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

PURPOSE

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.

EXPERIMENTAL DESIGN

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.

RESULTS

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).

CONCLUSIONS

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.