TMOD-09. GLIOBLASTOMA BIOPHYSICAL GROWTH ESTIMATION USING DEEP LEARNING-BASED REGRESSION

Abstract

Abstract BACKGROUND Glioblastomas are arguably the most aggressive, infiltrative, and heterogeneous adult brain tumor. Biophysical modeling of glioblastoma growth has shown its predictive value towards clinical endpoints, enabling more informed decision-making. However, the mathematically rigorous formulations of biophysical modeling come with a large computational footprint, hindering their application to clinical studies. METHODS We present a deep learning (DL)-based logistical regression model, to estimate in seconds glioblastoma biophysical growth, defined through three tumor-specific parameters: 1) diffusion coefficient of white matter (Dw), which describes how easily the tumor can infiltrate through the white matter, 2) mass-effect parameter (Mp), which defines the average tumor expansion, and 3) estimated time (T) in number of days that the tumor has been growing. Pre-operative multi-parametric MRI (mpMRI) structural scans (T1, T1-Gd, T1, T2-FLAIR) from 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 T2-FLAIR signal envelope, for training three DL models for the three tumor-specific parameters. Each of our DL models consist of two sets of convolution layers followed by a single max-pooling layer, with a normalized root mean squared error as the minimization metric and evaluated using 10-fold cross validation. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. RESULTS Pearson correlation coefficients between our DL-based estimations and the biophysical parameters were equal to 0.85 for Dw, 0.90 for Mp, and 0.94 for T. CONCLUSION This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation, paving the way towards their utilization in more clinical studies, while opening the door for leveraging advanced radiomic descriptors in future studies, as well as allowing for significantly faster parameter reconstruction compared to biophysical growth modeling approaches. *denotes equal senior authorship.