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Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins
S A Bobholz1, A K Lowman2, M Brehler2
1From the Departments of Biophysics (S.A.B., S.R.D., J.S., S.D.M.).
AJNR. American Journal of Neuroradiology
|April 15, 2022
Summary
This study developed a novel radio-pathomic model using postmortem brain tissue and MRI data to accurately predict glioma cellularity. The model identifies hypercellular tumor regions beyond contrast-enhancing margins, improving treatment guidance.
Area of Science:
- Neuro-oncology
- Radiology
- Computational Pathology
Background:
- Contrast-enhancing margins on T1-weighted imaging (T1WI) guide glioma treatment.
- Tumor invasion beyond these enhancing regions is a significant confounding factor in treatment planning.
Purpose of the Study:
- To quantify the relationship between MRI intensity values and cellularity in postmortem brain tissue.
- To develop a radio-pathomic model for predicting glioma cellularity using MRI data.
Main Methods:
- Utilized 93 postmortem tissue samples from 44 brain cancer patients.
- Aligned digitized H&E stained tissue samples with pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC MRI images.
- Trained an ensemble learner model to predict cellularity from 5x5 voxel image tiles.
Main Results:
- Found subtle associations between image intensity and cellularity, less pronounced in glioblastoma.
- The radio-pathomic model accurately predicted cellularity (RMSE = 1015 cells/mm²).
- Identified regions of hypercellularity extending beyond contrast-enhancing areas.
Conclusions:
- A postmortem-derived radio-pathomic model can predict glioma cellularity.
- This model identifies hypercellular tumor regions missed by traditional imaging signatures.
