Comprehensive Summary
Defining tumor heterogeneity of bladder cancer continues to pose a clinical challenge for monitoring and prognosis parameters. This study investigates the interpretation of genetic signatures via radiomics to provide insight into prognostic phenotypes. To interpret radiomic features regarding the select 22 immune cell subsets within the tumor, CIBERSORT deconvolution algorithm was employed. Furthermore, to investigate the biological functions of radiomics-associated genes, GO and KEGG enrichment analyses were conducted. Overall, this model developed by Zou et al proved to be highly accurate with regard to the prediction of patient survival outcomes (AUC = 0.861).
Outcomes and Implications
With current prognosis for advanced bladder cancer remaining poor, furthering the effectiveness of immunotherapy is critical. The use of more robust predictive measures is essential for the determination of treatment methods. Biomarker heterogeneity poses difficulties in immune checkpoint inhibitor therapy and mitigation of this via radiomics may lead to better patient outcomes. This study’s determination of clinically interpretable biomarkers is a key step forward in advancing treatment for those with advanced bladder cancer.