PT  - JOURNAL ARTICLE
AU  - Bobholz, S.A.
AU  - Lowman, A.K.
AU  - Brehler, M.
AU  - Kyereme, F.
AU  - Duenweg, S.R.
AU  - Sherman, J.
AU  - McGarry, S.D.
AU  - Cochran, E.J.
AU  - Connelly, J.
AU  - Mueller, W.M.
AU  - Agarwal, M.
AU  - Banerjee, A.
AU  - LaViolette, P.S.
TI  - Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins
AID  - 10.3174/ajnr.A7477
DP  - 2022 May 01
TA  - American Journal of Neuroradiology
PG  - 682--688
VI  - 43
IP  - 5
4099  - http://www.ajnr.org/content/43/5/682.short
4100  - http://www.ajnr.org/content/43/5/682.full
SO  - Am. J. Neuroradiol.2022 May 01; 43
AB  - 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.CD31cluster of differentiation 31CPMcellularity prediction mapGBMglioblastomaIHCimmunohistochemicallyMIB-1Mindbomb Homolog 1 indexNGGnon-GBM gliomaRMSEroot mean squared errorTICgadolinium-enhanced T1WI