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Predicting Future Bone Infiltration Patterns in Multiple Myeloma

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

Abstract

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.

Notes

Acknowledgement

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

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Roxane Licandro
    • 1
    • 2
  • 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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