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MR-derived radiomic features have demonstrated substantial predictive utilityin modeling different prognostic factors of glioblastomas and other braincancers. However, the biological relationship underpinning these predictivemodels has been largely unstudied, with the generalizability of these modelsalso called into question. Here, we examine the localized relationship betweenMR-derived radiomic features and histology-derived histomic features using adataset of 16 brain cancer patients. Tile-based radiomics features werecollected on T1W, post-contrast T1W, FLAIR, and DWI-derived ADC images acquiredprior to patient death, with analogous histomic features collected for autopsysamples co-registered to the MRI. Features were collected for each originalimage, as well as a 3D wavelet decomposition of each image, resulting in 837features per MR image and histology image. Correlative analyses were used toassess the degree of association between radiomic-histomic pairs for each MRI.The influence of several confounds were also assessed using linear mixed effectmodels for the normalized radiomic-histomic distance, testing for main effectsof scanners from different vendors and acquisition field strength. Results as awhole were largely heterogenous, but several features demonstrated substantialassociations with their histomic analogs, particularly those derived from theFLAIR and post-contrast T1W images. These most-associated features typicallypresented as stable across confounding factors as well. These data suggest thata subset of radiomic features are able to consistently capture textureinformation about the underlying tissue histology.

Radiomic features of multi-parametric MRI present stable associations with analogous histological features in brain cancer patients