Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors

Objectives To develop and validate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. Methods A primary cohort of 70 patients (31 high-grade BCa and 39 low-grade BCa) with BCa were retrospectively enrolled. Three sets of radiomics features were separately extracted from tumor volumes on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. Two sets of multimodal features were separately generated by the maxout and concatenation of the above mentioned single-modality features. Each feature set was subjected to a two-sample t test and the least absolute shrinkage and selection operator (LASSO) algorithm for feature selection. Multivariable logistic regression (LR) analysis was used to obtain five corresponding radiomics models. The diagnostic abilities of the radiomics models were evaluated using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. Validation was performed on a time-independent cohort containing 30 consecutive patients. Results The areas under the ROC curves (AUCs) of single-modality T2WI, DWI, and ADC models in the training cohort were 0.7933 (95% confidence interval [CI] 0.7471-0.8396), 0.8083 (95% CI 0.7565-0.8601), and 0.8350 (95% CI 0.7924-0.8776), respectively. Both multimodality models achieved higher AUCs (maxout 0.9233, 95% CI 0.9001-0.9466; concatenation 0.9233, 95% CI 0.9001-0.9466) than single-modality models. The AUCs of the maxout and concatenation models in the validation cohort were 0.9186 and 0.9276, respectively. Conclusions The MRI-based multiparametric radiomics approach has the potential to be used as a noninvasive imaging tool for preoperative grading of BCa tumors. Multicenter validation is needed to acquire high-level evidence for its clinical application.