Medical College of Wisconsin
CTSICores SearchResearch InformaticsREDCap

Radiomic Features of Multiparametric MRI Present Stable Associations With Analogous Histological Features in Patients With Brain Cancer. Tomography 2020 Jun;6(2):160-169



Pubmed ID


Pubmed Central ID




Scopus ID

2-s2.0-85086686895 (requires institutional sign-in at Scopus site)   13 Citations


Magnetic resonance (MR)-derived radiomic features have shown substantial predictive utility in modeling different prognostic factors of glioblastoma and other brain cancers. However, the biological relationship underpinning these predictive models has been largely unstudied, and the generalizability of these models had been called into question. Here, we examine the localized relationship between MR-derived radiomic features and histology-derived "histomic" features using a data set of 16 patients with brain cancer. Tile-based radiomic features were collected on T1, post-contrast T1, FLAIR, and diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC) images acquired before patient death, with analogous histomic features collected for autopsy samples coregistered to the magnetic resonance imaging. Features were collected for each original image, as well as a 3D wavelet decomposition of each image, resulting in 837 features per MR and histology image. Correlative analyses were used to assess the degree of association between radiomic-histomic pairs for each magnetic resonance imaging. The influence of several confounds was also assessed using linear mixed-effect models for the normalized radiomic-histomic distance, testing for main effects of different acquisition field strengths. Results as a whole were largely heterogeneous, but several features showed substantial associations with their histomic analogs, particularly those derived from the FLAIR and postcontrast T1W images. These features with the strongest association typically presented as stable across field strengths as well. These data suggest that a subset of radiomic features can consistently capture texture information on underlying tissue histology.

Author List

Bobholz SA, Lowman AK, Barrington A, Brehler M, McGarry S, Cochran EJ, Connelly J, Mueller WM, Agarwal M, O'Neill D, Nencka AS, Banerjee A, LaViolette PS


Mohit Agarwal MD Assistant Professor in the Radiology department at Medical College of Wisconsin
Anjishnu Banerjee PhD Associate Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Jennifer M. Connelly MD Professor in the Neurology department at Medical College of Wisconsin
Peter LaViolette PhD Associate Professor in the Radiology department at Medical College of Wisconsin
Wade M. Mueller MD Professor in the Neurosurgery department at Medical College of Wisconsin
Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged, 80 and over
Brain Neoplasms
Diffusion Magnetic Resonance Imaging
Magnetic Resonance Imaging
Middle Aged