Current role of radiomics and radiogenomics in predicting oncological outcomes in bladder cancer

Abstract

Abstract Background Radiomics refers to the conversion of medical images into high-throughput, quantifiable data to analyze disease patterns, aid decision-making, and predict prognosis. Radiogenomics is an extension of radiomics and involves a combination of conventional radiomics techniques with molecular analysis in the form of genomic and transcriptomic data. In the field of bladder cancer, studies have investigated the development, implementation, and efficacy of radiomic and radiogenomic nomograms in predicting tumor grade, gene expression, and oncological outcomes, with variable results. We aimed to perform a systematic review of the current literature to investigate the development of a radiomics-based nomogram to predict oncological outcomes in bladder cancer. Materials and methods The Medline, EMBASE, and Web of Science databases were searched up to February 17, 2023. Gray literature was also searched to further identify other suitable publications. Quality assessment of the included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies 2 and Radiomics Quality Score. Results Radiogenomic nomograms generally had good performance in predicting the primary outcome across the included studies. The median area under the curve, sensitivity, and specificity across the included studies were 0.83 (0.63–0.973), 0.813, and 0.815, respectively, in the training set and 0.75 (0.702–0.838), 0.723, and 0.652, respectively, in the validation set. Conclusions Several studies have demonstrated the predictive potential of radiomic and radiogenomic models in advanced pelvic oncology. Further large-scale studies in a prospective setting are required to further validate results and allow generalized use in modern medicine.