Objective: To develop and evaluate the performance of a magnetic resonance imaging (MRI)-based radiomics nomogram for prediction of response of patients with muscle-invasive bladder cancer (MIBC) to neoadjuvant chemotherapy (NAC).Methods: A total of 70 patients with clinical T2-4aN0M0 MIBC were enrolled in this retrospective study. For each patient, 1316 radiomics features were extracted from T2-weighted images (T2WI), diffusion-weighted images (DWI), and apparent diffusion coefficient (ADC) maps. The variance threshold algorithm and the Student’s t-test or the Mann–Whitney U test were applied to select optimal features. Multivariate logistic regression analysis was used to eliminate irrelevant features, and the retained features were incorporated into the final single-modality radiomics model. Combined radiomic models were generated by combining single-modality radiomics models. A radiomics nomogram, incorporating radiomics signatures and independent clinical risk factors, was developed to determine whether the performance of the model in predicting tumor response to NAC could be further improved.Results: Based on pathological T stage post-surgery, 36 (51%) patients were classified as good responders (GR) and 34 (49%) patients as non-good responders (non-GR). In addition, 3 single-modality radiomics models and 4 combined radiomics models were established. Among all radiomics models, the combined radiomics model based on T2WI_Score, DWI_Score, and ADC_Score yielded the highest area under the receiver operating characteristics curve (AUC) (0.967, 95% confidence interval (CI): 0.930–0.995). A radiomics nomogram, integrating the clinical T stage and 3 single-modality radiomics models, yielded a higher AUC (0.973, 95%CI: 0.934–0.998) than other combined radiomics models.Conclusion: The proposed MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for the quantitatively prediction of tumor response to NAC in patients with MIBC.

Objectives: To investigate whether reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with deep learning reconstruction (DLR) can improve the accuracy of evaluating muscle invasion using VI-RADS. Methods: Eighty-six bladder cancer participants who were evaluated by conventional full field-of-view (fFOV) DWI, standard rFOV (rFOV_STA) DWI, and fast rFOV with DLR (rFOV_DLR) DWI were included in this prospective study. Tumors were categorized according to the vesical imaging reporting and data system (VI-RADS). Qualitative image quality scoring, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and ADC value were evaluated. Friedman test with post hoc test revealed the difference across the three DWIs. Receiver operating characteristic analysis was performed to calculate the areas under the curve (AUCs). Results: The AUC of the rFOV_STA DWI and rFOV_DLR DWI were higher than that of fFOV DWI. rFOV_DLR DWI reduced the acquisition time from 5:02 min to 3:25 min, and showed higher scores in overall image quality with higher CNR and SNR, compared to rFOV_STA DWI (p < 0.05). The mean ADC of all cases of rFOV_STA DWI and rFOV_DLR DWI was significantly lower than that of fFOV DWI (all p < 0.05). There was no difference in mean ADC value and the AUC for evaluating muscle invasion between rFOV_STA DWI and rFOV_DLR DWI (p > 0.05). Conclusions: rFOV DWI with DLR can improve the diagnostic accuracy of fFOV DWI for evaluating muscle invasion. Applying DLR to rFOV DWI reduced the acquisition time and improved overall image quality while maintaining ADC value and diagnostic accuracy. Critical relevance statement: The diagnostic performance and image quality of full field-of-view DWI, reduced field-of-view (rFOV) DWI with and without DLR were compared. DLR would benefit the wide clinical application of rFOV DWI by reducing the acquisition time and improving the image quality. Key Points: Deep learning reconstruction (DLR) can reduce scan time and improve image quality. Reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) with DLR showed better diagnostic performances than full field-of-view DWI. There was no difference of diagnostic accuracy between rFOV DWI with DLR and standard rFOV DWI. Graphical