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

Abstract: MR-derived radiomic features have demonstrated substantial predictive utility in modeling different prognostic factors of glioblastomas and other brain cancers. However, the biological relationship underpinning these predictive models has been largely unstudied, with the generalizability of these models also called into question. Here, we examine the localized relationship between MR-derived radiomic features and histology-derived histomic features using a dataset of 16 brain cancer patients. Tile-based radiomics features were collected on T1W, post-contrast T1W, FLAIR, and DWI-derived ADC images acquired prior to patient death, with analogous histomic features collected for autopsy samples co-registered to the MRI. Features were collected for each original image, as well as a 3D wavelet decomposition of each image, resulting in 837 features per MR image and histology image. Correlative analyses were used to assess the degree of association between radiomic-histomic pairs for each MRI. The influence of several confounds were also assessed using linear mixed effect models for the normalized radiomic-histomic distance, testing for main effects of scanners from different vendors and acquisition field strength. Results as a whole were largely heterogenous, but several features demonstrated substantial associations with their histomic analogs, particularly those derived from the FLAIR and post-contrast T1W images. These most-associated features typically presented as stable across confounding factors as well. These data suggest that a subset of radiomic features are able to consistently capture texture information about the underlying tissue histology.