Samuel Bobholz1, Allison Lowman2, Alexander Barrington3, Michael Brehler2, Sean McGarry1, Jennifer Connelly4, Elizabeth Cochran5, Anjishnu Banerjee6, and Peter LaViolette2,3
1Biophysics, Medical College of Wisconsin, Wauwatosa, WI, United States, 2Radiology, Medical College of Wisconsin, Wauwatosa, WI, United States, 3Biomedical Engineering, Medical College of Wisconsin, Wauwatosa, WI, United States, 4Neurology, Medical College of Wisconsin, Wauwatosa, WI, United States, 5Pathology, Medical College of Wisconsin, Wauwatosa, WI, United States, 6Biostatistics, Medical College of Wisconsin, Wauwatosa, WI, United States
Synopsis
This study
sought to provide a biological basis of radiomics-based analyses by assessing
the relationship between MR features and analogous histomic features of the
underlying tissue using coregistered histology samples taken at autopsy from
brain cancer patients. Several radiomic
features demonstrated substantial mutual information with their histomic analogs,
with first order features showing the strongest associations. These histomic-preserving features were shown
to be stable across potential confounds such as differences in scanner vender
and acquisition field strength. These
findings suggest that MR radiomic features reflect information about the
texture of the underlying tissue.
Introduction
MR-derived
radiomic features have demonstrated substantial predictive utility in modeling
different prognostic factors of glioblastomas and other brain cancers [1–4].
However, the biological relationship underpinning these predictive
models has been largely unstudied, with the generalizability of these models
also called into question [5–7].
An essential step towards advancing this technique are addressing these
concerns, but studies attempting to tie radiomic features to biological
information have thus far been limited to biopsy cores, which are not able to
provide information outside of the MR-defined tumor boundary [8,9].
This study used autopsy tissue samples
coregistered to the MRI to address the biological underpinnings of radiomic
features across a variety of tissue types found in brain cancer. Here, we examine the localized relationship
between MR-derived radiomic features and histology-derived “histomic” features
in terms of mutual information.
Furthermore, we assessed the influence of several confounding factors
such as MR scanner vendor and field strength on the radiomic-histomic (RH)
relationship to address concerns regarding generalizability.Methods
Sixteen
patients with a pathologically confirmed brain cancer were enrolled in this
study (Figure 1). A diagrammatic
representation of the data collection process is provided in Figure 2. Forty-five tissue samples were collected at
autopsy, and clinical imaging was collected from the last session prior to
death (including a T1-weighted image (T1), T1 with injection of gadolinium
contrast agent (T1+C), a T2-weighted fluid attenuated inversion recovery image
(FLAIR), and an apparent diffusion coefficient image (ADC) calculated from
diffusion weighted imaging). Samples
were acquired using 3-D printed slicing jigs based on the clinical imaging in
order to slice the brain in line with the slices of the MRI. Following hematoxylin and eosin (HE) tissue
staining, full slides from the samples were then digitized at 10X magnification
using a sliding stage microscope.
In-house
custom software was used to coregister the histology to the MRI. Manually defined control points were used to
direct a nonlinear transformation of the histology slide to the FLAIR
image. Regions of interest (ROIs) were
then defined based on quality assessment for both the MRI and the histology
across each slide, with tile masks generated across the ROIs using a 10 voxel
by 10 voxel frame with single voxel stride.
Radiomic
features were calculated across each tile mask for the T1, T1+C, FLAIR, and ADC
image using Pyradiomics v2.1.0. Features
were extracted on each normalized image, as well as the eight images generated
from a 3-D wavelet decomposition (3DWD) of each image. Extracted features included first order (FO)
features, gray-level co-occurrence matrix (GLCM), gray-level dependence matrix
(GLDM) features, gray-level run level matrix (GLRLM) features, gray-level size
zone matrix (GLSZM) features, and neighboring gray tone dependence matrix
(NGTDM) features. These same features
were then calculated across each tile on the grayscale histology image, resulting
in 837 radiomic features for each of the four MRI and 837 histomic features for
the histology. Results
The RH
relationship between analogous features was calculated using a normalized
kernel-based estimation of shared information between the two variables. The mutual information (MI) between each RH
pair was divided by the entropy of the histomic feature, giving the proportion
of histomic information explained by the radiomic feature, presented as both a
heatmap to compare individual features (Figure 3) and by ranked
associations to compare across images (Figure 4). FO features tended to possess the strongest
RH relationships, with FLAIR and T1+C images showing more features with strong
RH relationships.
The
influence of scanner vendor and field strength was assessed by calculating the
normalized mutual information between each radiomic feature and the confounding
factor. Figure 5 shows the
confound MI plotted against the RH MI, revealing that the RH pairs with the
strongest relationships tended to have minor MI with scanner vendor and field
strength when compared to their radiomic-histomic MI.Discussion
This study sought to explore the histological
underpinnings of tile-based MP-MRI radiomic features in GBM and other brain
cancer patients. Several radiomic
features demonstrated substantial associations with their histomic analogs,
suggesting that these aspects of the MRI directly characterize the same
features of the underlying tissue histology.
Furthermore, these associated features are shown to be relatively robust
across different confounding factors, such as MR scanner vendor and acquisition
field strength.
The FLAIR image tends to provide the most robust
RH relationships, with gadolinium contrast enhancement from the T1+C image
providing substantial RH improvement over the standard T1. The distinction between strongly associated
FO features and the lesser-associated higher order features (GLCM, GLDM, GLRLM,
GLSZM, and NGTDM) tended to see increased divergence across the 3DWD, with FO
features of the 3DWD images seeing stable associations and higher order
features of the 3DWD images seeing both decreased and increased
associations. Field strength appeared to
influence the most associated radiomic features more than vendor differences and
should be taken into consideration in the interpretation of radiomics-based
modelling for localized histological information. Conclusion
These findings, taken as a whole, provide an information-based
characterization of the texture relationship between MR images and tissue
histology and begin to provide a neurobiological context for radiomics-based
modelling in brain cancer.Acknowledgements
We
would like to thank our patients for their participation in this study and our
funding sources: American Brain Tumor Association DG14004, R01CA218144,
R01CA218144-02S1, and R21CA23189201.References
1 McGarry SD, Hurrell SL, Kaczmarowski AL,
et al. Magnetic Resonance Imaging-Based Radiomic Profiles Predict
Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomogr
(Ann Arbor, Mich) 2016;2:223–8. doi:10.18383/j.tom.2016.00250
2 Sanghani P, Ang BT, King NKK, et al.
Overall survival prediction in glioblastoma multiforme patients from
volumetric, shape and texture features
using machine learning. Surg Oncol 2018;27:709–14.
doi:10.1016/j.suronc.2018.09.002
3 Gillies RJ, Kinahan PE, Hricak H.
Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278:563–77.
doi:10.1148/radiol.2015151169
4 Kniep HC, Madesta F, Schneider T, et
al. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type.
Radiology 2018;290:479–87. doi:10.1148/radiol.2018180946
5 Um H, Tixier F, Bermudez D, et al.
Impact of image preprocessing on the scanner dependence of multi-parametric MRI
radiomic features and covariate shift in multi-institutional glioblastoma
datasets. Phys Med Biol 2019;64:165011.
doi:10.1088/1361-6560/ab2f44
6 Chirra P, Leo P, Yim M, et al.
Multisite evaluation of radiomic feature reproducibility and discriminability
for identifying peripheral zone prostate tumors on MRI. J Med imaging
(Bellingham, Wash) 2019;6:24502. doi:10.1117/1.JMI.6.2.024502
7 Kumar V, Gu Y, Basu S, et al.
Radiomics: the process and the challenges. Magn Reson Imaging 2012;30:1234–48.
doi:10.1016/j.mri.2012.06.010
8 Prah MA, Al-Gizawiy MM, Mueller WM, et
al. Spatial discrimination of glioblastoma and treatment effect with
histologically-validated perfusion and diffusion magnetic resonance imaging
metrics. J Neurooncol 2018;136:13–21.
doi:10.1007/s11060-017-2617-3
9 Gates EDH, Lin JS, Weinberg JS, et
al. Guiding the first biopsy in glioma patients using estimated Ki-67 maps
derived from MRI: conventional versus advanced imaging. Neuro Oncol
2019;21:527–36. doi:10.1093/neuonc/noz004