Predicting Future Bone Infiltration Patterns in Multiple Myeloma

  • Roxane LicandroEmail author
  • Johannes Hofmanninger
  • Marc-André Weber
  • Bjoern Menze
  • Georg Langs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)


Multiple Myeloma (MM) is a bone marrow malignancy affecting the generation pathway of plasma cells and B-lymphocytes. It results in their uncontrolled proliferation and malignant transformation and ultimately can lead to osteolytic lesions first visible in MRI. The earliest possible reliable detection of these lesions is critical, since they are a prime marker of disease advance and a trigger for treatment. However, their detection is difficult. Here, we present and evaluate a methodology to predict future lesion emergence based on T1 weighted Magnetic Resonance Imaging (MRI) patch data. We train a predictor to identify early signatures of emerging lesions before they reach thresholds for reporting. The algorithm proposed uses longitudinal training data, and visualises high- risk locations in the bone structure.



This work was supported by the Austrian Science Fund (FWF) project number I2714-B31


  1. 1.
    Dimopoulos, M.A., et al.: Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement. J. Clin. Oncol. 33(6), 657–664 (2015)CrossRefGoogle Scholar
  2. 2.
    Durie, B.G.M., et al.: Myeloma management guidelines: a consensus report from the scientific advisors of the international Myeloma foundation. Hematol. J. 4(6), 379–398 (2003)CrossRefGoogle Scholar
  3. 3.
    Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782–90 (2012)CrossRefGoogle Scholar
  4. 4.
    Kloth, J.K., et al.: Appearance of monoclonal plasma cell diseases in whole-body magnetic resonance imaging and correlation with parameters of disease activity. Int. J. Cancer 135(10), 2380–2386 (2014)CrossRefGoogle Scholar
  5. 5.
    Kyle, R.A., Rajkumar, S.V.: Multiple myeloma. Blood 111(6), 2962–72 (2008)CrossRefGoogle Scholar
  6. 6.
    Lambert, L., Ourednicek, P., Meckova, Z., Gavelli, G., Straub, J., Spicka, I.: Whole-body low-dose computed tomography in multiple myeloma staging: superior diagnostic performance in the detection of bone lesions, vertebral compression fractures, rib fractures and extraskeletal findings compared to radiography with similar radiation. Oncol. Lett. 13(4), 2490–2494 (2017)CrossRefGoogle Scholar
  7. 7.
    Mateos, M.-V., 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. 17(8), 1127–1136 (2016)CrossRefGoogle Scholar
  8. 8.
    Merz, M., et al.: Predictive value of longitudinal whole-body magnetic resonance imaging in patients with smoldering multiple myeloma. Leukemia 28(9), 1902–1908 (2014)CrossRefGoogle Scholar
  9. 9.
    Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imaging (Bellingham, Wash.) 1(2), 024003 (2014)CrossRefGoogle Scholar
  10. 10.
    Tosi, P.: Diagnosis and treatment of bone disease in multiple Myeloma: spotlight on spinal involvement. Scientifica, p. 104546Google Scholar
  11. 11.
    Xu, L., et al.: Automated whole-body bone lesion detection for multiple myeloma on 68 ga-pentixafor PET/CT imaging using deep learning methods. Contrast Media Mol. Imaging 2018, 1–11 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Roxane Licandro
    • 1
    • 2
    Email author
  • Johannes Hofmanninger
    • 2
  • Marc-André Weber
    • 3
  • Bjoern Menze
    • 4
  • Georg Langs
    • 2
  1. 1.Institute of Visual Computing and Human-Centered Technology - Computer Vision LabTU WienViennaAustria
  2. 2.Department of Biomedical Imaging and Image-guided Therapy - Computational Imaging Research LabMedical University of ViennaViennaAustria
  3. 3.Institute of Diagnostic and Interventional RadiologyUniversity Medical Center RostockRostockGermany
  4. 4.Institute of Biomedical Engineering - Image-Based Biomedical ModellingTechnische Universität MünchenMunichGermany

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