Radiogenomic analysis of muscle-invasive bladder cancer using CT-based texture analysis.

Background: Urothelial bladder cancer exhibits marked molecular and clinical heterogeneity. While genomic and transcriptomic profiling of muscle-invasive bladder cancer (MIBC) has revealed recurrent alterations with therapeutic and prognostic relevance, limited access to molecular testing constrains clinical use. Computed tomography (CT), routinely performed for staging and surveillance, may serve as a noninvasive adjunct for tumor biology. Radiomics, the quantitative extraction of imaging features, offers a means to associate imaging phenotypes with molecular characteristics. Methods: Genomic data for The Cancer Genome Atlas were integrated with CT images from the Cancer Imaging Archive for 89 patients with biopsy-proven MIBC. An in-house radiomics pipeline extracted 488 texture metrics characterizing the brightness distribution, pixel relationships, and spatial patterns of segmented tumors. Three classifiers - Random Forest, Extreme Gradient Boosting, and Elastic Net - were trained to predict DNA mutations, tumor mutational burden (TMB), and mRNA expression. Model performance was evaluated using 10-fold cross-validation. Results: Among 15 recurrent mutations, EP300, FGFR3, and ARID1A were predicted most reliably (AUCs = 0.77, 0.76, 0.75). Models identified high-TMB tumors (AUC = 0.61), poor-prognosis transcriptomic signatures (AUC = 0.73, 0.65), expression of key cell cycle (CKDKN1A, AUC = 0.78) and apoptotic (CASP3, AUC = 0.71) genes, and discriminated the luminal infiltrated molecular subtype from other variants (AUC = 0.69). Conclusion: Our study demonstrates that CT-derived radiomics features can capture biologically and clinically relevant information in muscle-invasive bladder cancer. These findings support the potential utility of radiomics as a noninvasive, scalable adjunct to genomic profiling in MIBC.