A rare case of spontaneous bladder wall abscess mimicking bladder tumour in young women.

INTRODUCTION: Abscess of the bladder wall is a rare urological disorder, with a few cases recorded in the literature. The finding of a bladder wall mass via computed tomography (CT) imaging in a visiting patient is the subject of this report. CASE DISCUSSION: A 37-year-old woman with persistent pain in the suprapubic area and lower urinary tract symptoms was examined as a case study. Through a CT scan revealed an inhomogeneous structure in the anteroinferior part of the right bladder. A cystoscopy procedure followed by transurethral resection was performed to remove the mass, which was found to be an abscess. A Foley catheter with irrigation was administered after surgery, and the patient goes home in three days. CONCLUSION: the patient had no symptoms or discomfort in the lower urinary tract after follow-up. Despite the rarity of bladder wall abscesses, cystoscopy can be used to aid diagnosis. Transurethral resection of bladder wall can reduce the mass and eliminate the possibility of malignancy.

A novel model of artificial intelligence based automated image analysis of CT urography to identify bladder cancer in patients investigated for macroscopic hematuria.

OBJECTIVE: To evaluate whether artificial intelligence (AI) based automatic image analysis utilising convolutional neural networks (CNNs) can be used to evaluate computed tomography urography (CTU) for the presence of urinary bladder cancer (UBC) in patients with macroscopic hematuria. METHODS: Our study included patients who had undergone evaluation for macroscopic hematuria. A CNN-based AI model was trained and validated on the CTUs included in the study on a dedicated research platform (Recomia.org). Sensitivity and specificity were calculated to assess the performance of the AI model. Cystoscopy findings were used as the reference method. RESULTS: The training cohort comprised a total of 530 patients. Following the optimisation process, we developed the last version of our AI model. Subsequently, we utilised the model in the validation cohort which included an additional 400 patients (including 239 patients with UBC). The AI model had a sensitivity of 0.83 (95% confidence intervals [CI], 0.76-0.89), specificity of 0.76 (95% CI 0.67-0.84), and a negative predictive value (NPV) of 0.97 (95% CI 0.95-0.98). The majority of tumours in the false negative group (n = 24) were solitary (67%) and smaller than 1 cm (50%), with the majority of patients having cTaG1-2 (71%). CONCLUSIONS: We developed and tested an AI model for automatic image analysis of CTUs to detect UBC in patients with macroscopic hematuria. This model showed promising results with a high detection rate and excessive NPV. Further developments could lead to a decreased need for invasive investigations and prioritising patients with serious tumours.

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this pressing scenario, there is a burgeoning need to identify effective biomarkers for both the diagnosis and therapeutic guidance of bladder cancer. This study focuses on evaluating the potential of high-definition computed tomography (CT) imagery coupled with RNA-sequencing analysis to accurately predict bladder tumor stages, utilizing deep residual networks. Data for this study, including CT images and RNA-Seq datasets for 82 high-grade bladder cancer patients, were sourced from the TCIA and TCGA databases. We employed Cox and lasso regression analyses to determine radiomics and gene signatures, leading to the identification of a three-factor radiomics signature and a four-gene signature in our bladder cancer cohort. ROC curve analyses underscored the strong predictive capacities of both these signatures. Furthermore, we formulated a nomogram integrating clinical features, radiomics, and gene signatures. This nomogram's AUC scores stood at 0.870, 0.873, and 0.971 for 1-year, 3-year, and 5-year predictions, respectively. Our model, leveraging radiomics and gene signatures, presents significant promise for enhancing diagnostic precision in bladder cancer prognosis, advocating for its clinical adoption.

Paraganglioma of the urinary bladder: A case report.

It is very un comm on to discover Paraga ngliomas in the uri nar y bladder. Thes e tu mo ur s origina te from the sympathetic nerves which supp ly chromaffin cells in the bladde r wall. They can be classified as functional or nonfunctional. If functional, the most common presentations are with hypertensive crisis or post-micturition syncope. A si lent pa raganglioma of th e bl adder ca n be easily m isdiagnosed w hich can resu lt in severe pe ri- oper ative morbidity. We pre sent a ca se of a male pati ent who was being m a naged fo r hype rt ension for 2-3 ye ars. He presented at The Indus Hospital, Karachi o n 7th August 2022 with gross painless haematuria. An ultrasound scan revealed an echogenic lesion aris ing from base of the urinar y bladder, wh ic h was trea ted via Transurethral Resection o f Bladde r Tumour (TURBT). Histopathological report revealed Paraganglioma of the Bladder. He was later scheduled for Partial Cystectomy (PC) and has been doing well ever since.

Bladder MRI with deep learning-based reconstruction: a prospective evaluation of muscle invasiveness using VI-RADS.

PURPOSE: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort. METHODS: Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WIS), standard diffusion-weighted imaging (DWIS), fast T2WI with DLR (T2WIDL), and fast DWI with DLR (DWIDL) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWIS and DWIDL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: Compared to T2WIS and DWIS, T2WIDL and DWIDL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR. CONCLUSIONS: The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer.

Predicting preoperative muscle invasion status for bladder cancer using computed tomography-based radiomics nomogram.

OBJECTIVES: The aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa). METHODS: A retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram's performance was assessed based on discrimination, calibration, and clinical utility. RESULTS: After obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742-0.880), 0.845 (95% CI, 0.781-0.908), and 0.896 (95% CI, 0.846-0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703-0.913), 0.847 (95% CI, 0.739-0.954), and 0.887 (95% CI, 0.803-0.971). According to the DeLong test, the radiomics-clinical nomogram's AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models. CONCLUSIONS: CT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa.

MRI evaluation of vesical imaging reporting and data system for bladder cancer after neoadjuvant chemotherapy.

BACKGROUND: The Vesical Imaging-Reporting and Data System (VI-RADS) has demonstrated effectiveness in predicting muscle invasion in bladder cancer before treatment. The urgent need currently is to evaluate the muscle invasion status after neoadjuvant chemotherapy (NAC) for bladder cancer. This study aims to ascertain the accuracy of VI-RADS in detecting muscle invasion post-NAC treatment and assess its diagnostic performance across readers with varying experience levels. METHODS: In this retrospective study, patients with muscle-invasive bladder cancer who underwent magnetic resonance imaging (MRI) after NAC from September 2015 to September 2018 were included. VI-RADS scores were independently assessed by five radiologists, consisting of three experienced in bladder MRI and two inexperienced radiologists. Comparison of VI-RADS scores was made with postoperative histopathological diagnosis. Receiver operating characteristic curve analysis (ROC) was used for evaluating diagnostic performance, calculating sensitivity, specificity, and area under ROC (AUC)). Interobserver agreement was assessed using the weighted kappa statistic. RESULTS: The final analysis included 46 patients (mean age: 61 years ± 9 [standard deviation]; age range: 39-70 years; 42 men). The pooled AUC for predicting muscle invasion was 0.945 (95% confidence interval (CI): 0.893-0.977) for experienced readers, and 0.910 (95% CI: 0.831-0.959) for inexperienced readers, and 0.932 (95% CI: 0.892-0.961) for all readers. At an optimal cut-off value ≥ 4, pooled sensitivity and specificity were 74.1% (range: 66.0-80.9%) and 94.1% (range: 88.6-97.7%) for experienced readers, and 63.9% (range: 59.6-68.1%) and 86.4% (range: 84.1-88.6%) for inexperienced readers. Interobserver agreement ranged from substantial to excellent between all readers (k = 0.79-0.92). CONCLUSIONS: VI-RADS accurately assesses muscle invasion in bladder cancer patients after NAC and exhibits good diagnostic performance across readers with different experience levels.

Urothelial Carcinoma: Epidemiology and Imaging-Based Review.

Bladder cancer is the 6th most common malignancy in the United States, with urothelial carcinomas comprising over 95% of cases of bladder cancer, and commands a significant disease burden in Rhode Island. Imaging studies can provide valuable diagnostic information for urothelial carcinomas at initial presentation and are routinely used for noninvasive staging, treatment response monitoring, and post-treatment surveillance. This review aims to discuss and highlight three imaging modalities: ultrasonography, computed tomography, and magnetic resonance imaging, with particular focus on the notable features and appearance of urothelial carcinoma on each modality and their relative utility throughout the disease course. A general overview of disease epidemiology and treatment practices is also provided.

Contrast-enhanced CT in the differential diagnosis of bladder cancer and paraganglioma.

PURPOSE: We sought to summarize the value of contrast-enhanced computed tomography (CECT) in the differential diagnosis of bladder paraganglioma (BPG) and bladder cancer. METHODS: The medical records of 19 patients with BPG (13 males, 6 females) and 56 patients with bladder cancer (49 males, 7 females) between November 2007 and June 2023 were retrospectively reviewed. All patients underwent unenhanced and contrast-enhanced CT scanning. RESULTS: Patient age (46.4 ± 11.1 years vs. 58.6 ± 16.0 years), tumor calcification (1/19 vs. 18/56), stalk (0/19 vs. 10/56), internal vessels (15/19 vs. 19/56) and the enlarged adjacent supplying artery (14/19 vs. 10/56) were significantly different between BPG and bladder cancer (P < 0.05). The CT value in the corticomedullary phase (92.4 ± 16.6 HU vs. 64.0 ± 14.5 HU) and the contrast-enhanced value in the corticomedullary phase (54.5 ± 17.4 HU vs. 28.5 ± 12.8 HU) were significantly greater in BPG patients than in bladder cancer patients (P < 0.001), with corresponding area under the curve values of 0.930 and 0.912, respectively. The optimal cutoff values were 83.2 HU and 38.5 HU, respectively. A CT value > 83.2 HU in the corticomedullary phase and a contrast-enhanced CT value > 38.5 HU in the corticomedullary phase were used to indicate BPG with sensitivities of 78.9% and 89.5%, respectively, and specificities of 94.6% and 75.0%, respectively. CONCLUSION: The corticomedullary phase of CECT plays an important role in the preoperative differential diagnosis of BPG and bladder cancer.

Performance of VI-RADS in predicting muscle-invasive bladder cancer after transurethral resection: a single center retrospective analysis.

PURPOSE: To assess VIRADS performance and inter-reader agreement for detecting muscle-invasive bladder cancer (MIBC) following transurethral resection of bladder tumor (TURBT). METHODS: An IRB-approved, HIPAA-compliant, retrospective study from 2016 to 2020 included patients with bladder urothelial carcinoma who underwent MRI after TURBT, and cystectomy within 3 months without post-MRI treatments. Three radiologists blinded to pathology results independently reviewed MR images and assigned a VI-RADS score. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of VI-RADS were assessed for diagnosing MIBC using VI-RADS scores ≥ 3 and ≥ 4. Inter-reader agreement was assessed using Gwet's agreement coefficient (AC) and percent agreement. RESULTS: The cohort consisted of 70 patients (mean age, 68 years ± 11 [SD]; range 39-85; 58 men) and included 32/70 (46%) with MIBC at cystectomy. ROC analysis revealed an AUC ranging from 0.67 to 0.77 and no pairwise statistical difference between readers (p-values, 0.06, 0.08, 0.97). Percent sensitivity, specificity, PPV, NPV and accuracy for diagnosing MIBC for the three readers ranged from 81.3-93.8, 36.8-55.3, 55.6-60.5, 77.3-87.5, and 62.9-67.1 respectively for VI-RADS score ≥ 3, and 78.1-81.3, 47.4-68.4, 55.6-67.6, 72.0-78.8 and 61.4-72.9 respectively for VI-RADS score ≥ 4. Gwet's AC was 0.63 [95% confidence interval (CI): 0.49,0.78] for VI-RADS score ≥ 3 with 79% agreement [95% CI 72,87] and 0.54 [95%CI 0.38,0.70] for VI-RADS score ≥ 4 with 76% agreement [95% CI 69,84]. VIRADS performance was not statistically different among 31/70 (44%) patients who received treatments prior to MRI (p ≥ 0.16). CONCLUSION: VI-RADS had moderate sensitivity and accuracy but low specificity for detection of MIBC following TURBT, with moderate inter-reader agreement.

Prediction of histopathologic grades of bladder cancer with radiomics based on MRI: Comparison with traditional MRI.

PURPOSE: To compare biparametric magnetic resonance imaging (bp-MRI) radiomics signatures and traditional MRI model for the preoperative prediction of bladder cancer (BCa) grade. MATERIALS AND METHODS: This retrospective study included 255 consecutive patients with pathologically confirmed 113 low-grade and 142 high-grade BCa. The traditional MRI nomogram model was developed using univariate and multivariate logistic regression by the mean apparent diffusion coefficient (ADC), vesical imaging reporting and data system, tumor size, and the number of tumors. Volumes of interest were manually drawn on T2-weighted imaging (T2WI) and ADC maps by 2 radiologists. Using one-way analysis of variance, correlation, and least absolute shrinkage and selection operator methods to select features. Then, a logistic regression classifier was used to develop the radiomics signatures. Receiver operating characteristic (ROC) analysis was used to compare the diagnostic abilities of the radiomics and traditional MRI models by the DeLong test. Finally, decision curve analysis was performed by estimating the clinical usefulness of the 2 models. RESULTS: The area under the ROC curves (AUCs) of the traditional MRI model were 0.841 in the training cohort and 0.806 in the validation cohort. The AUCs of the 3 groups of radiomics model [ADC, T2WI, bp-MRI (ADC and T2WI)] were 0.888, 0.875, and 0.899 in the training cohort and 0.863, 0.805, and 0.867 in the validation cohort, respectively. The combined radiomics model achieved higher AUCs than the traditional MRI model. decision curve analysis indicated that the radiomics model had higher net benefits than the traditional MRI model. CONCLUSION: The bp-MRI radiomics model may help distinguish high-grade and low-grade BCa and outperforming the traditional MRI model. Multicenter validation is needed to acquire high-level evidence for its clinical application.

Determining the clinicopathological significance of the VI-RADS ≧4 group: a retrospective study.

BACKGROUND: The Vesical Imaging Reporting and Data System (VI-RADS) is widely used for predicting muscle-invasive bladder cancer (MIBC). This study aimed to determine the clinicopathological significance of the VI-RADS ≧4 (VI≧4) group. METHODS: Patients who underwent transurethral resections of bladder tumors during the study period and preoperative magnetic resonance imaging were considered. The patients were pathologically diagnosed with urothelial carcinoma (UC). We first compared the results of patients with VI-RADS scores of 3 and 4 to determine the cut-off score for MIBC; thereafter, the patients were divided into the VI≧4 and VI-RADS ≦3 (VI≦3) groups using VI-RADS. The clinicopathological significance of the VI≧4 group was examined retrospectively by comparing the characteristics of each group. RESULTS: In total, 121 cases were examined, of which 28 were pathologically diagnosed with MIBC. Of the 28 MIBC cases, three (10.7%) had a VI-RADS score of ≦3, and 25 (89.3%) had a VI-RADS score of ≧4. Of the 93 NMIBC cases, 86 (92.5%) had a VI-RADS score of ≦3, and seven (7.5%) had a VI-RADS score of ≧4. The diagnostic performance of the VI-RADS with a cut-off score of 4 was 89.3% for sensitivity, 92.5% for specificity, and an area under the curve (AUC) of 0.91. Contrastingly, for a cut-off score of 3, the sensitivity was 89.3%, specificity was 62.0%, and AUC was 0.72. A VI-RADS score of ≥ 4 could predict MIBC. In the VI≧4 group, 30 of 32 (93.8%) patients had high-grade tumors. The VI≧4 group had significantly more high-grade bladder cancers than the VI≦3 group (p < 0.001 OR = 31.77 95%CI:8.47-1119.07). In addition, the VI≧4 group had more tumor necrosis (VI≧4 vs VI≦3, p < 0.001 OR = 7.46 95%CI:2.61-21.34) and more UC variant cases (VI≧4 vs VI≦3, p = 0.034 OR = 3.28 95%CI:1.05-10.25) than the VI≦3 group. CONCLUSIONS: This study suggests that VI-RADS has a high diagnostic performance in predicting MIBC and that VI-RADS could diagnose high-grade tumors, necrosis, and UC variants.

Multiparametric Magnetic Resonance Imaging in the follow-up of non-muscle-invasive bladder tumors after intravesical instillations: a promising tool.

PURPOSE: The standard follow-up for non-muscle-invasive bladder cancer is based on cystoscopy. Unfortunately, post-instillation inflammatory changes can make the interpretation of this exam difficult, with lower specificity. This study aimed to evaluate the interest of bladder MRI in the follow-up of patients following intravesical instillation. METHODS: Data from patients who underwent cystoscopy and bladder MRI in a post-intravesical instillation setting between February 2020 and March 2023 were retrospectively collected. Primary endpoint was to evaluate and compare the diagnostic performance of cystoscopy and bladder MRI in the overall cohort (n = 67) using the pathologic results of TURB as a reference. The secondary endpoint was to analyze the diagnostic accuracy of cystoscopy and bladder MRI according to the appearance of the lesion on cystoscopy [flat (n = 40) or papillary (n = 27)]. RESULTS: The diagnostic performance of bladder MRI was better than that of cystoscopy, with a specificity of 47% (vs. 6%, p < 0.001), a negative predictive value of 88% (vs. 40%, p = 0.03), and a positive predictive value of 66% (vs. 51%, p < 0.001), whereas the sensitivity did not significantly differ between the two exams. In patients with doubtful cystoscopy and negative MRI findings, inflammatory changes were found on TURB in most cases (17/19). The superiority in MRI bladder performance prevailed for "flat lesions", while no significant difference was found for "papillary lesions". CONCLUSIONS: In cases of doubtful cystoscopy after intravesical instillations, MRI appears to be relevant with good performance in differentiating post-therapeutic inflammatory changes from recurrent tumor lesions and could potentially allow avoiding unnecessary TURB.

[Multiple Pulmonary Metastases Seven Years After Transurethral Bladder Tumor Resection for Non-muscle Invasion Bladder Cancer:Report of a Case].

A 64-year-old man was admitted because of multiple pulmonary nodules in right upper lobe on chest computed tomography (CT). He had non-muscle invasion bladder cancer resected by transurethral bladder tumor resection seven years ago. Partial resections of the right upper lobe were carried out at the video assisted thracoscopic surgery (VATS) for diagnostic purposes. The postoperative pathological examination revealed featuraes of pulmonary metastasis of bladder cancer. Although chemotherapy after biopsy was performed, a residual tumor in right S3 has grown. Partial resection of residual tumor was carried out thracoscopically and the tumor was diagnosed as metastasis of bladder cancer. He has been without recurrence for 1 year after the operation.

Recurrent bladder leiomyoma: a case report.

BACKGROUND: Bladder leiomyomas are rare benign growths in the bladder, comprising less than 0.5% of bladder tumors with only 250 cases reported globally. They are more common in women. This case involves a 70-year-old woman with recurrent leiomyoma, presenting with lower urinary tract symptoms and painless hematuria. A recurrent bladder leiomyoma is rarely reported, making its presence exceptionally rare. CASE PRESENTATION: A 70-year-old Pakistani woman with hypertension and diabetes presented with lower urinary tract symptoms (LUTS) and painless hematuria. She had a history of similar symptoms in 2010, leading to a diagnosis of bladder leiomyoma via cystoscopy and biopsy. Imaging studies revealed a substantial 3.7 × 4 × 4.0 cm isodense mass with calcifications at the bladder base, along with bladder wall thickening and diverticula. Pathological examination during Transurethral Resection of Bladder Tumor (TURBT) confirmed the presence of bladder tissue with smooth muscle, ruling out malignancy. Immunohistochemical studies supported the diagnosis. A successful TURBT was performed, and the patient recovered well. DISCUSSION: Recurrent bladder leiomyoma is a rarely-discussed topic in medical literature. This article primarily aims to review existing studies and present a detailed case study, shedding light on this rare phenomenon.

A lightweight bladder tumor segmentation method based on attention mechanism.

In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.

A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

BACKGROUND: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. METHODS: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. RESULTS: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P <0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. CONCLUSIONS: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

Practical Guide to VI-RADS: MRI Protocols, Lesion Characterization, and Pitfalls.

Urothelial carcinoma is the most common type of bladder cancer (BC), accounting for approximately 90% of all cases. Evaluating the depth of tumor invasion in the bladder wall (tumor staging) is essential for determining the treatment and prognosis in patients with BC. Neoadjuvant therapy followed by radical cystectomy is the most common treatment of localized muscle-invasive BC (MIBC). Therefore, it is vital to differentiate non-MIBC from MIBC. Transurethral resection of bladder tumor (TURBT) is the reference standard to determine the extent of tumor invasion into the bladder wall through tissue sampling. However, this diagnostic and therapeutic method may not adequately sample the muscularis propria, leading to a higher risk of residual disease, early recurrence, and tumor understaging in approximately 50% of patients during the initial TURBT. Multiparametric MRI can overcome some of the limitations of TURBT when evaluating BC, particularly regarding tumor staging. In this context, the Vesical Imaging Reporting and Data System (VI-RADS) classification was developed to establish standards for bladder multiparametric MRI and interpretation. It uses a 5-point scale to assess the likelihood of detrusor muscle invasion. T2-weighted MR images are particularly useful as an initial guide, especially for categories 1-3, while the presence of muscular invasion is determined with diffusion-weighted and dynamic contrast-enhanced sequences. Diffusion-weighted imaging takes precedence as the dominant method when optimal image quality is achieved. The presence of a stalk or a thickened inner layer and no evidence of interruption of the signal intensity of the muscular layer are central for predicting a low likelihood of muscle invasion. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Hoegger in this issue.

A novel predict method for muscular invasion of bladder cancer based on 3D mp-MRI feature fusion.

Objective. To assist urologist and radiologist in the preoperative diagnosis of non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), we proposed a combination models strategy (CMS) utilizing multiparametric magnetic resonance imaging.Approach. The CMS includes three components: image registration, image segmentation, and multisequence feature fusion. To ensure spatial structure consistency of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE), a registration network based on patch sampling normalized mutual information was proposed to register DWI and DCE to T2WI. Moreover, to remove redundant information around the bladder, we employed a segmentation network to obtain the bladder and tumor regions from T2WI. Using the coordinate mapping from T2WI, we extracted these regions from DWI and DCE and integrated them into a three-branch dual-channel input. Finally, to fully fuse low-level and high-level features of T2WI, DWI, and DCE, we proposed a distributed multilayer fusion model for preoperative MIBC prediction with five-fold cross-validation.Main results. The study included 436 patients, of which 404 were for the internal cohort and 32 for external cohort. The MIBC was confirmed by pathological examination. In the internal cohort, the area under the curve, accuracy, sensitivity, and specificity achieved by our method were 0.928, 0.869, 0.753, and 0.929, respectively. For the urologist and radiologist, Vesical Imaging-Reporting and Data System score >3 was employed to determine MIBC. The urologist demonstrated an accuracy, sensitivity, and specificity of 0.842, 0.737, and 0.895, respectively, while the radiologist achieved 0.871, 0.803, and 0.906, respectively. In the external cohort, the accuracy of our method was 0.831, which was higher than that of the urologist (0.781) and the radiologist (0.813).Significance. Our proposed method achieved better diagnostic performance than urologist and was comparable to senior radiologist. These results indicate that CMS can effectively assist junior urologists and radiologists in diagnosing preoperative MIBC.

Vesical Imaging-Reporting and Data System (VI-RADS) as a grouping imaging biomarker combined with a decision-tree mode to preoperatively predict the pathological grade of bladder cancer.

AIM: To investigate whether the Vesical Imaging-Reporting and Data System (VI-RADS) could be used to develop a new non-invasive preoperative grade-prediction system to partially predict high-grade bladder cancer (HG-BC). MATERIALS AND METHODS: The present study enrolled 89 primary BC patients prospectively from March 2022 to June 2023. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of VI-RADS for predicting HG-BC and muscle-invasive bladder cancer (MIBC) in the entire group. In the low VI-RADS (≤2) group, the decision tree-based method was used to obtain significant predictors and construct the decision-tree model (DT model). The performance of the DT model and low VI-RADS scores for predicting HG-BC was determined using ROC, calibration, and decision curve analyses. RESULTS: At a cut-off of ≥3, the specificity and positive predictive value of VI-RADS for predicting HG-BC in the entire group was 100%, and the area under the ROC curve (AUC) was 0.697. Among 65 patients with low VI-RADS scores, the DT model showed an AUC of 0.884 in predicting HG-BC compared to 0.506 for low VI-RADS scores. Calibration and decision curve analyses showed that the DT model performed better than the low VI-RADS scores. CONCLUSION: Most VI-RADS scores ≥3 correspond to HG-BCs. VI-RADS could be used as a grouping imaging biomarker for a pathological grade-prediction procedure, which in combination with the DT model for low VI-RADS (≤2) populations, would provide a potential preoperative non-invasive method of predicting HG-BC.