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Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software



The diagnostic reading of follow-up low-dose whole-body computed tomography (WBCT) examinations in patients with multiple myeloma (MM) is a demanding process. This study aimed to evaluate the diagnostic accuracy and benefit of a novel software program providing rapid-subtraction maps for bone lesion change detection.


Sixty patients (66 years ± 10 years) receiving 120 WBCT examinations for follow-up evaluation of MM bone disease were identified from our imaging archive. The median follow-up time was 292 days (range 200–641 days). Subtraction maps were calculated from 2-mm CT images using a nonlinear deformation algorithm. Reading time, correctly assessed lesions, and disease classification were compared to a standard reading software program. De novo clinical reading by a senior radiologist served as the reference standard. Statistics included Wilcoxon rank-sum test, Cohen’s kappa coefficient, and calculation of sensitivity, specificity, positive/negative predictive value, and accuracy.


Calculation time for subtraction maps was 84 s ± 24 s. Both readers reported exams faster using subtraction maps (reader A, 438 s ± 133 s; reader B, 1049 s ± 438 s) compared to PACS software (reader A, 534 s ± 156 s; reader B, 1486 s ± 587 s; p < 0.01). The course of disease was correctly classified by both methods in all patients. Sensitivity for lesion detection in subtraction maps/conventional reading was 92%/80% for reader A and 88%/76% for reader B. Specificity was 98%/100% for reader A and 95%/96% for reader B.


A software program for the rapid-subtraction map calculation of follow-up WBCT scans has been successfully tested and seems suited for application in clinical routine. Subtraction maps significantly facilitated reading of WBCTs by reducing reading time and increasing sensitivity.

Key Points

A novel algorithm has been successfully applied to generate motion-corrected bone subtraction maps of whole-body low-dose CT scans in less than 2 min.

Motion-corrected bone subtraction maps significantly facilitate the reading of follow-up whole-body low-dose CT scans in multiple myeloma by reducing reading time and increasing sensitivity.

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Multiple myeloma


Picture archiving and communication system


Revised International Staging System


Salmon and Durie


Whole-body computed tomography


  1. 1.

    Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin. https://doi.org/10.3322/caac.21551

  2. 2.

    Dimopoulos M, Kyle R, Fermand JP et al (2011) Consensus recommendations for standard investigative workup: report of the International Myeloma Workshop Consensus Panel 3. Blood 117:4701–4705

  3. 3.

    Hameed A, Brady JJ, Dowling P, Clynes M, O’Gorman P (2014) Bone disease in multiple myeloma: pathophysiology and management. Cancer Growth Metastasis 7:33–42

  4. 4.

    Kyle RA, Gertz MA, Witzig TE et al (2003) Review of 1027 patients with newly diagnosed multiple myeloma. Mayo Clin Proc 78:21–33

  5. 5.

    Schabel C, Horger M, Kum S et al (2016) Simplified response monitoring criteria for multiple myeloma in patients undergoing therapy with novel agents using computed tomography. Eur J Radiol 85:2195–2199

  6. 6.

    Ghosh S, Wadhwa P, Kumar A, Pai K, Seshadri S, Manohar C (2011) Abnormal radiological features in a multiple myeloma patient: a case report and radiological review of myelomas. Dentomaxillofac Radiol 40:513–518

  7. 7.

    Tosi P (2013) Diagnosis and treatment of bone disease in multiple myeloma: spotlight on spinal involvement. Scientifica (Cairo) 2013:104546

  8. 8.

    Cocks K, Cohen D, Wisloff F et al (2007) An international field study of the reliability and validity of a disease-specific questionnaire module (the QLQ-MY20) in assessing the quality of life of patients with multiple myeloma. Eur J Cancer 43:1670–1678

  9. 9.

    Bingham N, Reale A, Spencer A (2017) An evidence-based approach to myeloma bone disease. Curr Hematol Malig Rep. https://doi.org/10.1007/s11899-017-0370-5

  10. 10.

    Moreau P, San Miguel J, Sonneveld P et al (2017) Multiple myeloma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 28:iv52–iv61

  11. 11.

    Pawlyn C, Fowkes L, Otero S et al (2016) Whole-body diffusion-weighted MRI: a new gold standard for assessing disease burden in patients with multiple myeloma? Leukemia 30:1446–1448

  12. 12.

    Lai AYT, Riddell A, Barwick T et al (2019) 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. https://doi.org/10.1007/s00330-019-06281-x

  13. 13.

    Hillengass J, Usmani S, Rajkumar SV et al (2019) International myeloma working group consensus recommendations on imaging in monoclonal plasma cell disorders. Lancet Oncol 20:e302–e312

  14. 14.

    Chantry A, Kazmi M, Barrington S et al (2017) Guidelines for the use of imaging in the management of patients with myeloma. Br J Haematol 178:380–393

  15. 15.

    (2016) Myeloma: diagnosis and management. (National Institute for Health and Care Excellence: Clinical Guidelines), London, pp nice.org.uk/guidance/ng35

  16. 16.

    Merz M, Hielscher T, Wagner B et al (2014) Predictive value of longitudinal whole-body magnetic resonance imaging in patients with smoldering multiple myeloma. Leukemia 28:1902–1908

  17. 17.

    Moreau P, San Miguel J, Ludwig H et al (2013) Multiple myeloma: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 24(Suppl 6):vi133–vi137

  18. 18.

    Caers J, Garderet L, Kortum KM et al (2018) European Myeloma Network recommendations on tools for the diagnosis and monitoring of multiple myeloma: what to use and when. Haematologica 103:1772–1784

  19. 19.

    Pianko MJ, Terpos E, Roodman GD et al (2014) Whole-body low-dose computed tomography and advanced imaging techniques for multiple myeloma bone disease. Clin Cancer Res 20:5888–5897

  20. 20.

    Ueno M, Aoki T, Murakami S et al (2018) CT temporal subtraction method for detection of sclerotic bone metastasis in the thoracolumbar spine. Eur J Radiol 107:54–59

  21. 21.

    Horger M, Fritz J, Thaiss WM et al (2018) Comparison of qualitative and quantitative CT and MRI parameters for monitoring of longitudinal spine involvement in patients with multiple myeloma. Skeletal Radiol 47:351–361

  22. 22.

    Sakamoto R, Yakami M, Fujimoto K et al (2017) Temporal subtraction of serial CT images with large deformation diffeomorphic metric mapping in the identification of bone metastases. Radiology 285:629–639

  23. 23.

    Iwano S, Ito R, Umakoshi H et al (2017) Thoracic temporal subtraction three dimensional computed tomography (3D-CT): screening for vertebral metastases of primary lung cancers. PLoS One 12:e0170309

  24. 24.

    Horger M, Thaiss WM, Wiesinger B et al (2017) Longitudinal computed tomography monitoring of pelvic bones in patients with breast cancer using automated bone subtraction software. Invest Radiol 52:288–294

  25. 25.

    Horger M, Ditt H, Liao S et al (2017) Automated “bone subtraction” image analysis software package for improved and faster CT monitoring of longitudinal spine involvement in patients with multiple myeloma. Acad Radiol 24:623–632

  26. 26.

    Onoue K, Nishio M, Yakami M et al (2019) CT temporal subtraction improves early detection of bone metastases compared to SPECT. Eur Radiol. https://doi.org/10.1007/s00330-019-06107-w

  27. 27.

    Bossuyt PM, Reitsma JB, Bruns DE et al (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. Radiology 277:826–832

  28. 28.

    Durie BG, Kyle RA, Belch A et al (2003) Myeloma management guidelines: a consensus report from the Scientific Advisors of the International Myeloma Foundation. Hematol J 4:379–398

  29. 29.

    König L, Rühaak J, Derksen A, Lellmann J (2018) A matrix-free approach to parallel and memory-efficient deformable image registration. SIAM J Sci Comput 40:B858–B888

  30. 30.

    Fischer B, Modersitzki J (2003) Curvature based image registration. J Math Imaging Vision 18:81–85

  31. 31.

    Haber E, Modersitzki J (2006) Intensity gradient based registration and fusion of multi-modal images. In: Larsen R, Nielsen M, Sporring J (Eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4191. Springer, Berlin, Heidelberg

  32. 32.

    Pomerantz SM, White CS, Krebs TL et al (2000) Liver and bone window settings for soft-copy interpretation of chest and abdominal CT. AJR Am J Roentgenol 174:311–314

  33. 33.

    Heindel W, Gubitz R, Vieth V, Weckesser M, Schober O, Schafers M (2014) The diagnostic imaging of bone metastases. Dtsch Arztebl Int 111:741–747

  34. 34.

    Ruhaak J, Polzin T, Heldmann S et al (2017) Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans Med Imaging 36:1746–1757

  35. 35.

    Grob D, Oostveen L, Ruhaak J et al (2019) Accuracy of registration algorithms in subtraction CT of the lungs: a digital phantom study. Med Phys 46:2264–2274

  36. 36.

    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 44:971–979

  37. 37.

    Scholtz JE, Wichmann JL, Kaup M et al (2015) First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT. Eur J Radiol 84:437–442

  38. 38.

    Hardisty M, Gordon L, Agarwal P, Skrinskas T, Whyne C (2007) Quantitative characterization of metastatic disease in the spine. Part I. Semiautomated segmentation using atlas-based deformable registration and the level set method. Med Phys 34:3127–3134

  39. 39.

    O’Connor SD, Yao J, Summers RM (2007) Lytic metastases in thoracolumbar spine: computer-aided detection at CT—preliminary study. Radiology 242:811–816

  40. 40.

    Skrinskas T, Clemons M, Freedman O, Weller I, Whyne CM (2009) Automated CT-based analysis to detect changes in the prevalence of lytic bone metastases from breast cancer. Clin Exp Metastasis 26:97–103

  41. 41.

    Hammon M, Dankerl P, Tsymbal A et al (2013) Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography. Eur Radiol 23:1862–1870

  42. 42.

    Akasaka T, Yakami M, Nishio M et al (2019) Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images. Eur Radiol 29:759–769

  43. 43.

    Kosmala A, Weng AM, Krauss B, Knop S, Bley TA, Petritsch B (2018) Dual-energy CT of the bone marrow in multiple myeloma: diagnostic accuracy for quantitative differentiation of infiltration patterns. Eur Radiol 28:5083–5090

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The authors warmly thank Prof. Inke König for her skillful statistical advice.


The authors state that this work has not received any funding.

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Correspondence to M. M. Sieren.

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The scientific guarantor of this publication is Prof. Dr. Alex Frydrychowicz, Department of Radiology and Nuclear Medicine, UKSH, Campus Lübeck.

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The authors declare that they have no conflict of interest.

Statistics and biometry

Prof. Inke König kindly provided statistical advice for this manuscript.

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Written informed consent was waived by the institutional review board.

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Sieren, M.M., Brenne, F., Hering, A. et al. Rapid study assessment in follow-up whole-body computed tomography in patients with multiple myeloma using a dedicated bone subtraction software. Eur Radiol (2020). https://doi.org/10.1007/s00330-019-06631-9

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  • Multiple myeloma
  • Whole-body imaging
  • Algorithms
  • X-ray computed tomography
  • Subtraction technique