Grading and Staging of Bladder Tumors Using Radiomics Analysis in Magnetic Resonance Imaging

Aim of this study was to evaluate the performance of MRI radiomics analysis in distinguishing low-grade (LG) versus high-grade (HG) bladder lesions and non-muscle-invasive bladder cancer (NMIBC) versus muscle-invasive bladder cancer (MIBC). We proposed a computational statistical analysis model that is standardized and reproducible, and identified predictive and prognostic models to facilitate the process of making medical decisions. Sixteen patients with bladder lesions and preoperative mpMRI were included for a total of 35 bladder lesions. Lesions were manually segmented from T2-weighted sequences. PyRadiomics software was used to extract radiomics features and a total of 120 radiomics features were obtained from each lesions. An operator-independent statistical system was adopted for the selection and reduction of the characteristics, while discriminant analysis was used for the construction of the predictive model. The performance in the discrimination between LG and HG lesions, with an AUROC of 0.84 (95% C.I. between 0.71 and 0.98), sensitivity of 65.6%, specificity of 81.5%, with p < 0.001. The performance in the discrimination between NMIBC and MIBC, with an AUROC of 0.7 (95% C.I. between 0.11 and 1), sensitivity of 100%, specificity of 86.7%, with p-value of 0.0031. Our results demonstrate the valuable contribution of radiomics analysis in improving the characterization and differentiation of bladder lesions, both in terms of differentiating LG from HG lesions and discriminating NMIBC from MIBC.