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RT Journal Article
SR Electronic
T1 Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 682
OP 688
DO 10.3174/ajnr.A7477
VO 43
IS 5
A1 Bobholz, S.A.
A1 Lowman, A.K.
A1 Brehler, M.
A1 Kyereme, F.
A1 Duenweg, S.R.
A1 Sherman, J.
A1 McGarry, S.D.
A1 Cochran, E.J.
A1 Connelly, J.
A1 Mueller, W.M.
A1 Agarwal, M.
A1 Banerjee, A.
A1 LaViolette, P.S.
YR 2022
UL http://www.ajnr.org/content/43/5/682.abstract
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&amp;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