CT and MRI features of sarcomatoid urothelial carcinoma of the bladder and its differential diagnosis with conventional urothelial carcinoma.

BACKGROUND: Sarcomatoid urothelial carcinoma (SUC) is a rare and highly malignant form of bladder cancer with a poor prognosis. Currently, there is limited information on the imaging features of bladder SUC and reliable indicators for distinguishing it from conventional urothelial carcinoma (CUC). The objective of our study was to identify the unique imaging characteristics of bladder SUC and determine factors that aid in its differential diagnosis. MATERIALS AND METHODS: This retrospective study enrolled 22 participants with bladder SUC and 61 participants with CUC. The clinical, pathologic, and CT/MRI data from both groups were recorded, and a comparison was conducted using univariate analysis and multinomial logistic regression for distinguishing SUC from CUC. RESULTS: The majority of SUCs were located in the trigone of the bladder and exhibited large tumor size, irregular shape, low ADC values, Vesical Imaging-Reporting and Data System (VI-RADS) score ≥ 4, the presence of necrosis, and an invasive nature. Univariate analysis revealed significant differences in terms of tumor location, shape, the maximum long-axis diameter (LAD), the short-axis diameter (SAD), ADC-value, VI-RADS scores, necrosis, extravesical extension (EVE), pelvic peritoneal spread (PPS), and hydronephrosis/ureteral effusion (p < .001 ~ p = .037) between SUCs and CUCs. Multinomial logistic regression found that only SAD (p = .014) and necrosis (p = .003) emerged as independent predictors for differentiating between SUC and CUC. The model based on these two factors achieved an area under curve (AUC) of 0.849 in ROC curve analysis. CONCLUSION: Bladder SUC demonstrates several distinct imaging features, including a high incidence of trigone involvement, large tumor size, and obvious invasiveness accompanied by necrosis. A bladder tumor with a large SAD and evidence of necrosis is more likely to be SUC rather than CUC.

Three-dimensional imaging of upper tract urothelial carcinoma improves diagnostic yield and accuracy.

Upper tract urothelial carcinoma (UTUC) is a rare form of urothelial cancer with a high incidence of recurrence and a low survival rate. Almost two-thirds of UTUCs are invasive at the time of diagnosis; therefore, improving diagnostic methods is key to increasing survival rates. Histopathological analysis of UTUC is essential for diagnosis and typically requires endoscopy biopsy, tissue sectioning, and labeling. However, endoscopy biopsies are minute, and it is challenging to cut into thin sections for conventional histopathology; this complicates diagnosis. Here, we used volumetric 3-dimensional (3D) imaging to explore the inner landscape of clinical UTUC biopsies, without sectioning, revealing that 3D analysis of phosphorylated ribosomal protein S6 (pS6) could predict tumor grade and prognosis with improved accuracy. By visualizing the tumor vasculature, we discovered that pS6+ cells were localized near blood vessels at significantly higher levels in high-grade tumors than in low-grade tumors. Furthermore, the clustering of pS6+ cells was associated with shorter relapse-free survival. Our results demonstrate that 3D volume imaging of the structural niches of pS6 cells deep inside the UTUC samples improved diagnostic yield, grading, and prognosis prediction.

Pictorial review of multiparametric MRI in bladder urothelial carcinoma with variant histology: pearls and pitfalls.

Bladder cancer (BC), predominantly comprising urothelial carcinomas (UCs), ranks as the tenth most common cancer worldwide. UCs with variant histology (variant UC), including squamous differentiation, glandular differentiation, plasmacytoid variant, micropapillary variant, sarcomatoid variant, and nested variant, accounting for 5-10% of cases, exhibit more aggressive and advanced tumor characteristics compared to pure UC. The Vesical Imaging-Reporting and Data System (VI-RADS), established in 2018, provides guidelines for the preoperative evaluation of muscle-invasive bladder cancer (MIBC) using multiparametric magnetic resonance imaging (mpMRI). This technique integrates T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE)-MRI, and diffusion-weighted imaging (DWI) to distinguish MIBC from non-muscle-invasive bladder cancer (NMIBC). VI-RADS has demonstrated high diagnostic performance in differentiating these two categories for pure UC. However, its accuracy in detecting muscle invasion in variant UCs is currently under investigation. These variant UCs are associated with a higher likelihood of disease recurrence and require precise preoperative assessment and immediate surgical intervention. This review highlights the potential value of mpMRI for different variant UCs and explores the clinical implications and prospects of VI-RADS in managing these patients, emphasizing the need for careful interpretation of mpMRI examinations including DCE-MRI, particularly given the heterogeneity and aggressive nature of variant UCs. Additionally, the review addresses the fundamental MRI reading procedures, discusses potential causes of diagnostic errors, and considers future directions in the use of artificial intelligence and radiomics to further optimize the bladder MRI protocol.

Radiomics-based machine learning approach for the prediction of grade and stage in upper urinary tract urothelial carcinoma: a step towards virtual biopsy.

OBJECTIVES: Upper tract urothelial carcinoma (UTUC) is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards. METHODS: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D. Digital segmentation of the tumours was conducted by extracting textural radiomics features. They were further classified using 11 predictive models. The predicted grades and stages were compared to the histopathology of radical nephroureterectomy specimens. RESULTS: Classifier models worked well in mining the radiomics data and delivered satisfactory predictive machine learning models. The multilayer panel showed 84% sensitivity and 93% specificity while predicting UTUC grades. The Logistic Regression model showed a sensitivity of 83% and a specificity of 76% while staging. Similarly, other classifier algorithms [e.g. Support Vector classifier (SVC)] provided a highly accurate prediction while grading UTUC compared to clinical features alone or ureteroscopic biopsy histopathology. CONCLUSION: Data mining tools could handle medical imaging datasets from small (<2 cm) tumours for UTUC. The radiomics-based machine learning algorithms provide a potential tool to model tumour grading and staging with implications for clinical practice and the upgradation of current paradigms in cancer diagnostics. CLINICAL RELEVANCE: Machine learning based on radiomics features can predict upper tract urothelial cancer grading and staging with significant improvement over ureteroscopic histopathology. The study showcased the prowess of such emerging tools in the set objectives with implications towards virtual biopsy.

Is the excretory phase necessary to identify upper tract urothelial neoplasms at CT urography? A 10-year population-based study.

PURPOSE: To assess the proportion of upper tract urothelial carcinomas (UTUC) that are evident without the excretory phase at CT urography (CTU), and the proportion of potentially avoidable radiation. METHODS: UTUCs diagnosed between January 2008-December 2017 were retrospectively identified from a population-based cancer registry. For each patient, US, non-urographic CT, and MRI exams were assessed for a primary mass and secondary imaging findings (hydronephrosis, urinary tract thickening, luminal distention, fat stranding, and lymphadenopathy/metastatic disease). CTUs were assessed for primary and secondary findings, and whether the tumor was evident as a filling defect on excretory phase. The dose-length product (DLP) of potentially avoidable excretory phases was calculated as a fraction of total DLP. RESULTS: 288 patients (mean age, 72±11 years, 165 males) and 545 imaging examinations were included. Of 192 patients imaged with 370 non-urographic CTs, a primary mass was evident in 154 (80.2%), secondary findings were evident in 172 (89.6%), and primary or secondary findings were evident in 179 (93.2%). Of 175 CTUs, primary and secondary findings were evident in 157 (89.7%) and 166 (94.9%) examinations, respectively, and primary or secondary findings were evident in 170/175 (97.1%). 131/175 (74.9%) UTUCs were evident as a filling defect, including the 5/175 (2.9%) UTUCs without primary or secondary findings. Of 144 CTUs with available DLP data, the proportion of potentially avoidable radiation was 103.7/235.8 (44.0%) Gy⋅cm. CONCLUSION: In our population, almost all UTUCs were evident via primary or secondary imaging findings without requiring the excretory phase. These results support streamlining protocols and pathways.

Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma.

PURPOSE: This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC). METHODS: Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model's performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram. RESULTS: Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993-0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram. CONCLUSION: A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC.

Automated Upper Tract Urothelial Carcinoma Tumor Segmentation During Ureteroscopy Using Computer Vision Techniques.

Introduction: Endoscopic tumor ablation of upper tract urothelial carcinoma (UTUC) allows for tumor control with the benefit of renal preservation but is impacted by intraoperative visibility. We sought to develop a computer vision model for real-time, automated segmentation of UTUC tumors to augment visualization during treatment. Materials and Methods: We collected 20 videos of endoscopic treatment of UTUC from two institutions. Frames from each video (N = 3387) were extracted and manually annotated to identify tumors and areas of ablated tumor. Three established computer vision models (U-Net, U-Net++, and UNext) were trained using these annotated frames and compared. Eighty percent of the data was used to train the models while 10% was used for both validation and testing. We evaluated the highest performing model for tumor and ablated tissue segmentation using a pixel-based analysis. The model and a video overlay depicting tumor segmentation were further evaluated intraoperatively. Results: All 20 videos (mean 36 ± 58 seconds) demonstrated tumor identification and 12 depicted areas of ablated tumor. The U-Net model demonstrated the best performance for segmentation of both tumors (area under the receiver operating curve [AUC-ROC] of 0.96) and areas of ablated tumor (AUC-ROC of 0.90). In addition, we implemented a working system to process real-time video feeds and overlay model predictions intraoperatively. The model was able to annotate new videos at 15 frames per second. Conclusions: Computer vision models demonstrate excellent real-time performance for automated upper tract urothelial tumor segmentation during ureteroscopy.

CT and MRI findings of small cell neuroendocrine carcinoma of the urinary bladder: comparison with urothelial carcinoma.

OBJECTIVE: This study aimed to evaluate the efficacy of CT and MRI findings to differentiate small cell neuroendocrine carcinoma (SCNEC) from urothelial carcinoma (UC) of the urinary bladder. MATERIALS AND METHODS: This study included 90 patients with histopathologically confirmed bladder cancer (10 SCNECs and 80 UCs). Eight patients with bladder SCNEC and 80 with UC underwent CT and MRI, whereas the remaining two patients with SCNEC underwent CT alone before treatment. CT and MRI findings were retrospectively evaluated and compared between the two pathologies. RESULTS: The maximum diameter (36.5 mm vs. 19.0 mm, p < 0.01) and height (22.0 mm vs. 14.0 mm, p < 0.01) of the tumor in bladder SCNEC were higher than in UC. The pedunculated configuration (20% vs. 61%, p < 0.05) and irregular tumor margins (20% vs. 76%, p < 0.01) in bladder SCNEC were less common than in UC. The CT attenuation of the solid component in unenhanced CT images was higher in bladder SCNEC than in UC (37 Hounsfield unit [HU] vs. 34 HU, p < 0.01). The apparent diffusion coefficient (ADC) of the solid component in bladder SCNEC was lower than in UC (0.49 × 10-3 mm2/s vs. 1.02 × 10-3 mm2/s, p < 0.01). CONCLUSION: In comparison with UC, bladder SCNEC was larger, had higher unenhanced CT attenuation, and had a lower ADC value. The pedunculated configuration and irregular tumor margins were typical of bladder UC.

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.

Prognostic predictive value of urothelial carcinoma of the bladder after TURBT based on multiphase CT radiomics.

OBJECTIVE: To investigate multiphase computed tomography (CT) radiomics-based combined with clinical factors to predict overall survival (OS) in patients with bladder urothelial carcinoma (BLCA) who underwent transurethral resection of bladder tumor (TURBT). METHODS: Data were retrospectively collected from 114 patients with primary BLCA from February 2016 to February 2018. The regions of interest (ROIs) of the plain, arterial, and venous phase images were manually segmented. The Cox regression algorithm was used to establish 3 basic models for the plain phase (PP), arterial phase (AP), and venous phase (VP) and 2 combination models (AP + VP and PP + AP + VP). The highest-performing radiomics model was selected to calculate the radiomics score (Rad-score), and independent risk factors affecting patients' OS were analyzed using Cox regression. The Rad-score and clinical risk factors were combined to construct a joint model and draw a visualized nomogram. RESULTS: The combined model of PP + AP + VP showed the best performance with the Akaike Information Criterion (AIC) and Consistency Index (C-index) in the test group of 130.48 and 0.779, respectively. A combined model constructed with two independent risk factors (age and Ki-67 expression status) in combination with the Rad-score outperformed the radiomics model alone; AIC and C-index in the test group were 115.74 and 0.840, respectively. The calibration curves showed good agreement between the predicted probabilities of the joint model and the actual (p < 0.05). The decision curve showed that the joint model had good clinical application value within a large range of threshold probabilities. CONCLUSION: This new model can be used to predict the OS of patients with BLCA who underwent TURBT.

Primary urethral carcinoma: Recommendations of the oncology committee of the French Urology Association.

Primary urethral carcinoma (PUC) is defined as a tumor process arising within the urethra, with no history of other urinary tract localization or synchronous tumor of the urinary tract. The most common histological types are urothelial carcinoma (UC), squamous cell carcinoma (SCC) and adenocarcinoma (AC). In men, UC predominates, while AC is rare. In women, AC affects around one in two patients, while EC and UC are equally divided between the remaining cases. Diagnosis is often delayed, and requires endoscopic examination with biopsies. MRI is the gold standard for local staging. FDG-PET scan can help in cases of doubt about regional or distant extension. The prognosis remains unfavorable despite aggressive surgical treatment. Multimodal management combining surgery, radiotherapy and chemotherapy appears to improve prognosis in severe forms.

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 computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.

OBJECTIVES: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC). METHODS: A total of 140 patients with UTUC who underwent CTU examination from January 2017 to August 2023 were retrospectively enrolled. Tumor lesions on the unenhanced, medullary, and excretory periods of CTU were used to extract Features, respectively. Feature selection was screened by the Pearson and Spearman correlation analysis, least absolute shrinkage and selection operator algorithm, random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost). The logistic regression (LR) was used to screen for independent influencing factors of clinical baseline characteristics. Machine learning models based on different feature datasets were constructed and validated using algorithms such as LR, RF, SVM, and XGBoost. By computing the selected features, a radiomics score was generated, and a diverse feature dataset was constructed. Based on the training set, 16 ML models were created, and their performance was evaluated using the validation set for metrics including sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and others. RESULTS: The training set consisted of 98 patients (mean age: 64.5 ± 10.5 years; 30 males), whereas the validation set consisted of 42 patients (mean age: 65.3 ± 9.78 years; 17 males). Hydronephrosis was the best independent influence factor (p < 0.05). The RF model had the best performance in predicting high-grade UTUC, with AUC of 0.914 (95% Confidence Interval [95%CI] 0.852-0.977) and 0.903 (95%CI 0.809-0.997) in the training set and validation set, and accuracy of 0.878 and 0.857, respectively. CONCLUSIONS: An ML model based on the RF algorithm exhibits excellent predictive performance, offering a non-invasive approach for predicting preoperative high-grade UTUC.

CT findings and clinical characteristics in distinguishing renal urothelial carcinoma mimicking renal cell carcinoma from clear cell renal cell carcinoma.

BACKGROUND: We aimed to characterize the clinical and multiphase computed tomography (CT) features, which can distinguish renal urothelial carcinoma (RUC) mimicking renal cell carcinoma (RCC) from clear cell renal cell carcinoma (ccRCC) with collecting system invasion (CSI). METHODS: Data from 56 patients with RUC (46 men and 10 women) and 366 patients with ccRCC (262 men and 104 women) were collected and assessed retrospectively. The median age was 65.50 (IQR: 56.25-69.75) and 53.50 (IQR: 42.25-62.5) years, respectively. Univariate and multivariate logistic regression analyses were performed on clinical and CT characteristics to determine independent factors for distinguishing RUC and ccRCC, and an integrated predictive model was constructed. Differential diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC). RESULTS: The independent predictors for differentiating RUC from ccRCC were infiltrative growth pattern, hydronephrosis, heterogeneous enhancement, preserving reniform contour, and hematuria. The differential diagnostic performance of the integrated predictive model-1 (AUC: 0.947, sensitivity: 89.07%, specificity: 89.29%) and model-2 (AUC: 0.960, sensitivity: 92.1%, specificity: 89.3%) were both better than that of the infiltrative growth pattern (AUC: 0.830, sensitivity: 71.9%, specificity: 92.9%), heterogeneous enhancement (AUC: 0.771, sensitivity: 86.3%, specificity: 67.9%), preserving reniform contour (AUC = 0.758, sensitivity: 85.5%, specificity: 66.1%), hydronephrosis (AUC: 0.733, sensitivity: 87.7%, specificity: 58.9%), or hematuria (AUC: 0.706, sensitivity: 79.5%, specificity: 51.8%). CONCLUSION: The CT and clinical characteristics showed extraordinary discriminative abilities in the differential diagnosis of RUC and ccRCC, which might provide helpful information for clinical decision-making.

MRI for risk stratification of muscle invasion by upper tract urothelial carcinoma: a feasibility study.

BACKGROUND: Magnetic resonance imaging (MRI) is recommended in patients with upper tract urothelial carcinoma (UTUC) only when computed tomography (CT) is contraindicated. However, CT does not allow distinguishing ureter wall layers, making impossible to assess muscle invasion, a factor contributing to differentiate high- from low-risk UTUCs, which require different therapeutic approaches. We investigated the feasibility of MRI assessment of UTUC muscle invasion. METHODS: From June 2022 to March 2023, we prospectively enrolled patients suspected of UTUC, i.e., with positive urinary tract ultrasound and/or ureteroscopy, or positive urinary cytology and/or hematuria but negative cystoscopy and bladder ultrasound at two Italian centers. They underwent CT followed by MRI (≤ 24 h apart), independently reported by two experienced radiologists, blinded from histopathology results. After imaging confirmation, they all underwent nephroureterectomy and histopathology analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS: Thirty-nine lesions were detected in 30 patients on both CT and MRI. Muscle-invasive UTUC prevalence was 81% (21/26) among patients with MRI suspicion and 8% (1/13) among those without MRI suspicion (p < 0.001). Considering the assessment of muscle-layer invasion, the more experienced reader achieved 95% sensitivity (95% confidence interval 82-100), 71% specificity (47-88), 81% PPV (63-93), 92% NPV (70-100), 85% accuracy (67-96), and 0.84 AUC (0.70-0.98). Inter-reader agreement was substantial (κ = 0.73). CONCLUSIONS: MRI showed a promising diagnostic performance for the assessment of UTUC risk of muscle invasion. RELEVANCE STATEMENT: Resulting feasible both in technical and clinical terms, MRI could be helpful for upper tract urothelial carcinomas pre-operative risk stratification, to allow a personalized patients' management. These results play in favor of promoting preoperative MRI for UTUC, as already proven for bladder cancer. KEY POINTS: • Muscle invasion is a crucial information for tailored treatments of upper tract urothelial carcinomas. • CT does not distinguish ureter wall layers, making muscle invasion risk assessment not feasible. • MRI was shown to reliably diagnose muscle-layer invasion by upper tract urothelial carcinomas (sensitivity 95%, specificity 71%).

Application of contrast-enhanced ultrasound in diagnosis and grading of bladder urothelial carcinoma.

PURPOSE: To explore the application of contrast-enhanced ultrasound (CEUS) for the diagnosis and grading of bladder urothelial carcinoma (BUC). METHODS: The results of a two-dimensional ultrasound, color Doppler ultrasound and CEUS, were analyzed in 173 bladder lesion cases. The ultrasound and surgical pathology results were compared, and their diagnostic efficacy was analyzed. RESULTS: There were statistically significant differences between BUC and benign lesions in terms of color blood flow distribution intensity and CEUS enhancement intensity (both P < 0.05). The area under the time-intensity curve (AUC), rising slope, and peak intensity of BUC were significantly higher than those of benign lesions (all P < 0.05). The H/T (height H / basal width T)value of 0.63 was the critical value for distinguishing high- and low-grade BUC, had a diagnostic sensitivity of 80.0% and a specificity of 60.0%. CONCLUSION: The combination of CEUS and TIC can help improve the diagnostic accuracy of BUC. There is a statistically significant difference between high- and low-grade BUC in contrast enhancement intensity (P < 0.05); The decrease of H/T value indicates the possible increase of the BUC grade.

PET/Computed Tomography Transformation of Oncology: Kidney and Urinary Tract Cancers.

Renal cell carcinoma (RCC) and urothelial carcinoma (UC) are two of the most common genitourinary malignancies. 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) can play an important role in the evaluation of patients with RCC and UC. In addition to the clinical utility of 18F-FDG PET to evaluate for metastatic RCC or UC, the shift in molecular imaging to focus on specific ligand-receptor interactions should provide novel diagnostic and therapeutic opportunities in genitourinary malignancies. In combination with the rise of artificial intelligence, our ability to derive imaging biomarkers that are associated with treatment selection, response assessment, and overall patient prognostication will only improve.

Biparametric versus Multiparametric Magnetic Resonance Imaging for Assessing Muscle Invasion in Bladder Urothelial Carcinoma with Variant Histology Using the Vesical Imaging-Reporting and Data System.

BACKGROUND: The diagnostic performance of contrast medium-free biparametric magnetic resonance imaging (bpMRI; combining T2-weighted imaging [T2WI] and diffusion-weighted imaging [DWI]) for evaluating variant-histology urothelial carcinoma (VUC) remains unknown. OBJECTIVE: To compare the diagnostic performance of bpMRI and multiparametric MRI (mpMRI; combining T2WI, DWI, and dynamic contrast-enhanced MRI]) for assessing muscle invasion of VUC. DESIGN, SETTING, AND PARTICIPANTS: This multi-institution retrospective analysis included 118 patients with pathologically verified VUC who underwent bladder mpMRI before transurethral bladder tumor resection between 2010 and 2019. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Three board-certified radiologists separately evaluated two sets of images, set 1 (bpMRI) and set 2 (mpMRI), in accordance with the Vesical Imaging Reporting and Data System (VI-RADS). The histopathology results were utilized as a reference standard. Receiver operating characteristic curve analysis, Z test, and Wald test were used to assess diagnostic abilities. RESULTS AND LIMITATIONS: Sixty-six (55.9%) and 52 (44.1%) of the 118 patients with VUC included in the analysis (mean age, 71 ± 10 yr; 88 men) had muscle-invasive bladder cancer (MIBC) and non-MIBC, respectively. For the diagnosis of MIBC, the areas under the curve for bpMRI were significantly smaller than those for mpMRI (0.870-0.884 vs 0.902-0.923, p < 0.05). The sensitivity of bpMRI was significantly lower than that of mpMRI for all readers with a VI-RADS cutoff score of 4 (65.2-66.7% vs 77.3-80.3%, p < 0.05). The specificity of bpMRI and mpMRI did not differ significantly for all readers (88.5-90.4 vs 88.5-92.3, p > 0.05). A limitation of the study is the limited sample size because of the rarity of VUC. CONCLUSIONS: In patients with VUC, on applying VI-RADS, the diagnostic results of bpMRI were inferior to those of mpMRI for evaluating muscle invasion. Therefore, mpMRI-based methods are recommended for evaluating muscle invasiveness of VUC. PATIENT SUMMARY: Contrast medium-free biparametric magnetic resonance imaging (bpMRI)-based Vesical Imaging Reporting and Data System (VI-RADS) can accurately diagnose pure urothelial carcinomas, similar to conventional multiparametric magnetic resonance imaging-based VI-RADS. However, bpMRI-based VI-RADS may misdiagnose muscle invasiveness of urothelial carcinoma with variant histology, particularly when its cutoff score is 4.

Lymph Node Dissection in Upper Tract Urothelial Carcinoma: Current Status and Future Perspectives.

PURPOSE OF REVIEW: This narrative review aims to evaluate the role of lymph node dissection (LND) in upper tract urothelial carcinoma (UTUC) and its implications for staging and management outcomes, as well as future perspectives. RECENT FINDINGS: Multiple studies have demonstrated the limitations of conventional imaging techniques in accurately localizing lymph node metastasis (LNM) in UTUC. While 18F-fluorodeoxyglucose positron emission tomography with computed tomography (18FDG-PET/CT) shows promise for preoperative LNM detection, its specificity is low. Alternative methods such as choline PET/CT and sentinel lymph node detection are under consideration but require further investigation. Additionally, various preoperative factors associated with LNM hold potential for predicting nodal involvement, thereby improving nodal staging and oncologic outcomes of LND. Several surgical approaches, including segmental ureterectomy and robot-assisted nephroureterectomy, provide a possibility for LND, while minimizing morbidity. LND remains the primary nodal staging tool for UTUC, but its therapeutic benefit is still uncertain. Advances in imaging techniques and preoperative risk assessment show promise in improving LNM detection. Further research and multi-center studies are needed to comprehensively assess the advantages and limitations of LND in UTUC, as well as the long-term outcomes of alternative staging and treatment strategies.

Pedal acrometastasis secondary to urothelial carcinoma masquerading as Charcot arthropathy in a patient with diabetes.

A woman in her 80s with known diabetes mellitus and bladder cancer presented to her general practitioner (GP) with pain and swelling in her left foot following trauma. Initial radiographs were reported as normal, prompting a diagnosis of a simple sprain and conservative management. Three months later, the patient was referred to the orthopaedic team due to progressively increasing pain and swelling. Repeat X-rays revealed lytic lesions in both the talus and navicular bones; MRI confirmed the presence of a lytic and proliferative defect in the mid-foot, which was reported as acute Charcot arthropathy with superimposed infection. This was also considered the most likely diagnosis when imaging was reviewed in two separate multidisciplinary team) meetings. However, biopsy demonstrated that the cause of the presentation was in fact acrometastasis from urothelial carcinoma, an infrequently described entity.