![]() In particular, high-grade primary brain tumors such as glioblastomas are associated with particularly dismal prognoses, with a mean survival rate of around 12-18 months post-diagnosis 2. We concluded that a radio-pathomic model for cellularity is able to identify regions of hypercellular tumor beyond traditional imaging signatures.īrain cancer, along with other central nervous system cancers, are the tenth leading cause of death worldwide, with an estimated 5-year survival rate of approximately 38 percent 1. The radio-pathomic model was able to accurately predict cellularity in the test set (RMSE = 1015 cells/mm2) and identified regions of hypercellularity beyond the contrast enhancing region. Single image analyses found subtle associations between image intensity and cellularity, with a less pronounced relationship within GBM patients. An ensemble learner was trained to predict cellularity using 5 by 5 voxel tiles from each image, employing a 2/3-1/3 train-test split for validation. Mixed effect models were used to assess the relationship between single image intensity and cellularity for each image. In-house software was used to align tissue samples to the FLAIR image via manually defined control points. Pre- and post-gadolinium contrast T1-weighted images (T1, T1C), T2 fluid-attenuated inversion recovery (FLAIR) images, and apparent diffusion coefficient (ADC) images calculated from diffusion imaging were collected from each patients’ final acquisition prior to death. Tissue samples were processed, stained for hematoxylin and eosin (HE) and digitized for nuclei segmentation and cell density calculation. This study used 93 samples collected at autopsy from 44 brain cancer patients. ![]() Therefore, this study used autopsy tissue samples aligned to clinical MRIs in order to quantify the relationship between intensity values and cellularity, as well as to develop a radio-pathomic model to predict cellularity using MRI data. Current MRI signatures of brain cancer often fail to identify regions of hypercellularity beyond the contrast enhancing region. ![]()
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