8 % patients (all showing PD).

Reading time could be substantially shortened as compared to regular CT assessment.

Keywords

Multiple myeloma Lytic bone lesions Pelvic bones CT imaging Bone subtraction maps 

Notes

Acknowledgements

We thank Claudia Kroll (medical technician) for the best support in carrying out this study.

The scientific guarantor of this publication is Prof. Dr. Marius Horger. The authors of this manuscript declare relationships with the following companies: Two of the authors (H.D. and S.L.) are Siemens AG staff members. One author (H.M.) has contributed conceptually to the development of this post-processing software but has no financial interest to disclose. Jan Fritz received institutional research funds and speaker's honorarium from Siemens Healthcare USA and is a scientific advisor of Siemens Healthcare USA and Alexion Pharmaceuticals, Inc. One of the authors has significant statistical expertise (CK). Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. No study subjects or cohorts have been previously reported.

Methodology: retrospective, diagnostic or prognostic study, performed at one institution.

References

  1. 1.
    Homann G, Mustafa DF, Ditt H et al (2015) Improved detection of bone metastases from lung cancer in the thoracic cage using 5- and 1-mm axial images versus a new CT software generating rib unfolding images: Comparison with standard (1)(8)F-FDG-PET/CT. Acad Radiol 22:505–512CrossRefPubMedGoogle Scholar
  2. 2.
    Homann G, Weisel K, Mustafa DF, Ditt H, Nikolaou K, Horger M (2015) Improvement of diagnostic confidence for detection of multiple myeloma involvement of the ribs by a new CT software generating rib unfolded images: Comparison with 5- and 1-mm axial images. Skeletal Radiol 44:971–979CrossRefPubMedGoogle Scholar
  3. 3.
    Toth DF, Topker M, Mayerhofer ME et al (2014) Rapid detection of bone metastasis at thoracoabdominal CT: Accuracy and efficiency of a new visualization algorithm. Radiology 270:825–833CrossRefPubMedGoogle Scholar
  4. 4.
    Schulze M, Weisel K, Grandjean C et al (2014) Increasing bone sclerosis during bortezomib therapy in multiple myeloma patients: Results of a reduced-dose whole-body MDCT study. AJR Am J Roentgenol 202:170–179CrossRefPubMedGoogle Scholar
  5. 5.
    Durie BG, Salmon SE (1975) A clinical staging system for multiple myeloma. correlation of measured myeloma cell mass with presenting clinical features, response to treatment, and survival. Cancer 36:842–854CrossRefPubMedGoogle Scholar
  6. 6.
    Palumbo A, Avet-Loiseau H, Oliva S et al (2015) Revised international staging system for multiple myeloma: A report from the International Myeloma Working Group. J Clin Oncol 33:2863–2869CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Horger M, Pereira P, Claussen CD et al (2008) Hyperattenuating bone marrow abnormalities in myeloma patients using whole-body non-enhanced low-dose MDCT: Correlation with haematological parameters. Br J Radiol 81:386–396CrossRefPubMedGoogle Scholar
  8. 8.
    Viola P, Jones M (2004) Robust real-time face detection. Int J Comput Vision 57:137–154CrossRefGoogle Scholar
  9. 9.
    Viola PWW (1997) Alignment by maximization of mutual information. Int J Comput Vision 24:137–154CrossRefGoogle Scholar
  10. 10.
    Zhan Y, Dewan M, Harder M, Krishnan A, Zhou XS (2011) Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection. IEEE Trans Med Imaging 30:2087–2100CrossRefPubMedGoogle Scholar
  11. 11.
    International Myeloma Working Group (2003) Criteria for the classification of monoclonal gammopathies, multiple myeloma and related disorders: A report of the international myeloma working group. Br J Haematol 121:749–757CrossRefGoogle Scholar
  12. 12.
    Mohty M, Malard F, Mohty B, Savani B, Moreau P, Terpos E (2014) The effects of bortezomib on bone disease in patients with multiple myeloma. Cancer 120:618–623CrossRefPubMedGoogle Scholar
  13. 13.
    Princewill K, Kyere S, Awan O, Mulligan M (2013) Multiple myeloma lesion detection with whole body CT versus radiographic skeletal survey. Cancer Invest 31:206–211CrossRefPubMedGoogle Scholar
  14. 14.
    Wolf MB, Murray F, Kilk K et al (2014) 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 83:1222–1230CrossRefPubMedGoogle Scholar
  15. 15.
    Gleeson TG, Moriarty J, Shortt CP et al (2009) Accuracy of whole-body low-dose multidetector CT (WBLDCT) versus skeletal survey in the detection of myelomatous lesions, and correlation of disease distribution with whole-body MRI (WBMRI). Skeletal Radiol 38:225–236CrossRefPubMedGoogle Scholar
  16. 16.
    Baur-Melnyk A, Buhmann S, Becker C et al (2008) Whole-body MRI versus whole-body MDCT for staging of multiple myeloma. AJR Am J Roentgenol 190:1097–1104CrossRefPubMedGoogle Scholar
  17. 17.
    Hur J, Yoon CS, Ryu YH, Yun MJ, Suh JS (2007) 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 31:342–347CrossRefPubMedGoogle Scholar
  18. 18.
    Moulopoulos LA, Dimopoulos MA, Alexanian R, Leeds NE, Libshitz HI (1994) Multiple myeloma: MR patterns of response to treatment. Radiology 193:441–446CrossRefPubMedGoogle Scholar
  19. 19.
    Lecouvet FE, Dechambre S, Malghem J, Ferrant A, Vande Berg BC, Maldague B (2001) Bone marrow transplantation in patients with multiple myeloma: Prognostic significance of MR imaging. AJR Am J Roentgenol 176:91–96CrossRefPubMedGoogle Scholar
  20. 20.
    Zacchino M, Bonaffini PA, Corso A et al (2015) Inter-observer agreement for the evaluation of bone involvement on whole body low dose computed tomography (WBLDCT) in multiple myeloma (MM). Eur Radiol 25:3382–3389CrossRefPubMedGoogle Scholar
  21. 21.
    Dutoit JC, Verstraete KL (2016) MRI in multiple myeloma: A pictorial review of diagnostic and post-treatment findings. Insights Imaging 7:553–569CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Fonti R, Salvatore B, Quarantelli M et al (2008) 18F-FDG PET/CT, 99mTc-MIBI, and MRI in evaluation of patients with multiple myeloma. J Nucl Med 49:195–200CrossRefPubMedGoogle Scholar
  23. 23.
    Wahl RL, Jacene H, Kasamon Y, Lodge MA (2009) From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors. J Nucl Med 50(Suppl 1):122S–150SCrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Oyajobi BO, Garrett IR, Gupta A et al (2007) Stimulation of new bone formation by the proteasome inhibitor, bortezomib: Implications for myeloma bone disease. Br J Haematol 139:434–438CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Pennisi A, Li X, Ling W, Khan S, Zangari M, Yaccoby S (2009) The proteasome inhibitor, bortezomib suppresses primary myeloma and stimulates bone formation in myelomatous and nonmyelomatous bones in vivo. Am J Hematol 84:6–14CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Evans HR, Karmakharm T, Lawson MA et al (2015) Osteolytica: An automated image analysis software package that rapidly measures cancer-induced osteolytic lesions in in vivo models with greater reproducibility compared to other commonly used methods. Bone 83:9–16CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Marius Horger
    • 1
  • Wolfgang M. Thaiss
    • 1
  • Hendrik Ditt
    • 2
  • Katja Weisel
    • 3
  • Jan Fritz
    • 4
  • Konstantin Nikolaou
    • 1
  • Shu Liao
    • 5
  • Christopher Kloth
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyEberhard-Karls-University TübingenTuebingenGermany
  2. 2.Siemens AG HealthcareSector Imaging and Interventional RadiologyForchheimGermany
  3. 3.Department of Internal Medicine IIEberhard-Karls-University TübingenTübingenGermany
  4. 4.Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedcineBaltimoreUSA
  5. 5.Siemens Medical SolutionsMalvernUSA

Personalised recommendations