Staging of bladder cancer with multiparametric MRI

Performance of 18F-FDG PET/MRI and its parameters in staging and neoadjuvant therapy response evaluation in bladder cancer

18F-FDG PET/MRI shows potential efficacy in the diagnosis of bladder cancer (BLCA). However, the performance of 18F-FDG PET/MRI in staging and neoadjuvant therapy (NAT) response evaluation for BLCA patients remains elusive. Here, we conduct this study to evaluate the performance of 18F-FDG PET/MRI and its derived parameters for tumor staging and NAT response prediction in BLCA. Forty BLCA patients were retrospectively enrolled to evaluate the performance of 18F-FDG PET/MRI in staging and NAT response prediction in BLCA. The feasibility of using 18F-FDG PET/MRI-related parameters for tumor staging and NAT response evaluation was also analyzed. In conclusion, 18F-FDG PET/MRI is found to show good performance in the BLCA staging and NAT response prediction. Moreover, ΔSUVmean is an efficacious candidate parameter for NAT response prediction. This study highlights that 18F-FDG PET/MRI is a promising imaging approach in the clinical diagnosis and treatment for BLCA.

SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading

Background

Multi-parametric magnetic resonance imaging (MP-MRI) may provide comprehensive information for graded diagnosis of bladder cancer (BCa). Nevertheless, existing methods ignore the complex correlation between these MRI sequences, failing to provide adequate information. Therefore, the main objective of this study is to enhance feature fusion and extract comprehensive features from MP-MRI using deep learning methods to achieve an accurate diagnosis of BCa grading.

Methods

In this study, a self-attention-based MP-MRI feature fusion framework (SMMF) is proposed to enhance the performance of the model by extracting and fusing features of both T2-weighted imaging (T2WI) and dynamic contrast-enhanced imaging (DCE) sequences. A new multiscale attention (MA) model is designed to embed into the neural network (CNN) end to further extract rich features from T2WI and DCE. Finally, a self-attention feature fusion strategy (SAFF) was used to effectively capture and fuse the common and complementary features of patients' MP-MRIs.

Results

In a clinically collected sample of 138 BCa patients, the SMMF network demonstrated superior performance compared to the existing deep learning-based bladder cancer grading model, with accuracy, F1 value, and AUC values of 0.9488, 0.9426, and 0.9459, respectively.

Conclusion

Our proposed SMMF framework combined with MP-MRI information can accurately predict the pathological grading of BCa and can better assist physicians in diagnosing BCa.

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.

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.

Contrast-enhanced Ultrasound Combined With Elastography for the Evaluation of Muscle-invasive Bladder Cancer in Rats

OBJECTIVES

By comparing with the control group, we evaluated the usefulness of contrast-enhanced ultrasound (CEUS) combined with elastography for the assessment of muscle invasion by bladder cancer (MIBC) in a Sprague-Dawley (SD) rat model.

METHODS

In the experimental group, 40 SD rats developed in situ bladder cancer (BLCA) in response to N-methyl-N-nitrosourea treatment, whereas 40 SD rats were included in the control group for comparison. We compared PI, Emean , microvessel density (MVD), and collagen fiber content (CFC) between the two groups. In the experimental group, Bland-Altman test was used to assess the relationships between various parameters. The largest Youden value was used as the cut-off point, and binomial logistic regression analysis was performed to analyze the PI and Emean . Receiver operating characteristic (ROC) curve analysis was performed to determine the diagnostic power of parameters, individually and in combination.

RESULTS

The PI, Emean , MVD, and CFC were significantly lower in the control group than in the experimental group (P < .05). The PI, Emean , MVD, and CFC were significantly higher for MIBC than for non-muscle-invasive bladder cancer (P < .05). There were significant correlations between PI and MVD, and between Emean and CFC. The diagnostic efficiency analysis showed PI had the highest sensitivity, CFC had the highest specificity, and PI + Emean had the highest diagnostic efficacy.

CONCLUSION

CEUS and elastography can distinguish lesions from normal tissue. PI, MVD, Emean , and CFC were useful for the detection of BLCA myometrial invasion. The comprehensive utilization of PI and Emean improved diagnostic accuracy and have clinical application.

[Bladder preservation treatments for bladder cancer: Trimodality therapy, an overview of clinical practices in 2023]

Bladder cancer is the most frequent tumor of the urinary tract. Patients diagnosed at a stage when the tumor has spread into or through the muscle layer of the bladder wall are usually treated with cystectomy. The evolution of cancer treatments, related to the development of alternative treatment options to the historical surgical standard and to the implication of the patient as an actor in decision-making, trends towards organ and function preservation without sacrificing efficacy. Trimodality therapy, which is a maximal transurethral resection of the tumor followed by concurrent chemoradiation, is an interesting therapeutic alternative for patients unfit for surgery and for those wishing to benefit from organ preservation. Radiotherapy offers excellent treatment possibilities for muscle-invasive bladder cancer. In selected T2-stage patients fit for trimodality therapy, it has an equivalent oncological outcome compared to cystectomy while having less severe complications and offering organ preservation. It remains feasible in inoperable patients while offering significant perspectives of relapse-free survival. Finally, it also is an efficient palliative treatment in patients where mid-term local control and hemostasis are sought after.

Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm

Introduction

Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images.

Materials

We retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively.

Results

We obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor.

Conclusion

We developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.

Seeing is Believing: State of the Art Imaging of Bladder Cancer

Imaging plays an important role in bladder cancer (BCa) diagnostic work-up. Ultrasound achieves an intermediate sensitivity in detecting urinary tract alterations and is considered a suboptimal imaging technique in diagnosis of BCa. CT urography accurately detects BCa in patients presenting with hematuria Multiparametric MRI achieves a very high rate of BCa detection and helps with accurate staging of patients; however, this modality is still not widely supported by international guidelines. The main applications of MRI are local tumor staging and differentiation between non-muscle-invasive BCa and muscle-invasive BCa. These applications led to development of Vesical Imaging-Reporting and Data System (VI-RADS) scoring system. The VI-RADS scoring system was developed in the setting of post-resection of primary bladder tumor and instillation of intravesical Bacillus Calmette-Guerin therapy; however validation of this system in the post-treatment setting showed promising results. The high risk of BCa recurrence leads to its application in the assessment of response to therapy and for disease surveillance after treatment. MRI is rapidly becoming a leading imaging modality in BCa diagnostic workup, assessment of response to therapies and for longitudinal surveillance, and plays an important role in BCa surgical and radiation therapy treatment planning.

Multi-task deep learning based on T2-Weighted Images for predicting Muscular-Invasive Bladder Cancer

BACKGROUND

An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle-Invasive Bladder Cancer (MIBC) in Bladder Cancer (BCa) can help the urologist make diagnostic decisions. Considering the absence of multiparametric MRI for contrast medium allergy and economic reasons, this study aims to develop a deep learning method based on T2-Weighted (T2WI) images alone for predicting NMIBC and MIBC.

METHOD

We propose a Multi-task BCa Muscular Invasion Prediction (MBMIP) model to discriminate MIBC from NMIBC. The three-channel-input including the original T2WI image, segmented bladder, and the region of interest can help the MBMIP model locate the bladder and pay more attention to the surrounding information of the tumor. Inception V3 is used as the feature extraction module, which uses multiple branches to extract high-level features with different degrees of abstraction. In addition, based on the idea of multi-task learning, a reconstruction block for T2WI images is also introduced to assist the backbone classification network to improve the classification performance.

RESULTS

The entire data consist of retrospective data (390 cases), prospective data (39 cases), and multi-center data (39 cases). In the retrospective test, the accuracy, sensitivity, and specificity of the MBMIP model are 0.911, 0.889, and 0.920 respectively, while those of the prospective test are 0.923, 1.000, and 0.885. And in the muti-center test, the MBMIP model yields accuracy, sensitivity, and specificity of 0.846, 0.667, and 0.879.

CONCLUSION

The MBMIP model could achieve a satisfactory prediction result in discriminating between NMIBC and MIBC, which may aid urologists in preoperative decision-making for BCa patients.

Artificial intelligence in multi-parametric magnetic resonance imaging: A review

Multi-parametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availability of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI. This article is protected by copyright. All rights reserved.

Comparison of reduced field-of-view DWI and full field-of view DWI for the differentiation between non-muscle invasive bladder cancer and muscle invasive bladder cancer using VI-RADS

Purpose

To evaluate whether reduced field-of-view (rFOV) DWI sequence improves the differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) using VI-RADS.

Material and methods

Eighty-nine patients underwent bladder MRI with full field-of-view (fFOV) DWI and rFOV DWI sequence. Images were independently evaluated by 2 radiologists. The sensitivities, specificities, accuracies, and areas under the curve (AUCs) for the differentiation between NMIBC and MIBC with fFOV DWI and with rFOV DWI sequence were calculated using VI-RADS. Apparent diffusion coefficients (ADC) values were measured for each patient and averaged.

Results

The sensitivity, specificity, accuracy, and AUC by reader 1 were 92%, 78%, 82% and 0.905 with fFOV DWI, and 92%, 86%, 88% and 0.916 with rFOV DWI sequence, respectively. The sensitivity, specificity, accuracy and AUC by reader 2 were 96%, 76%, 82% and 0.900 with conventional DWI, and 96%, 81%, 85% and 0.907 with rFOV DWI sequence, respectively. The specificity and accuracy of reader 1 were significantly better with rFOV DWI sequence than with fFOV DWI, in contrast there was no significant difference for the others. The average of ADC values of fFOV DWI and rFOV DWI sequence were 1.004×10⁻⁶ mm²/s and 1.003×10⁻⁶ mm²/s, respectively.

Conclusion

The diagnostic ability of rFOV DWI sequence may be better than that of fFOV DWI using VI-RADS for the differentiation between NMIBC and MIBC regardless of image-reading experience, it is controversial.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

[Modern tomography imaging techniques in urological diseases]

BACKGROUND

Radiologic imaging is important for the detection, staging and follow-up of urological tumors. Basic therapy decisions for both oncological (surgical vs. systemic therapy, e.g. in testicular cancer) and non-oncological pathologies (interventional vs. conservative therapy, e.g. for ureteral stones) depend largely on the tomographic imaging performed. Due to its almost ubiquitous availability, speed and cost-effectiveness, computed tomography (CT) plays an important role not only in the clarification of abdominal trauma and non-traumatic emergencies, but also in staging and follow-up of oncological patients. However, the level of radiation exposure, impaired renal function and allergies to iodinated contrast media limit the use of CT. Magnetic resonance imaging (MRI) can be a good alternative for many areas of application in oncological and non-oncological imaging due to its high soft tissue differentiation and functional-specific protocols but without the use of ionizing radiation.

AIM

In the following, the main indications of abdominal and pelvic CT and MRI in urology and their limitations are summarized.

RESULTS

The areas of application between CT and MRI are increasingly overlapping, since the latest developments in CT continue to further reduce radiation exposure and increase contrast information, while the speed and robustness of MRI are significantly improving at the same time.

Magnetic Fields and Cancer: Epidemiology, Cellular Biology, and Theranostics

Humans are exposed to a complex mix of man-made electric and magnetic fields (MFs) at many different frequencies, at home and at work. Epidemiological studies indicate that there is a positive relationship between residential/domestic and occupational exposure to extremely low frequency electromagnetic fields and some types of cancer, although some other studies indicate no relationship. In this review, after an introduction on the MF definition and a description of natural/anthropogenic sources, the epidemiology of residential/domestic and occupational exposure to MFs and cancer is reviewed, with reference to leukemia, brain, and breast cancer. The in vivo and in vitro effects of MFs on cancer are reviewed considering both human and animal cells, with particular reference to the involvement of reactive oxygen species (ROS). MF application on cancer diagnostic and therapy (theranostic) are also reviewed by describing the use of different magnetic resonance imaging (MRI) applications for the detection of several cancers. Finally, the use of magnetic nanoparticles is described in terms of treatment of cancer by nanomedical applications for the precise delivery of anticancer drugs, nanosurgery by magnetomechanic methods, and selective killing of cancer cells by magnetic hyperthermia. The supplementary tables provide quantitative data and methodologies in epidemiological and cell biology studies. Although scientists do not generally agree that there is a cause-effect relationship between exposure to MF and cancer, MFs might not be the direct cause of cancer but may contribute to produce ROS and generate oxidative stress, which could trigger or enhance the expression of oncogenes.

Conspicuity and muscle-invasiveness assessment for bladder cancer using VI-RADS: a multi-reader, contrast-free MRI study to determine optimal b-values for diffusion-weighted imaging

OBJECTIVE

To (1) compare bladder cancer (BC) muscle invasiveness among three b-values using a contrast-free approach based on Vesical Imaging-Reporting and Data System (VI-RADS), to (2) determine if muscle-invasiveness assessment is affected by the reader experience, and to (3) compare BC conspicuity among three b-values, qualitatively and quantitatively.

METHODS

Thirty-eight patients who underwent a bladder MRI on a 3.0-T scanner were enrolled. The gold standard was histopathology report following transurethral resection of BC. Three sets of images, including T2w and different b-values for DWI, set 1 (b = 1000 s/mm²), set 2 (b = 1500 s/mm²), and set 3 (b = 2000 s/mm²), were reviewed by three differently experienced readers. Descriptive statistics and Intraclass Correlation Coefficient (ICC) were calculated. Comparisons among readers and DWI sets were performed with the Wilcoxon test. Receiver operating characteristic (ROC) analysis was performed. Areas under the curves (AUCs) and pairwise comparison were calculated.

RESULTS

AUCs of muscle-invasiveness assessment ranged from 0.896 to 0.984 (reader 1), 0.952-0.968 (reader 2), and 0.952-0.984 (reader 3) without significant differences among different sets and readers (p > 0.05). The mean conspicuity qualitative scores were higher in Set 1 (2.21-2.33), followed by Set 2 (2-2.16) and Set 3 (1.82-2.14). The quantitative conspicuity assessment showed that mean normalized intensity of tumor was significantly higher in Set 2 (4.217-4.737) than in Set 1 (3.923-4.492) and Set 3 (3.833-3.992) (p < 0.05).

CONCLUSION

Muscle invasiveness can be assessed with high accuracy using a contrast-free protocol with T2W and DWI, regardless of reader's experience. b = 1500 s/mm² showed the best tumor delineation, while b = 1000 s/mm² allowed for better tumor-wall interface assessment.

MRI as a Tool to Assess Interstitial Cystitis Associated Bladder and Brain Pathologies

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic, often incapacitating condition characterized by pain seeming to originate in the bladder in conjunction with lower urinary tract symptoms of frequency and urgency, and consists of a wide range of clinical phenotypes with diverse etiologies. There are currently no diagnostic tests for IC/BPS. Magnetic resonance imaging (MRI) is a relatively new tool to assess IC/BPS. There are several methodologies that can be applied to assess either bladder wall or brain-associated alterations in tissue morphology and/or pain. IC/BPS is commonly associated with bladder wall hyperpermeability (BWH), particularly in severe cases. Our group developed a contrast-enhanced magnetic resonance imaging (CE-MRI) approach to assess BWH in preclinical models for IC/BPS, as well as for a pilot study for IC/BPS patients. We have also used the CE-MRI approach to assess possible therapies to alleviate the BWH in preclinical models for IC/BPS, which will hopefully pave the way for future clinical trials. In addition, we have used molecular-targeted MRI (mt-MRI) to quantitatively assess BWH biomarkers. Biomarkers, such as claudin-2, may be important to assess and determine the severity of BWH, as well as to assess therapeutic efficacy. Others have also used other MRI approaches to assess the bladder wall structural alterations with diffusion-weighted imaging (DWI), by measuring changes in the apparent diffusion coefficient (ADC), diffusion tensor imaging (DTI), as well as using functional MRI (fMRI) to assess pain and morphological MRI or DWI to assess anatomical or structural changes in the brains of patients with IC/BPS. It would be beneficial if MRI-based diagnostic tests could be routinely used for these patients and possibly used to assess potential therapeutics.

MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons

Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumor. Hence, multiregion segmentation on patients presenting with symptoms of bladder tumors using deep learning heralds a new level of staging accuracy and prediction of the biologic behavior of the tumor. Nevertheless, despite the success of these models in other medical problems, progress in multiregion bladder segmentation, particularly in MRI and CT modalities, is still at a nascent stage, with just a handful of works tackling a multiregion scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.

MR-Guided Adaptive Radiotherapy for Bladder Cancer

Radiotherapy has an important role in the curative and palliative treatment settings for bladder cancer. As a target for radiotherapy the bladder presents a number of technical challenges. These include poor tumor visualization and the variability in bladder size and position both between and during treatment delivery. Evidence favors the use of magnetic resonance imaging (MRI) as an important means of tumor visualization and local staging. The availability of hybrid systems incorporating both MRI scanning capabilities with the linear accelerator (MR-Linac) offers opportunity for in-room and real-time MRI scanning with ability of plan adaption at each fraction while the patient is on the treatment couch. This has a number of potential advantages for bladder cancer patients. In this article, we examine the technical challenges of bladder radiotherapy and explore how magnetic resonance (MR) guided radiotherapy (MRgRT) could be leveraged with the aim of improving bladder cancer patient outcomes. However, before routine clinical implementation robust evidence base to establish whether MRgRT translates into improved patient outcomes should be ascertained.