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International MICCAI Brainlesion Workshop

BrainLes 2020: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries pp 157–167Cite as

Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12658))

Abstract

Glioblastoma (GBM) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Here we present a scheme to accelerate the computation. In particular, we present a deep learning (DL)-based logistic regression model to estimate the GBM’s biophysical growth in seconds. This growth is defined by three tumor-specific parameters: 1) a diffusion coefficient in white matter (Dw), which prescribes the rate of infiltration of tumor cells in white matter, 2) a mass-effect parameter (Mp), which defines the average tumor expansion, and 3) the estimated time (T) in number of days that the tumor has been growing. Preoperative structural multi-parametric MRI (mpMRI) scans from \(n=135\) subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for each of the tumor-specific growth parameters. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. The average Pearson correlation coefficients between our DL-based estimations and the biophysical parameters are 0.85 for Dw, 0.90 for Mp, and 0.94 for T, respectively. This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation. It paves the way towards their clinical translation and opens the door for leveraging advanced radiomic descriptors in future studies by means of a significantly faster parameter reconstruction compared to biophysical growth modeling approaches.

C. Davatzikos and S. Bakas—Equally contributing senior author.

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Notes

  1. 1.

    https://www.med.upenn.edu/cbica/sbia/glistrboost.html.

  2. 2.

    https://github.com/pyushkevich/greedy.

  3. 3.

    http://www.itksnap.org.

  4. 4.

    https://www.cbica.upenn.edu/captk.

  5. 5.

    https://github.com/CBICA/BrainMaGe/.

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Acknowledgments

Research reported in this publication was partly supported by the National Institutes of Health (NIH) under award number NIH/NCI:U01CA242871, NIH/NINDS:R01NS042645, NIH/NCI:U24CA189523. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH.

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Authors and Affiliations

  1. Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA

    Sarthak Pati, Siddhesh P. Thakur, Hamed Akbari, Christos Davatzikos & Spyridon Bakas

  2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sarthak Pati, Siddhesh P. Thakur, Hamed Akbari, Christos Davatzikos & Spyridon Bakas

  3. Department of Electrical and Electronics Engineering, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

    Vaibhav Sharma & Heena Aslam

  4. Department of Mathematics, University of Houston, Houston, TX, USA

    Andreas Mang

  5. Oden Institute of Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA

    Shashank Subramanian & George Biros

  6. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Sarthak Pati, Siddhesh P. Thakur & Spyridon Bakas

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  1. Sarthak Pati
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  2. Vaibhav Sharma
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Correspondence to Spyridon Bakas .

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Editors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

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Pati, S. et al. (2021). Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-72084-1_15

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