Comprehensive radiogenomics analysis of qualitative and quantitative features of cross-sectional imaging in the TCGA project in MIBC.

Abstract

482 Background: Quantitative imaging descriptors derived from CT and MRI can be integrated with genomic data that may be used as non-invasive prognostic or predictive biomarkers. We report an integrated radiogenomics project designed to develop subjective and objective parameters extracted from cross-sectional imaging of MIBC from studies archived in the TCIA and linked to the TCGA project. Methods: We reported comprehensive integrated genomic analysis of 412 tumors (Cell 2017). 7 of 33 tissue source sites submitted CT scans to the TCIA (n=106). We developed 17 features describing tumor size/location, metastases sites, and tumor morphology; 9 GU radiologists reviewed the scans in a blinded manner. EH analyzed the data independent of the radiologists. We computed kappa statistics for categorical features and coverage probabilities for quantitative features (Lin et al 2002). The tumor was segmented on an axial image and the segmented image analyzed using a radiomics panel (radiomicslab.usc.edu). Associations between individual features and subtypes were assessed (Fisher’s Exact Test) for categorical features and Kruskal-Wallis Test for quantitative features. Results: Substantial agreement (k≥ 0.6) was observed in 4 features: tumor laterality, tumor within bladder diverticulum, right and left UVJ involvement and hydroureter. We observed weak agreement (95% CI <0.4) for bladder neck, posterior bladder, dome, and trigone involvement, tumor margin, internal architecture, radiographic stage, left upper tract involvement, and metastases. The coverage probability for lesion size was 0.59 (0.544-0.638) (Figure). Tumor morphology was associated with microRNA cluster, with diffuse wall thickening having a higher tendency toward Clusters 3 and 4 (p < .001). Radiomic analysis identified statistically significant associations of mutations in FGFR3, CREBBP, CASP8 and EP300 with multiple radiomic features. Conclusions: This blinded comprehensive assessment of features extracted from CT images highlights many of the ongoing challenges in staging patients with MIBC. Preliminary analysis shows promise in analyzing associations between radiomic features and mutations.