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


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.


  • Glioblastoma
  • Deep features
  • Disease progression
  • Radiomics

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


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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.

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Correspondence to Yannick Suter .

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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: , 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.

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