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.
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.
Authors: Quinn T Ostrom; Luc Bauchet; Faith G Davis; Isabelle Deltour; James L Fisher; Chelsea Eastman Langer; Melike Pekmezci; Judith A Schwartzbaum; Michelle C Turner; Kyle M Walsh; Margaret R Wrensch; Jill S Barnholtz-Sloan Journal: Neuro Oncol Date: 2014-07 Impact factor: 12.300
Authors: Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith Journal: Neuroimage Date: 2011-09-16 Impact factor: 6.556
Authors: Sean D McGarry; Sarah L Hurrell; Kenneth A Iczkowski; William Hall; Amy L Kaczmarowski; Anjishnu Banerjee; Tucker Keuter; Kenneth Jacobsohn; John D Bukowy; Marja T Nevalainen; Mark D Hohenwalter; William A See; Peter S LaViolette Journal: Int J Radiat Oncol Biol Phys Date: 2018-04-24 Impact factor: 8.013
Authors: Samuel A Bobholz; Allison K Lowman; Alexander Barrington; Michael Brehler; Sean McGarry; Elizabeth J Cochran; Jennifer Connelly; Wade M Mueller; Mohit Agarwal; Darren O'Neill; Andrew S Nencka; Anjishnu Banerjee; Peter S LaViolette Journal: Tomography Date: 2020-06
Authors: E D H Gates; D Suki; A Celaya; J S Weinberg; S S Prabhu; R Sawaya; J T Huse; J P Long; D Fuentes; D Schellingerhout Journal: AJNR Am J Neuroradiol Date: 2022-09-15 Impact factor: 4.966