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Tomography
Volume 6
Issue 2
10.18383/j.tom.2019.00029
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Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
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Article

Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer

by
Samuel A. Bobholz
1,
Allison K. Lowman
2,
Alexander Barrington
3,
Michael Brehler
2,
Sean McGarry
1,
Elizabeth J. Cochran
4,
Jennifer Connelly
5,
Wade M. Mueller
6,
Mohit Agarwal
2,
Darren O'Neill
2,
Andrew S. Nencka
2,
Anjishnu Banerjee
7 and
Peter S. LaViolette
2,3,*
1
Departments of Biophysics, Milwaukee, WI, USA
2
Departments of Radiology, College of Wisconsin, 8701 Watertown Plank Rd. Milwaukee, WI, USA
3
Departments of Biomedical Engineering, College of Wisconsin, 8701 Watertown Plank Rd. Milwaukee, WI, USA
4
Departments of Pathology, Milwaukee, WI, USA
5
Departments of Neurology, Milwaukee, WI, USA
6
Departments of Neurosurgery, Milwaukee, WI, USA
7
Departments of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 160-169; https://doi.org/10.18383/j.tom.2019.00029
Submission received: 10 March 2020 / Revised: 12 April 2020 / Accepted: 12 May 2020 / Published: 1 June 2020
Versions Notes

Abstract

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.
Keywords: radiomics; glioma; MRI; autopsy; histology radiomics; glioma; MRI; autopsy; histology

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MDPI and ACS Style

Bobholz, S.A.; Lowman, A.K.; Barrington, A.; Brehler, M.; McGarry, S.; Cochran, E.J.; Connelly, J.; Mueller, W.M.; Agarwal, M.; O'Neill, D.; et al. Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer. Tomography 2020, 6, 160-169. https://doi.org/10.18383/j.tom.2019.00029

AMA Style

Bobholz SA, Lowman AK, Barrington A, Brehler M, McGarry S, Cochran EJ, Connelly J, Mueller WM, Agarwal M, O'Neill D, et al. Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer. Tomography. 2020; 6(2):160-169. https://doi.org/10.18383/j.tom.2019.00029

Chicago/Turabian Style

Bobholz, Samuel A., Allison K. Lowman, Alexander Barrington, Michael Brehler, Sean McGarry, Elizabeth J. Cochran, Jennifer Connelly, Wade M. Mueller, Mohit Agarwal, Darren O'Neill, and et al. 2020. "Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer" Tomography 6, no. 2: 160-169. https://doi.org/10.18383/j.tom.2019.00029

APA Style

Bobholz, S. A., Lowman, A. K., Barrington, A., Brehler, M., McGarry, S., Cochran, E. J., Connelly, J., Mueller, W. M., Agarwal, M., O'Neill, D., Nencka, A. S., Banerjee, A., & LaViolette, P. S. (2020). Radiomic Features of Multiparametric MRI Present Stable Associations with Analogous Histological Features in Patients with Brain Cancer. Tomography, 6(2), 160-169. https://doi.org/10.18383/j.tom.2019.00029

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