A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

Abstract

Radiomics is the extraction of multidimensional imaging features, which when correlated with genomics, is termed radiogenomics. However, radiogenomic biological validation is not sufficiently described in the literature. We seek to establish causality between differential gene expression status and MRI-extracted radiomic-features in glioblastoma. Radiogenomic predictions and validation were done using the Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data glioblastoma patients ( = 93) and orthotopic xenografts (OX; = 40). Tumor phenotypes were segmented, and radiomic-features extracted using the developed radiome-sequencing pipeline. Patients and animals were dichotomized on the basis of Periostin ( expression levels. RNA and protein levels confirmed RNAi-mediated knockdown in OX. Total RNA of tumor cells isolated from mouse brains (knockdown and control) was used for microarray-based expression profiling. Radiomic-features were utilized to predict expression status in patient, mouse, and interspecies. Our robust pipeline consists of segmentation, radiomic-feature extraction, feature normalization/selection, and predictive modeling. The combination of skull stripping, brain-tissue focused normalization, and patient-specific normalization are unique to this study, providing comparable cross-platform, cross-institution radiomic features. expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted expression status in patients (AUC: 76.56%; sensitivity/specificity: 73.91/78.26%) and OX (AUC: 92.26%; sensitivity/specificity: 92.86%/91.67%). Furthermore, radiomic features in OX were significantly associated with patients with similar expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; = 02.021E-15). We determined causality between radiomic texture features and expression levels in a preclinical model with clinical validation. Our biologically validated radiomic pipeline also showed the potential application for human-mouse matched coclinical trials.

Implementing Systems Modelling and Molecular Imaging to Predict the Efficacy of BCL-2 Inhibition in Colorectal Cancer Patient-Derived Xenograft Models

“…Indeed, the translational relevance of pre-clinical radiomic ‘discovery’ studies in animal models is now becoming evident. For example, Zinn et al recently employed radiomic analyses on glioblastoma magnetic resonance data sets from mice and humans, and showed that selected imaging features are conserved across species [ 44 ]. In the current context, future work employing CT scan data from large scale population-based CRC PDX trials is now warranted.…”

Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics

“…The variability generated by different voxel sizes can also be reduced by spatial resampling 9 , 11 , 12 . Moreover, brain extraction is mandatory to remove the skull regions that generate the most important variations in intensities and to define the region in which intensities should be considered before any image intensity normalization 13 , 14 . However, even though these three types of pre-processing of brain MRI are widely accepted by the community, there is no consensus within radiomics studies regarding the applied image normalization method (Table 1 ).…”

Classification of the glioma grading using radiomics analysis

“…Other studies have predicted prognosis using features obtained from functional imaging ( Ryu et al, 2014 ; Lee et al, 2016 ). Recently, radiomics has been combined with genomics to leverage two distinct types of information to better study various tumor types ( Gutman et al, 2015 ; Li et al, 2016 ; Beig et al, 2018 ; Zinn et al, 2018 ). The new approach is referred to as radiogenomics and has to potential to reveal novel findings combining two distinct high dimensional information of gene and imaging information.…”