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Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins. AJNR Am J Neuroradiol 2022 May;43(5):682-688

Date

04/16/2022

Pubmed ID

35422419

Pubmed Central ID

PMC9089258

DOI

10.3174/ajnr.A7477

Scopus ID

2-s2.0-85130001814   5 Citations

Abstract

BACKGROUND AND PURPOSE: Currently, contrast-enhancing margins on T1WI are used to guide treatment of gliomas, yet tumor invasion beyond the contrast-enhancing region is a known confounding factor. Therefore, this study used postmortem tissue samples aligned with clinically acquired MRIs to quantify the relationship between intensity values and cellularity as well as to develop a radio-pathomic model to predict cellularity using MR imaging data.

MATERIALS AND METHODS: This single-institution study used 93 samples collected at postmortem examination from 44 patients with brain cancer. Tissue samples were processed, stained with H&E, and digitized for nuclei segmentation and cell density calculation. Pre- and postgadolinium contrast T1WI, T2 FLAIR, and ADC images were collected from each patient's final acquisition before death. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Mixed-effects 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 × 5 voxel tiles from each image, with a two-thirds to one-third train-test split for validation.

RESULTS: Single-image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship in patients with glioblastoma. The radio-pathomic model accurately predicted cellularity in the test set (root mean squared error = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast-enhancing region.

CONCLUSIONS: A radio-pathomic model for cellularity trained with tissue samples acquired at postmortem examination is able to identify regions of hypercellular tumor beyond traditional imaging signatures.

Author List

Bobholz SA, Lowman AK, Brehler M, Kyereme F, Duenweg SR, Sherman J, McGarry SD, Cochran EJ, Connelly J, Mueller WM, Agarwal M, Banerjee A, LaViolette PS

Authors

Mohit Agarwal MD Assistant Professor in the Radiology department at Medical College of Wisconsin
Anjishnu Banerjee PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Jennifer M. Connelly MD Professor in the Neurology department at Medical College of Wisconsin
Peter LaViolette PhD Associate Professor in the Radiology department at Medical College of Wisconsin
Wade M. Mueller MD Professor in the Neurosurgery department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Brain Neoplasms
Cell Count
Glioblastoma
Glioma
Humans
Magnetic Resonance Imaging
Margins of Excision