American Association for Cancer Research
10780432ccr173420-sup-192500_3_supp_4893115_pbtnlq.pdf (43.09 MB)

Supplementary Figure 7 from A Coclinical Radiogenomic Validation Study: Conserved Magnetic Resonance Radiomic Appearance of Periostin-Expressing Glioblastoma in Patients and Xenograft Models

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journal contribution
posted on 2023-03-31, 20:47 authored by Pascal O. Zinn, Sanjay K. Singh, Aikaterini Kotrotsou, Islam Hassan, Ginu Thomas, Markus M. Luedi, Ahmed Elakkad, Nabil Elshafeey, Tagwa Idris, Jennifer Mosley, Joy Gumin, Gregory N. Fuller, John F. de Groot, Veera Baladandayuthapani, Erik P. Sulman, Ashok J. Kumar, Raymond Sawaya, Frederick F. Lang, David Piwnica-Worms, Rivka R. Colen

Supplementary Figure 7


Radiological Society of North America


Neurosurgery Research and Education Foundation

MD Anderson Cancer Center




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 (n = 93) and orthotopic xenografts (OX; n = 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 (POSTN) expression levels. RNA and protein levels confirmed RNAi-mediated POSTN 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 POSTN 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. POSTN expression status was not associated with qualitative or volumetric MRI parameters. Radiomic features significantly predicted POSTN 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 POSTN expression levels (AUC: 93.36%; sensitivity/specificity: 82.61%/95.74%; P = 02.021E−15). We determined causality between radiomic texture features and POSTN 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.

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