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Background Preoperative discrimination between nonmuscle‐invasive bladder carcinomas (NMIBC) and the muscle‐invasive ones (MIBC) is very crucial in the management of patients with bladder cancer (BC). Purpose To evaluate the discriminative performance of multiparametric MRI radiomics features for precise differentiation of NMIBC from MIBC, preoperatively. Study Type Retrospective, radiomics. Population Fifty‐four patients with postoperative pathologically proven BC lesions (24 in NMIBC and 30 in MIBC groups) were included. Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W) and multi‐b‐value diffusion‐weighted (DW) sequences. Assessment A total of 1104 radiomics features were extracted from carcinomatous regions of interest on T2W and DW images, and the apparent diffusion coefficient maps. Support vector machine with recursive feature elimination (SVM‐RFE) and synthetic minority oversampling technique (SMOTE) were used to construct an optimal discriminative model, and its performance was evaluated and compared with that of using visual diagnoses by experts. Statistical Tests Chi‐square test and Student's t‐test were applied on clinical characteristics to analyze the significant differences between patient groups. Results Of the 1104 features, an optimal subset involving 19 features was selected from T2W and DW sequences, which outperformed the other two subsets selected from T2W or DW sequence in muscle invasion discrimination. The best performance for the differentiation task was achieved by the SVM‐RFE+SMOTE classifier, with averaged sensitivity, specificity, accuracy, and area under the curve of receiver operating characteristic of 92.60%, 100%, 96.30%, and 0.9857, respectively, which outperformed the diagnostic accuracy by experts. Data Conclusion The proposed radiomics approach has potential for the accurate differentiation of muscle invasion in BC, preoperatively. The optimal feature subset selected from multiparametric MR images demonstrated better performance in identifying muscle invasiveness when compared with that from T2W sequence or DW sequence only. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1489–1498.

Background Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type Retrospective. Population Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W), multi‐b‐value diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics features were extracted from the T2W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine‐based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle‐invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics‐clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. Statistical Tests Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics‐clinical nomogram than using the radiomics or clinical model alone. Data Conclusion The proposed radiomics‐clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893–1904.

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Submucosal linear enhancement under the tumor base on DCE-MRI complements tumor stalk detection on DWI for differentiating stage T1 from stage T2 bladder urothelial carcinoma.