Radio-pathomic maps of cell density identify glioma invasion beyond traditional MR imaging defined margins

Abstract

Current MRI signatures of brain cancer often fail to identify regions of hypercellularity beyond the contrast enhancing region. Therefore, this study used autopsy tissue samples aligned to clinical MRIs in order to quantify the relationship between intensity values and cellularity, as well as to develop a radio-pathomic model to predict cellularity using MRI data. This study used 93 samples collected at autopsy from 44 brain cancer patients. Tissue samples were processed, stained for hematoxylin and eosin (HE) and digitized for nuclei segmentation and cell density calculation. Pre- and post-gadolinium contrast T1-weighted images (T1, T1C), T2 fluid-attenuated inversion recovery (FLAIR) images, and apparent diffusion coefficient (ADC) images calculated from diffusion imaging were collected from each patients final acquisition prior to death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed effect models were used to assess the relationship between single image intensity and cellularity for each image. An ensemble learner was trained to predict cellularity using 5 by 5 voxel tiles from each image, employing a 2/3-1/3 train-test split for validation. Results: Single image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship within GBM patients. The radio-pathomic model was able to accurately predict cellularity in the test set (RMSE = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast enhancing region. We concluded that a radio-pathomic model for cellularity is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

Initial Condition Assessment for Reaction-Diffusion Glioma Growth Models: A Translational MRI-Histology (In)Validation Study

“…Beyond the field of reaction-diffusion tumor growth modeling, assessing the tumor cell density distribution in gliomas is also of utmost importance for treatment planning and response assessment [ 33 , 34 , 35 , 36 ]. To this extend, advanced MR acquisition and processing methods have been proposed previously.…”

“…In [ 36 ], anatomic, DW, and dynamic contrast-enhanced MR sequences are used to predict cell density values assessed by biopsies using a random forest classifier, with moderate-to-strong correlations reported between the observed and predicted density values. A similar approach was adopted in [ 35 ], with a reported root mean squared error of 1015 on test data. Aside from MRI, the use of positron emission tomography (PET) imaging to assess glioma cell density has also been investigated previously.…”