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Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning

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Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (MLCN 2020, RNO-AI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12449))

Abstract

Disease progression for Glioblastoma multiforme patients is currently assessed with manual bi-dimensional measurements of the active contrast-enhancing tumor on Magnetic Resonance Images (MRI). This method is known to be susceptible to error; in the lack of a data-driven approach, progression thresholds had been set rather arbitrarily considering measurement inaccuracies. We propose a data-driven methodology for disease progression assessment, building on tumor volumetry, classical radiomics, and deep learning based features. For each feature type, we infer progression thresholds by maximizing the correlation of the time-to-progression (TTP) and overall survival (OS). On a longitudinal study comprising over 500 data points, we observed considerable underestimation of the current volumetric disease progression threshold. We evaluate the data-driven disease progression thresholds based on expert ratings using the current clinical practice.

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Notes

  1. 1.

    Following the recommendations for the number of bins in the PyRadiomics documentation.

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Acknowledgements

We gladly acknowledge the funding received from the Swiss Cancer League (Krebsliga Schweiz), grant KFS-3979-08-2016 and the NVIDIA Corporation for donating a Titan Xp GPU. Computations were partly performed on Ubelix, the HCP cluster at the University of Bern.

Author information

Authors and Affiliations

  1. Insel Data Science Center, Inselspital, Bern University Hospital, Bern, Switzerland

    Yannick Suter & Mauricio Reyes

  2. ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland

    Yannick Suter, Urspeter Knecht & Mauricio Reyes

  3. Radiology Department, Spital Emmental, Burgdorf, Switzerland

    Urspeter Knecht

  4. Support Center for Advanced Neuroimaging, Inselspital, Bern University Hospital, Bern, Switzerland

    Roland Wiest

  5. Pathology Institute, University of Bern, Bern, Switzerland

    Ekkehard Hewer

  6. Department of Neurosurgery, Inselspital, Bern University Hospital, Bern, Switzerland

    Philippe Schucht

Authors
  1. Yannick Suter
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  2. Urspeter Knecht
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  3. Roland Wiest
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  4. Ekkehard Hewer
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  5. Philippe Schucht
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  6. Mauricio Reyes
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Corresponding author

Correspondence to Yannick Suter .

Editor information

Editors and Affiliations

  1. Donders Institute, Nijmegen, The Netherlands

    Seyed Mostafa Kia

  2. Lahore University of Management Sciences, Lahore, Pakistan

    Hassan Mohy-ud-Din

  3. University of Pennsylvania, Philadelphia, PA, USA

    Ahmed Abdulkadir

  4. King’s College London, London, UK

    Cher Bass

  5. The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

    Mohamad Habes

  6. University College London, London, UK

    Jane Maryam Rondina

  7. CUBRIC, Cardiff, UK

    Chantal Tax

  8. IBM Almaden Research Center, San Jose, CA, USA

    Hongzhi Wang

  9. University of Oslo, Oslo, Norway

    Thomas Wolfers

  10. Eli Lilly Pharmaceutical Company, Philadelphia, PA, USA

    Saima Rathore

  11. Symbiosis Institute of Technology, Pune, India

    Madhura Ingalhalikar

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Suter, Y., Knecht, U., Wiest, R., Hewer, E., Schucht, P., Reyes, M. (2020). Towards MRI Progression Features for Glioblastoma Patients: From Automated Volumetry and Classical Radiomics to Deep Feature Learning. In: Kia, S.M., et al. Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN RNO-AI 2020 2020. Lecture Notes in Computer Science(), vol 12449. Springer, Cham. https://doi.org/10.1007/978-3-030-66843-3_13

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  • DOI: https://doi.org/10.1007/978-3-030-66843-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66842-6

  • Online ISBN: 978-3-030-66843-3

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