Clinical validity of non-contrast-enhanced VI-RADS: prospective study using 3-T MRI with high-gradient magnetic field

European Association of Urology Guidelines on Muscle-invasive and Metastatic Bladder Cancer: Summary of the 2025 Guidelines

BACKGROUND AND OBJECTIVE

This publication represents a summary of the updated 2025 European Association of Urology (EAU) guidelines for muscle-invasive and metastatic bladder cancer (MMIBC). The aim is to provide practical recommendations on the clinical management of MMIBC with a focus on diagnosis, treatment, and follow-up.

METHODS

For the 2025 guidelines, new and relevant evidence was identified, collated, and appraised via a structured assessment of the literature. Databases searched included Medline, EMBASE, and the Cochrane Libraries. Recommendations within the guidelines were developed by the panel to prioritise clinically important care decisions. The strength of each recommendation was determined according to a balance between desirable and undesirable consequences of alternative management strategies, the quality of the evidence (including the certainty of estimates), and the nature and variability of patient values and preferences.

KEY FINDINGS AND LIMITATIONS

The key recommendations emphasise the importance of thorough diagnosis, treatment, and follow-up for patients with MMIBC. The guidelines stress the importance of a multidisciplinary approach to the treatment of MMIBC patients and the importance of shared decision-making with patients. The key changes in the 2025 muscle-invasive bladder cancer (MIBC) guidelines include the following: a new recommendation for the use of susceptible FGFR3 alterations to select patients with unresectable or metastatic urothelial carcinoma for treatment with erdafitinib; significant adaption and update of the recommendations for pre- and postoperative radiotherapy and sexual organ-preserving techniques in women; new recommendation related to radical cystectomy and extent of lymph node dissection based on the results of the SWOG trial; recommendation related to hospital volume; new recommendations for salvage cystectomy after trimodality therapy and for the management of all patients who are candidates for trimodality bladder-preserving treatment in a multidisciplinary team setting using a shared decision-making process; significant adaption and update to the recommendation for adjuvant nivolumab in selected patients with pT3/4 and/or pN+ disease not eligible for, or who declined, adjuvant cisplatin-based chemotherapy; and addition of a new recommendation for metastatic disease regarding the antibody-drug conjugate trastuzumab deruxtecan in case of HER2 overexpression; in addition, removal of the recommendations on sacituzumab govitecan as the manufacturer has withdrawn the US Food and Drug Administration approval for this product; update of the follow-up of MIBC; and full update of the management algorithms of MIBC.

CONCLUSIONS AND CLINICAL IMPLICATIONS

This overview of the 2025 EAU guidelines offers valuable insights into risk factors, diagnosis, classification, treatment, and follow-up of MIBC patients and is designed for effective integration into clinical practice.

Multiparametric MRI for Bladder Cancer: A Practical Approach to the Clinical Application of VI-RADS

Multiparametric MRI of the bladder is highly accurate in the detection and local staging of bladder cancer. The Vesical Imaging Reporting and Data System (VI-RADS) scoring system has improved the diagnostic accuracy, reproducibility, and interpretability of bladder MRI in the assessment of the invasion of the muscularis propria. There are several technical details concerning bladder MRI that need to be strictly applied to obtain the highest possible diagnostic potential from the MRI. In addition, image evaluation, accurate interpretation, and reporting need to be standardized to optimize diagnostic accuracy and interreader agreement. This review describes the patient population for bladder MRI and discusses, with a practical approach, the correct acquisition protocol for optimal image quality using VI-RADS with reporting tips, pitfalls, and challenges for its clinical application. This review also discusses the latest evidence, clinical implications, current controversies, and future challenges, including gaps in knowledge, of the VI-RADS scoring system.

Comparative diagnostic performance of VI-RADS based on biparametric and multiparametric MRI in predicting muscle invasion in bladder cancer

BACKGROUND

Vesical Imaging-Reporting and Data System (VI-RADS) based on multiparametric magnetic resonance imaging (mp-MRI) performed well in diagnosing muscle-invasive bladder cancer (MIBC). However, certain cases may present challenges in determining the final VI-RADS score using only T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, especially in the absence of dynamic contrast-enhanced (DCE) imaging. This study aims to evaluates whether biparametric MRI (bp-MRI) achieve comparable diagnostic performance to mp-MRI for predicting MIBC and seeks to identify the most suitable bp-MRI criterion by establishing four specific conditions based on T2WI and DWI.

METHODS

A retrospective analysis was conducted on 447 patients who underwent preoperative mp-MRI. Images were evaluated according to the VI-RADS protocol by three independent readers. In the bp-DWI and bp-DWI Plus criteria, DWI was the primary sequence used for lesion assessment, while T2WI was the primary sequence for bp-T2WI and bp-T2WI Plus criteria. The Plus criteria (bp-DWI Plus and bp-T2WI Plus) assigned a final VI-RADS score of 4 when both T2WI and DWI scores were 3. The gold standard for diagnosis was histopathological evaluation after surgery. Diagnostic performance was evaluated by comparing the area under the curve (AUC), sensitivity, specificity, and inter-reader agreement using Cohen's kappa analysis.

RESULTS

Among 447 patients, 304 confirmed as NMIBC and 143 as MIBC. The kappa values were 0.876, 0.873, 0.873, 0.642, and 0.642 for mp-MRI, bp-DWI, bp-DWI Plus, bp-T2WI, and bp-T2WI Plus, respectively, when VI-RADS cutoff > 2. Similarly, when cutoff > 3, the kappa values were 0.848, 0.811, 0.873, 0.811, and 0.873. No significant differences were observed between mp-MRI and bp-DWI (AUC: 0.916 vs. 0.912, p = 0.498), but mp-MRI and bp-DWI had higher AUCs compared to bp-DWI Plus, bp-T2WI, and bp-T2WI Plus.

CONCLUSIONS

Both mp-MRI and bp-DWI demonstrate excellent performance in predicting MIBC, with bp-DWI being an alternative to mp-MRI.

TRIAL REGISTRATION

retrospectively.

Evaluating biparametric MRI for diagnosing muscle-invasive bladder cancer with variant urothelial histology: a multicenter study

BACKGROUND

Vesical Imaging-Reporting and Data System (VI-RADS) based on multiparametric MRI (mp-MRI) demonstrated excellent performance in diagnosing muscle-invasive bladder cancer (MIBC) in cases of pure urothelial carcinoma. However, the performance of VI-RADS based on mp-MRI and biparametric MRI (bp-MRI) in diagnosing urothelial carcinoma with variant histology (VUC) remains unknown.

PURPOSE

To evaluate the applicability of VI-RADS using mp-MRI and bp-MRI in diagnosing MIBC in patients with VUC.

METHODS

A retrospective analysis was conducted on 86 patients with VUC from different medical centers. Each patient underwent mp-MRI, with images evaluated using VI-RADS scores. The acquired images were divided into two groups: the mp-MRI group and the bp-MRI group. The mp-MRI group was evaluated according to the VI-RADS protocol. For the bp-MRI group, two VI-RADS scoring criteria were established: bp-DWI, primarily driven by DWI, and bp-T2WI, primarily driven by T2WI. The bp-MRI group was evaluated based on these two criteria. Inter-reader agreement performance was evaluated using Kappa analysis. The evaluation methods were evaluated by receiver operating characteristic curve. Comparison of the area under the curve (AUC) was performed used DeLong's test. A p-value < 0.05 was considered significant.

RESULTS

Inter-reader agreement was high across all evaluation methods, with Kappa values exceeding 0.80. The AUCs for mp-MRI, bp-DWI, and bp-T2WI were 0.934, 0.885, and 0.932, respectively. The diagnostic performance of bp-T2WI was comparable with that of mp-MRI (p = 0.682) and significantly higher than bp-DWI (p = 0.007). Both mp-MRI and bp-T2WI demonstrated high sensitivity and specificity.

CONCLUSION

VI-RADS based on mp-MRI demonstrates good diagnostic performance for MIBC in VUC patients. bp-T2WI may provide comparable diagnostic performance to mp-MRI.

Multiparametric MRI and artificial intelligence in predicting and monitoring treatment response in bladder cancer

Bladder cancer is the 10th most common and 13th most deadly cancer worldwide, with urothelial carcinomas being the most common type. Distinguishing between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is essential due to significant differences in management and prognosis. MRI may play an important diagnostic role in this setting. The Vesical Imaging Reporting and Data System (VI-RADS), a multiparametric MRI (mpMRI)-based consensus reporting platform, allows for standardized preoperative muscle invasion assessment in BCa with proven diagnostic accuracy. However, post-treatment assessment using VI-RADS is challenging because of anatomical changes, especially in the interpretation of the muscle layer. MRI techniques that provide tumor tissue physiological information, including diffusion-weighted (DW)- and dynamic contrast-enhanced (DCE)-MRI, combined with derived quantitative imaging biomarkers (QIBs), may potentially overcome the limitations of BCa evaluation when predominantly focusing on anatomic changes at MRI, particularly in the therapy response setting. Delta-radiomics, which encompasses the assessment of changes (Δ) in image features extracted from mpMRI data, has the potential to monitor treatment response. In comparison to the current Response Evaluation Criteria in Solid Tumors (RECIST), QIBs and mpMRI-based radiomics, in combination with artificial intelligence (AI)-based image analysis, may potentially allow for earlier identification of therapy-induced tumor changes. This review provides an update on the potential of QIBs and mpMRI-based radiomics and discusses the future applications of AI in BCa management, particularly in assessing treatment response. CRITICAL RELEVANCE STATEMENT: Incorporating mpMRI-based quantitative imaging biomarkers, radiomics, and artificial intelligence into bladder cancer management has the potential to enhance treatment response assessment and prognosis prediction. KEY POINTS: Quantitative imaging biomarkers (QIBs) from mpMRI and radiomics can outperform RECIST for bladder cancer treatments. AI improves mpMRI segmentation and enhances radiomics feature extraction effectively. Predictive models integrate imaging biomarkers and clinical data using AI tools. Multicenter studies with strict criteria validate radiomics and QIBs clinically. Consistent mpMRI and AI applications need reliable validation in clinical practice.

Precise vesical wall staging of bladder cancer in the era of precision medicine: has it been fulfilled?

Urinary bladder cancer is a global disease that poses medical and socioeconomic challenges to patients and healthcare systems. Predicting detrusor invasiveness and pathological grade of bladder cancer by the radiologist is imperative for informed decision-making and effective patient-tailored therapy. Cystoscopy and TURBT are the current gold standard for preoperative histologic diagnosis and local pathological staging but are compromised by their intrusiveness, under-sampling, and staging inaccuracies. Over the last few decades, incredible imaging technology advancements have enabled radiologists to progress in these grading and staging tasks. MRI has become widely accepted as a noninvasive alternative. It supplements morphologic data with functional insights into the tumor microenvironment, enhancing tumor characterization and predicting the detrusor's histologic grade and invasiveness status. Radiomics is a promising field that helps radiologists achieve higher accuracies in bladder cancer staging, re-staging, and direct treating teams to potential management readjustments. Such knowledge leaps hold promise for personalized management of bladder cancer in a precision medicine era.

Recent trends in AI applications for pelvic MRI: a comprehensive review

Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.

Development of deep learning model for diagnosing muscle-invasive bladder cancer on MRI with vision transformer

Rationale and objectives

To develop and validate a deep learning (DL) model to automatically diagnose muscle-invasive bladder cancer (MIBC) on MRI with Vision Transformer (ViT).

Materials and methods

This multicenter retrospective study included patients with BC who reported to two institutions between January 2016 and June 2020 (training dataset) and a third institution between May 2017 and May 2022 (test dataset). The diagnostic model for MIBC and the segmentation model for BC on MRI were developed using the training dataset with 5-fold cross-validation. ViT- and convolutional neural network (CNN)-based diagnostic models were developed and compared for diagnostic performance using the area under the curve (AUC). The performance of the diagnostic model with manual and auto-generated regions of interest (ROImanual and ROIauto, respectively) was validated on the test dataset and compared to that of radiologists (three senior and three junior radiologists) using Vesical Imaging Reporting and Data System scoring.

Results

The training and test datasets included 170 and 53 patients, respectively. Mean AUC of the top 10 ViT-based models with 5-fold cross-validation outperformed those of the CNN-based models (0.831 ± 0.003 vs. 0.713 ± 0.007-0.812 ± 0.006, p < .001). The diagnostic model with ROImanual achieved AUC of 0.872 (95 % CI: 0.777, 0.968), which was comparable to that of junior radiologists (AUC = 0.862, 0.873, and 0.930). Semi-automated diagnosis with the diagnostic model with ROIauto achieved AUC of 0.815 (95 % CI: 0.696, 0.935).

Conclusion

The DL model effectively diagnosed MIBC. The ViT-based model outperformed CNN-based models, highlighting its utility in medical image analysis.

Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.

The role of gadolinium-based contrast agents in magnetic resonance imaging structured reporting and data systems (RADS)

Among the 28 reporting and data systems (RADS) available in the literature, we identified 15 RADS that can be used in Magnetic Resonance Imaging (MRI). Performing examinations without using gadolinium-based contrast agents (GBCA) has benefits, but GBCA administration is often required to achieve an early and accurate diagnosis. The aim of the present review is to summarize the current role of GBCA in MRI RADS. This overview suggests that GBCA are today required in most of the current RADS and are expected to be used in most MRIs performed in patients with cancer. Dynamic contrast enhancement is required for correct scores calculation in PI-RADS and VI-RADS, although scientific evidence may lead in the future to avoid the GBCA administration in these two RADS. In Bone-RADS, contrast enhancement can be required to classify an aggressive lesion. In RADS scoring on whole body-MRI datasets (MET-RADS-P, MY-RADS and ONCO-RADS), in NS-RADS and in Node-RADS, GBCA administration is optional thanks to the intrinsic high contrast resolution of MRI. Future studies are needed to evaluate the impact of the high T1 relaxivity GBCA on the assignment of RADS scores.

Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

Magnetic resonance radiographic features which might lead to misdiagnosis of muscle-invasive bladder cancer based on vesical imaging reporting and data system: the application experience of a single center

Background

The Vesical Imaging Reporting and Data System (VI-RADS) has been widely used for diagnosing muscle-invasive bladder cancer (MIBC), yet instances of misdiagnosis persist. However, limited research discusses the factors affecting its accuracy. This study aimed to evaluate the diagnostic efficacy of the VI-RADS in our center and to preliminarily identify possible magnetic resonance imaging (MRI) characteristics of misdiagnosis.

Methods

From January 2018 to February 2023, a consecutive series of 211 participants pathologically diagnosed with bladder cancer (BC) who underwent an MRI exam were retrospectively enrolled. MRI was interpreted by 2 radiologists with different levels of experience, the diagnostic performance was validated using the receiver operating characteristic (ROC) curve, and VI-RADS ≥4 was considered to indicate MIBC-positive status. The clinical and radiographic characteristics of the true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) groups were analyzed using Kruskal-Wallis test or Fisher exact test.

Results

With VI-RADS ≥4 as the cutoff value, the area under the ROC curves (AUCs) were 0.951 (0.912-0.976) and 0.847 (0.791-0.893) for the more-experienced reader and less-experienced reader, respectively, with good interobserver agreement (κ=0.74105). The median tumor size in the TP (more experienced: 57 cases; less experienced: 44 cases) and FP (more experienced: 8 cases; less experienced: 9 cases) groups was larger than that in the TN (more experienced: 141 cases; less experienced: 139 cases) group for the more-experienced reader (TP: 28 mm; FP: 31 mm; TN: 19 mm; P<0.001 and P=0.031, respectively) and the less-experienced reader (TP: 31 mm; FP: 28 mm; TN: 19 mm; P<0.001 and P=0.042, respectively). The tumor base in the TP and FP groups was larger than that in the TN group for the more-experienced reader (TP: 37 mm; FP: 48 mm; TN: 15 mm; both P<0.001) and for the less-experienced reader (FP: 42 mm; FP: 36 mm; TN: 15 mm; P<0.001 and P=0.022, respectively). The median tumor base in the TP group was larger than that in the FN group for the less-experienced reader (TP: 42 mm; FN: 17 mm; P=0.004).

Conclusions

We observed good to excellent AUCs with good interobserver agreement among radiologists with different levels of expertise using VI-RADS. Large tumor size and wide tumor base affected the accuracy of VI-RADS in MIBC diagnosis.

Diagnostic performance of multiparametric MRI based Vesical Imaging-Reporting and Data System (VI-RADS) scoring in discriminating between non-muscle invasive and muscle invasive bladder cancer

Purpose

The purpose of the present study was to assess the diagnostic accuracy of the Vesical Imaging-Reporting and Data System (VI-RADS) scoring system in predicting muscle infiltration of bladder cancer (BC) on a pre-operative multiparametric magnetic resonance imaging (mpMRI).

Methods

The prospective study enrolled patients with bladder lesions detected on a preliminary ultrasonography or cystoscopy. The patients underwent mpMRI on a 3T MRI scanner followed by surgery within 2 weeks. The tumours were assigned a VI-RADS score by 2 experienced abdominal radiologists. The VI-RADS score was compared with postoperative histopathological findings to confirm detrusor muscle infiltration. The diagnostic performance of VI-RADS for predicting muscle invasion was assessed by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy.

Results

A total of 60 patients were included in the study with a male: female ratio of 4.4 : 1. Transurethral resection of bladder tumour (TURBT) was performed in 47 (78.4%) and radical cystectomy in 13 (21.6%) patients. 19 (31.7%) had non-muscle invasive invasive BC (NMIBCa) and 41 (68.3%) had muscle invasive BC (MIBCa) on histopathology. There was a significant association between VI-RADS score and its components with muscle invasion (p < 0.05). A VI-RADS score of ≥ 3 had a sensitivity of 97.56% (95% CI: 0.87-0.99%), specificity of 73.68% (95% CI: 0.49-0.91), positive predictive value of 88.9% (95% CI: 0.79-0.94), negative predictive value of 93.33% (95% CI: 0.66-0.99), and diagnostic accuracy of 90% (95% CI: 0.80-0.96) for prediction of muscle invasion.

Conclusion

VI-RADS scoring system pre-operatively predicts the likelihood of muscle invasion in BC with a satisfactory diagnostic performance, and it should be incorporated in the diagnostic work-up of BC patients.