Role of Cytoreductive Nephrectomy in Metastatic Clear Cell Renal cell Carcinoma in the Era of immunotherapy: An Analysis of the National Cancer Database.

BACKGROUND: The effectiveness of the clinical outcome of CN (Cytoreductive Nephrectomy) in cases of mccRCC (Metastatic Clear Cell Renal cell Carcinoma) is still uncertain despite two trials, SURTIME and CARMENA. These trials, conducted with Sunitinib as the standard treatment, did not provide evidence supporting the use of CN. METHODS: We queried the NCDB for stage IV mccRCC patients between the years of 2004 to 2020, who received (immunotherapy) IO with or without nephrectomy. Overall survival (OS) was calculated among three groups of IO alone, IO followed by CN (IOCN), CN followed by IO (CNIO). Cox models compared OS by treatment group after adjusting for sociodemographic, health, and facility variables. RESULTS: From 1,549,101 renal cancer cases, 7983 clear and nonclear cell renal cell carcinoma cases were identified. After adjusting for sociodemographic and health covariates, patients who received IO followed by CN or CN followed by IO had a respective 64% (adjusted Hazard Ratio [aHR] = 0.36, 95% CI = 0.30-0.43, P = .006] and 47% (aHR = 0.53, 95% CI = 0.49-0.56, P = .001) mortality risk reduction respectively compared to patients who received IO alone. Compared to White adults, individuals who identified as Black exhibited 17% higher risk mortality (aHR = 1.17, 95% CI = 1.06-1.30, P = .002). Patients who received CN prior to IO had a 59% associated mortality risk compared to patients who received IO followed by CN who had a lower risk, 35.7% (P < .001). CONCLUSIONS: Patients receiving CN regardless of sequence with IO did better than IO alone in this national registry-based adjusted analysis for mccRCC. Presently available data indicates that the combination of CN and IO holds promise for enhancing clinical results in patients with mRCC.

Urachal and Nonurachal Adenocarcinomas of the Urinary Bladder: A Comprehensive Genomic Profiling Study.

PURPOSE: Although both urachal (U) and nonurachal (NU) bladder adenocarcinomas (adenoCas) share several histologic similarities, they differ in location and sometimes in therapeutic options. We analyzed the differences in genomic alterations (GAs) between these tumor entities, with the aim of identifying potential therapeutic targets for clinical trials. MATERIALS AND METHODS: Overall, 133 U and 328 NU adenoCas were analyzed. Hybrid capture-based comprehensive genomic profiling (CGP) was performed to evaluate all classes of GA. Germline status of GA was predicted using a validated somatic-germline computational method. CGP was performed using the FoundationOne and FoundationOne CDx assays (Foundation Medicine, Inc). RESULTS: The most frequent GA in both U and NU cohorts included TP53 (86.5% v 81.1%) and KRAS (34.6% v 27.7%). GAs characteristic of colorectal adenoCa, such as SMAD4 (P = .069) and GNAS (P = .071), were more common in U versus NU. Conversely, TERT (P < .01) and RB1 (P = .071) were more prevalent in NU adenoCa. Notably, both U and NU adenoCas exhibited possibly targetable GA in PIK3CA (7.5% v 7.9%) and ERBB2 (6.8% v 7.6%). Biomarkers associated with potential benefit from anti-PD-1/L1 were infrequent. Median tumor mutational burden was 2.6 and 3.5 mutations per megabase for U and NU, respectively, and PD-L1 expression >1% was rare. Genomic ancestry and genomic signature distribution were similar in both tumor types. GAs were most commonly of somatic nature. Limitations include lack of clinical data, tumor heterogeneity, and retrospective nature. CONCLUSION: U and NU adenoCAs revealed differences in GA, with PIK3CA and ERBB2 being identified as putative therapeutic targets. Biomarkers of response to anti-PD-(L)1 were uncommon. Results highlight the potential of CGP to personalize treatment options of bladder adenoCa and inform clinical trial designs.

Rapid prediction of wall shear stress in stenosed coronary arteries based on deep learning.

There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and time-consuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 s; making this solution time-efficient and clinically relevant.

Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.

The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

Delineating excess comorbidities in idiopathic pulmonary fibrosis: an observational study.

BACKGROUND: Our study examined whether prevalent and incident comorbidities are increased in idiopathic pulmonary fibrosis (IPF) patients when compared to matched chronic obstructive pulmonary disease (COPD) patients and control subjects without IPF or COPD. METHODS: IPF and age, gender and smoking matched COPD patients, diagnosed between 01/01/1997 and 01/01/2019 were identified from the Clinical Practice Research Datalink GOLD database multiple registrations cohort at the first date an ICD-10 or read code mentioned IPF/COPD. A control cohort comprised age, gender and pack-year smoking matched subjects without IPF or COPD. Prevalent (prior to IPF/COPD diagnosis) and incident (after IPF/COPD diagnosis) comorbidities were examined. Group differences were estimated using a t-test. Mortality relationships were examined using multivariable Cox proportional hazards adjusted for patient age, gender and smoking status. RESULTS: Across 3055 IPF patients, 38% had 3 or more prevalent comorbidities versus 32% of COPD patients and 21% of matched control subjects. Survival time reduced as the number of comorbidities in an individual increased (p < 0.0001). In IPF, prevalent heart failure (Hazard ratio [HR] = 1.62, 95% Confidence Interval [CI]: 1.43-1.84, p < 0.001), chronic kidney disease (HR = 1.27, 95%CI: 1.10-1.47, p = 0.001), cerebrovascular disease (HR = 1.18, 95%CI: 1.02-1.35, p = 0.02), abdominal and peripheral vascular disease (HR = 1.29, 95%CI: 1.09-1.50, p = 0.003) independently associated with reduced survival. Key comorbidities showed increased incidence in IPF (versus COPD) 7-10 years prior to IPF diagnosis. INTERPRETATION: The mortality impact of excessive prevalent comorbidities in IPF versus COPD and smoking matched controls suggests that multiorgan mechanisms of injury need elucidation in patients that develop IPF.

Oncolytic adenoviral therapy plus pembrolizumab in BCG-unresponsive non-muscle-invasive bladder cancer: the phase 2 CORE-001 trial.

Cretostimogene grenadenorepvec is a serotype-5 oncolytic adenovirus designed to selectively replicate in cancer cells with retinoblastoma pathway alterations, previously tested as monotherapy in bacillus Calmette-Guérin (BCG)-experienced non-muscle-invasive bladder cancer. In this phase 2 study, we assessed the potential synergistic efficacy between intravesical cretostimogene and systemic pembrolizumab in patients with BCG-unresponsive non-muscle-invasive bladder cancer with carcinoma in situ (CIS). Thirty-five patients were treated with intravesical cretostimogene with systemic pembrolizumab. Induction cretostimogene was administered weekly for 6 weeks followed by three weekly maintenance infusions at months 3, 6, 9, 12 and 18 in patients maintaining complete response (CR). Patients with persistent CIS/high-grade Ta at the 3-month assessment were eligible for re-induction. Pembrolizumab was administered for up to 24 months. The primary endpoint was CR at 12 months as assessed by cystoscopy, urine cytology, cross-sectional imaging and mandatory bladder mapping biopsies. Secondary endpoints included CR at any time, duration of response, progression-free survival and safety. The CR rate in the intention-to-treat population at 12 months was 57.1% (20 out of 35, 95% confidence interval (CI) 40.7-73.5%), meeting the primary endpoint. A total of 29 out of 35 patients (82.9%, 95% CI 70.4-95.3%) derived a CR at 3 months. With a median follow-up of 26.5 months, the median duration of response has not been reached (95% CI 15.7 to not reached). The CR rate at 24 months was 51.4% (18 out of 35) (95% CI 34.9-68.0%). No patient progressed to muscle-invasive bladder cancer in this trial. Adverse events attributed to cretostimogene were low grade, self-limiting and predominantly limited to bladder-related symptoms. A total of 5 out of 35 patients (14.3%) developed grade 3 treatment-related adverse effects. There was no evidence of overlapping or synergistic toxicities. Combination intravesical cretostimogene and systemic pembrolizumab demonstrated enduring efficacy. With a toxicity profile similar to its monotherapy components, this combination may shift the benefit-to-risk ratio for patients with BCG-unresponsive CIS. ClinicalTrials.gov Identifier: NCT04387461 .

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently. Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

Interstitial lung disease: a review of classification, etiology, epidemiology, clinical diagnosis, pharmacological and non-pharmacological treatment.

Interstitial lung diseases (ILDs) refer to a heterogeneous and complex group of conditions characterized by inflammation, fibrosis, or both, in the interstitium of the lungs. This results in impaired gas exchange, leading to a worsening of respiratory symptoms and a decline in lung function. While the etiology of some ILDs is unclear, most cases can be traced back to factors such as genetic predispositions, environmental exposures (including allergens, toxins, and air pollution), underlying autoimmune diseases, or the use of certain medications. There has been an increase in research and evidence aimed at identifying etiology, understanding epidemiology, improving clinical diagnosis, and developing both pharmacological and non-pharmacological treatments. This review provides a comprehensive overview of the current state of knowledge in the field of interstitial lung diseases.

Frequency and Nature of Genomic Alterations in ERBB2-Altered Urothelial Bladder Cancer.

BACKGROUND: Human epidermal growth factor-2 (HER2) overexpression is an oncogenic driver in many solid tumors, including urothelial bladder cancer (UBC). In addition, activating mutations in the ERBB2 gene have been shown to play an oncogenic role similar to ERBB2 amplification. OBJECTIVE: To describe and compare the frequency and nature of genomic alterations (GA) of ERBB2-altered (mutations, amplification) and ERBB2 wild-type UBC. PATIENTS AND METHODS: Using a hybrid capture-based comprehensive profiling assay, 9518 UBC cases were grouped by ERBB2 alteration and evaluated for all classes of genomic alterations (GA), tumor mutational burden (TMB), microsatellite instability (MSI), genome-wide loss of heterozygosity (gLOH), and genomic mutational signature. PD-L1 expression was measured by immunohistochemistry (Dako 22C3). Categorical statistical comparisons were performed using Fisher's exact tests. RESULTS: A total of 602 (6.3%) UBC cases featured ERBB2 extracellular domain short variant (SV) GA (ECDmut+), 253 (2.7%) cases featured ERBB2 kinase domain SV GA (KDmut+), 866 (9.1%) cases had ERBB2 amplification (amp+), and 7797 (81.9%) cases were ERBB2 wild-type (wt). European genetic ancestry of ECDmut+ was higher than ERBB2wt. Numerous significant associations were observed when comparing GA by group. Notably among these, CDKN2A/MTAP loss were more frequent in ERBB2wt versus ECDmut+ and amp+. ERBB3 GA were more frequent in ECDmut+ and KDmut+ than ERBB2wt. TERT GA were more frequent in ECDmut+, KDmut+, and amp+ versus ERBB2wt. TOP2A amplification was significantly more common in ECDmut+ and amp+ versus ERBB2wt, and TP53 SV GA were significantly higher in ERBB2 amp+ versus ERBB2wt. Mean TMB levels were significantly higher in ECDmut+, KDmut+, and amp+ than in ERBB2wt. Apolipoprotein B mRNA-editing enzyme, catalytic polypeptides (APOBEC) signature was more frequent in ECDmut+, KDmut+, and amp+ versus ERBB2wt. No significant differences were observed in PD-L1 status between groups, while gLOH-high status was more common in amp+ versus ERBB2wt. MSI-high status was more frequent in KDmut+ versus ERBB2wt, and in ERBB2wt than in amp+. CONCLUSIONS: We noted important differences in co-occurring GA in ERBB2-altered (ECDmut+, KDmut+, amp+) versus ERBB2wt UBC, as well as higher mean TMB and higher APOBEC mutational signature in the ERBB2-altered groups. Our results can help refine future clinical trial designs and elucidate possible response and resistance mechanisms for ERBB2-altered UBC.

Adaptive multi-interventional trial platform to improve patient care for fibrotic interstitial lung diseases.

BACKGROUND: Fibrotic interstitial lung diseases (fILDs) are a heterogeneous group of lung diseases associated with significant morbidity and mortality. Despite a large increase in the number of clinical trials in the last 10 years, current regulatory-approved management approaches are limited to two therapies that prevent the progression of fibrosis. The drug development pipeline is long and there is an urgent need to accelerate this process. This manuscript introduces the concept and design of an innovative research approach to drug development in fILD: a global Randomised Embedded Multifactorial Adaptive Platform in fILD (REMAP-ILD). METHODS: Description of the REMAP-ILD concept and design: the specific terminology, design characteristics (multifactorial, adaptive features, statistical approach), target population, interventions, outcomes, mission and values, and organisational structure. RESULTS: The target population will be adult patients with fILD, and the primary outcome will be a disease progression model incorporating forced vital capacity and mortality over 12 months. Responsive adaptive randomisation, prespecified thresholds for success and futility will be used to assess the effectiveness and safety of interventions. REMAP-ILD embraces the core values of diversity, equity, and inclusion for patients and researchers, and prioritises an open-science approach to data sharing and dissemination of results. CONCLUSION: By using an innovative and efficient adaptive multi-interventional trial platform design, we aim to accelerate and improve care for patients with fILD. Through worldwide collaboration, novel analytical methodology and pragmatic trial delivery, REMAP-ILD aims to overcome major limitations associated with conventional randomised controlled trial approaches to rapidly improve the care of people living with fILD.

Expectation maximisation pseudo labels.

In this paper, we study pseudo-labelling. Pseudo-labelling employs raw inferences on unlabelled data as pseudo-labels for self-training. We elucidate the empirical successes of pseudo-labelling by establishing a link between this technique and the Expectation Maximisation algorithm. Through this, we realise that the original pseudo-labelling serves as an empirical estimation of its more comprehensive underlying formulation. Following this insight, we present a full generalisation of pseudo-labels under Bayes' theorem, termed Bayesian Pseudo Labels. Subsequently, we introduce a variational approach to generate these Bayesian Pseudo Labels, involving the learning of a threshold to automatically select high-quality pseudo labels. In the remainder of the paper, we showcase the applications of pseudo-labelling and its generalised form, Bayesian Pseudo-Labelling, in the semi-supervised segmentation of medical images. Specifically, we focus on: (1) 3D binary segmentation of lung vessels from CT volumes; (2) 2D multi-class segmentation of brain tumours from MRI volumes; (3) 3D binary segmentation of whole brain tumours from MRI volumes; and (4) 3D binary segmentation of prostate from MRI volumes. We further demonstrate that pseudo-labels can enhance the robustness of the learned representations. The code is released in the following GitHub repository: https://github.com/moucheng2017/EMSSL.

The effect of dietary interventions or patterns on the cardiometabolic health of individuals treated with androgen deprivation therapy for prostate cancer: A systematic review.

Prostate cancer survivors treated with androgen deprivation therapy may be at increased risk of cardiovascular disease. Dietary recommendations for the prevention and/or management of cardiovascular disease for these individuals are lacking. This review synthesizes the evidence on the effect of dietary interventions on cardiometabolic biomarkers and cardiovascular disease risk in prostate cancer survivors receiving androgen deprivation therapy. A systematic review was conducted across PubMed, CINAHL, Embase, and Cochrane CENTRAL. Intervention or observational cohort studies evaluating diets, nutrients, or nutraceuticals with or without concurrent exercise interventions on cardiovascular disease, cardiovascular events, or cardiovascular disease biomarkers in those treated with androgen deprivation therapy were included. Confidence in the body of evidence was appraised using Grading of Recommendations, Assessment, Development and Evaluations. Twelve studies reported across fifteen papers were included. Interventions were heterogenous, with most studies including an exercise co-intervention (n = 8). Few significant findings for the effects of diet on cardiometabolic markers were likely due to weak methodology and sample sizes. Strongest evidence was for the effect of a healthy Western dietary pattern with exercise on improved blood pressure (Confidence: moderate). The healthy Western dietary pattern with exercise may improve high-density lipoprotein cholesterol (Confidence: Low) and flow-mediated dilation. Soy may improve total cholesterol (Confidence: Very low). A low-carbohydrate diet with physical activity may improve high-density lipoprotein cholesterol, incidence of metabolic syndrome, and Framingham cardiovascular disease risk score. Evidence of the effect of dietary interventions on cardiometabolic biomarkers and cardiovascular disease risk of prostate cancer survivors receiving androgen deprivation therapy is insufficient to inform practice. Well-designed dietary interventions aimed at improving cardiometabolic outcomes of this population are warranted to inform future dietary recommendations.

Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients.

Background: The COVID-19 can cause long-term symptoms in the patients after they overcome the disease. Given that this disease mainly damages the respiratory system, these symptoms are often related with breathing problems that can be caused by an affected diaphragm. The diaphragmatic function can be assessed with imaging modalities like computerized tomography or chest X-ray. However, this process must be performed by expert clinicians with manual visual inspection. Moreover, during the pandemic, the clinicians were asked to prioritize the use of portable devices, preventing the risk of cross-contamination. Nevertheless, the captures of these devices are of a lower quality. Objectives: The automatic quantification of the diaphragmatic function can determine the damage of COVID-19 on each patient and assess their evolution during the recovery period, a task that could also be complemented with the lung segmentation. Methods: We propose a novel multi-task fully automatic methodology to simultaneously localize the position of the hemidiaphragms and to segment the lung boundaries with a convolutional architecture using portable chest X-ray images of COVID-19 patients. For that aim, the hemidiaphragms' landmarks are located adapting the paradigm of heatmap regression. Results: The methodology is exhaustively validated with four analyses, achieving an 82.31% ± 2.78% of accuracy when localizing the hemidiaphragms' landmarks and a Dice score of 0.9688 ± 0.0012 in lung segmentation. Conclusions: The results demonstrate that the model is able to perform both tasks simultaneously, being a helpful tool for clinicians despite the lower quality of the portable chest X-ray images.

Imaging of pulmonary fibrosis in children: A review, with proposed diagnostic criteria.

Computed tomography (CT) imaging findings of pulmonary fibrosis are well established for adults and have been shown to correlate with prognosis and outcome. Recognition of fibrotic CT findings in children is more limited. With approved treatments for adult pulmonary fibrosis, it has become critical to define CT criteria for fibrosis in children, to identify patients in need of treatment and those eligible for clinical trials. Understanding how pediatric fibrosis compares with idiopathic pulmonary fibrosis and other causes of fibrosis in adults is increasingly important as these patients transition to adult care teams. Here, we review what is known regarding the features of pulmonary fibrosis in children compared with adults. Pulmonary fibrosis in children may be associated with genetic surfactant dysfunction disorders, autoimmune systemic disorders, and complications after radiation, chemotherapy, transplantation, and other exposures. Rather than a basal-predominant usual interstitial pneumonia pattern with honeycombing, pediatric fibrosis is primarily characterized by reticulation, traction bronchiectasis, architectural distortion, or cystic lucencies/abnormalities. Ground-glass opacities are more frequent in children with fibrotic interstitial lung disease than adults, and disease distribution appears more diffuse, without clearly defined axial or craniocaudal predominance. Following discussion and consensus amongst a panel of expert radiologists, pathologists and physicians, distinctive disease features were integrated to develop criteria for the first global Phase III trial in children with pulmonary fibrosis.

Characterizing the Genomic Landscape of the Micropapillary Subtype of Urothelial Carcinoma of the Bladder Harboring Activating Extracellular Mutations of ERBB2.

The micropapillary subtype of urothelial carcinoma (MPUC) of the bladder is a very aggressive histological variant of urothelial bladder cancer (UBC). A high frequency of MPUC contains activating mutations in the extracellular domain (ECD) of ERBB2. We sought to further characterize ERBB2 ECD-mutated MPUC to identify additional genomic alterations that have been associated with tumor progression and therapeutic response. In total, 5,485 cases of archived formalin-fixed, paraffin-embedded UBC underwent comprehensive genomic profiling to identify ERBB2 ECD-mutated MPUC and evaluate the frequencies of genomic co-alterations. We identified 219 cases of UBC with ERBB2 ECD mutations (74% S310F and 26% S310Y), of which 63 (28.8%) were MPUC. Genomic analysis revealed that TERT, TP53, and ARID1A were the most common co-altered genes in ERBB2-mutant MPUC (82.5%, 58.7%, and 39.7%, respectively) and did not differ from ERBB2-mutant non-MPUC (86.5%, 51.9%, and 35.3%). The main differences between ERBB2 ECD-mutated MPUC compared with non-MPUC were KMT2D, RB1, and MTAP alterations. KMT2D and RB1 are tumor-suppressor genes. KMT2D frequency was significantly decreased in ERBB2 ECD-mutated MPUC (6.3%) in contrast to non-MPUC (27.6%; P < .001). RB1 mutations were more frequent in ERBB2 ECD-mutated MPUC (33.3%) than in non-MPUC (17.3%; P = .012). Finally, MTAP loss, an emerging biomarker for new synthetic lethality-based anticancer drugs, was less frequent in ERBB2 ECD-mutated MPUC (11.1%) than in non-MPUC (26.9%; P = .018). Characterizing the genomic landscape of MPUC may not only improve our fundamental knowledge about this aggressive morphological variant of UBC but also has the potential to identify possible prognostic and predictive biomarkers that may drive tumor progression and dictate treatment response to therapeutic approaches.