Detection Of Bladder Cancer Research Articles

Diagnostic accuracy of urinary cytokeratin fragment-19 (CYFRA21-1) for bladder cancer

Bladder cancer is known for its high recurrence rate and requires constant patient monitoring. To confirm the diagnosis, a tissue sample from a cystoscopy is required, which the patient often avoids. Urine has the potential to be utilized as a diagnostic fluid because of its non-invasive nature and various biomarker contents. The aim of this study was to determine the diagnostic value of cytokeratin fragment-19 (CYFRA21-1) level in urine for diagnosing bladder cancer. This single-center cross-sectional study was performed with eligible inclusion were adults aged ≥18 years who presented with hematuria and suspected bladder cancer from imaging. Patients with a history of intravesical chemotherapy, radiotherapy and immunotherapy were excluded. Urine samples were collected prior to the cystoscopy. Detection of urinary CYFRA21-1 was carried out using the ELISA method. Of 154 patients included in the study, the diagnosis of bladder cancer was confirmed in 92 patients. Patients with bladder cancer had significantly higher urinary CYFRA21-1 levels compared to the non-bladder cancer group. The sensitivity, specificity, positive and negative predictive value, and positive likelihood ratio of the CYFRA21-1 were 80.4%, 43.5%, 67.9%, 60% and 1.425, respectively. The area under the curve for CYFRA21-1 was 0.608, computed from a receiver operating curve with a cut-off value of 13.3 ng/mL. In conclusion, urinary CYFRA21-1 levels have moderate diagnostic accuracy in determining bladder cancer among suspected individuals. Due to its high sensitivity, this biomarker could potentially be used alongside other screening tools for bladder cancer detection.

Machine Learning-Based Detection of Bladder Cancer by Urine cfDNA Fragmentation Hotspots that Capture Cancer-Associated Molecular Features.

cfDNA fragmentomics-based liquid biopsy is a potential option for noninvasive bladder cancer (BLCA) detection that remains an unmet clinical need. We assessed the diagnostic performance of cfDNA hotspot-driven machine-learning models in a cohort of 55 BLCA patients, 51 subjects with benign conditions, and 11 healthy volunteers. We further performed functional bioinformatics analysis for biological understanding and interpretation of the tool's diagnostic capability. Urinary cfDNA hotspots-based machine-learning model enabled effective BLCA detection, achieving high performance (area under curve 0.96) and an 87% sensitivity at 100% specificity. It outperformed models using other cfDNA-derived features. In stage-stratified analysis, the sensitivity at 100% specificity of the urine hotspots-based model was 71% and 92% for early (low-grade Ta and T1) and advanced (high-grade T1 and muscle-invasive) disease, respectively. Biologically, cfDNA hotspots effectively retrieved regulatory elements and were correlated with the cell of origin. Urine cfDNA hotspots specifically captured BLCA-related molecular features, including key functional pathways, chromosome loci associated with BLCA risk as identified in genome-wide association studies, or presenting frequent somatic alterations in BLCA tumors, and the transcription factor regulatory landscape. Our findings support the applicability of urine cfDNA fragmentation hotspots for noninvasive BLCA diagnosis, as well as for future translational study regarding its molecular pathology and heterogeneity.

Adenomatous Polyposis Coli (APC) Promoter Gene Methylation in Urine-Derived DNA: A Non-invasive Biomarker for Early Bladder Cancer Detection and Tumor Aggressiveness

Adenomatous Polyposis Coli (APC) Promoter Gene Methylation in Urine-Derived DNA: A Non-invasive Biomarker for Early Bladder Cancer Detection and Tumor Aggressiveness

Bladder Cancer detection by urinary methylation markers GHSR/MAL: a validation study

PurposeAlthough cystoscopy is a reliable tool for detecting bladder cancer, it poses a high burden on patients and entails high costs. This highlights the need for non-invasive and cost-effective alternatives. This study aimed to validate a previously developed urinary methylation marker panel containing GHSR and MAL.MethodsWe enrolled 134 patients who underwent cystoscopy because of hematuria, including 63 individuals with primary bladder cancer and 71 with non-malignant findings. Urine samples were self-collected at home and sent via regular mail. Subsequently, DNA was extracted and the hypermethylation of GHSR and MAL was evaluated using quantitative methylation-specific polymerase chain reaction. The performance of methylation markers was assessed using area-under-the-curve (AUC) analysis and sensitivity and specificity based on pre-established cut-off values.ResultsValidation of the marker panel GHSR/MAL resulted in an AUC of 0.87 at 79% sensitivity and 80% specificity. Sensitivity was comparable to the previous investigation (P > 0.9), though specificity was significantly lower (P = 0.026). Sensitivity was higher for high-grade tumors compared to low-grade tumors (94% vs. 60%, P = 0.002).ConclusionValidation of the GHSR/MAL methylation marker panel on at home collected urine samples confirms its robust performance for bladder cancer detection in a hematuria population, and underscores the diagnostic potential for future clinical application.

Can artificial intelligence aid the urologists in detecting bladder cancer?

ABSTRACT Introduction: The emergence of artificial intelligence (AI)-based support system endoscopy, including cystoscopy, has shown promising results by training deep learning algorithms with large datasets of images and videos. This AI-aided cystoscopy has the potential to significantly transform the urological practice by assisting the urologists in identifying malignant areas, especially considering the diverse appearance of these lesions. Methods: Four databases, the PubMed, ProQuest, EBSCOHost, and ScienceDirect were searched, along with a manual hand search. Prospective and retrospective studies, experimental studies, cross-sectional studies, and case–control studies assessing the utilization of AI for the detection of bladder cancer through cystoscopy and comparing with the histopathology results as the reference standard were included. The following terms and their variants were used: “artificial intelligence,” “cystoscopy,” and “bladder cancer.” The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A random effects model was used to calculate the pooled sensitivity and specificity. The Moses–Littenberg model was used to derive the Summary Receiver Operating Characteristics (SROC) curve. Results: Five studies were selected for the analysis. Pooled sensitivity and specificity were 0.953 (95% confidence interval [CI]: 0.908–0.976) and 0.957 (95% CI: 0.923–0.977), respectively. Pooled diagnostic odd ratio was 449.79 (95% CI: 12.42–887.17). SROC curve (area under the curve: 0.988, 95% CI: 0.982–0.994) indicated a strong discriminating power of AI-aided cystoscopy in differentiation normal or benign bladder lesions from the malignant ones. Conclusions: Although the utilization of AI for aiding in the detection of bladder cancer through cystoscopy remains questionable, it has shown encouraging potential for enhancing the detection rates. Future studies should concentrate on identification of the patients groups which could derive maximum benefit from accurate identification of the bladder cancer, such as those with intermediate or high-risk invasive tumors.

Screening Model for Bladder Cancer Early Detection With Serum miRNAs Based on Machine Learning: A Mixed-Cohort Study Based on 16,189 Participants.

Early detection of bladder cancer (BCa) can have a positive impact on patients' prognosis. However, there is currently no widely accepted method for early screening of BCa. We aimed to develop an efficient, clinically applicable, and noninvasive method for the early screening of BCa by detecting specific serum miRNA levels. A mixed-cohort (including BCa, 12 different other cancers, benign disease patients, and health population) study was conducted using a sample size of 16,189. Five machine learning algorithms were utilized to develop screening models for BCa using the training dataset. The performance of the model was evaluated using receiver operating characteristic curve and decision curve analysis on the testing dataset, and subsequently, the model with the best predictive power was selected. Furthermore, the selected model's screening performance was evaluated using both the validation set and external set. The BCaS3miR model, utilizing only three serum miRNAs (miR-6087, miR-1343-3p, and miR-5100) and based on the KNN algorithm, is the superior screening model chosen for BCa. BCaS3miR consistently performed well in both the testing, validation, and external sets, exceeding 90% sensitivity and specificity levels. The area under the curve was 0.990 (95% CI: 0.984-0.991), 0.964 (95% CI: 0.936-0.984), and 0.917 (95% CI: 0.836-0.953) in the testing, validation, and external set. The subgroup analysis revealed that the BCaS3miR model demonstrated outstanding screening accuracy in various clinical subgroups of BCa. In addition, we developed a BCa screening scoring model (BCaSS) based on the levels of miR-1343-3p/miR-6087 and miR-5100/miR-6087. The screening effect of BCaSS is investigated and the findings indicate that it has predictability and distinct advantages. Using a mixed cohort with the largest known sample size to date, we have developed effective screening models for BCa, namely BCaS3miR and BCaSS. The models demonstrated remarkable screening accuracy, indicating potential for the early detection of BCa.

In vivo self-assembled bispecific fluorescence probe for early detection of bladder cancer and metastasis

In vivo self-assembled bispecific fluorescence probe for early detection of bladder cancer and metastasis

Using artificial intelligence for bladder cancer detection during cystoscopy and its impact on clinical outcomes: a protocol for a systematic review and meta-analysis

IntroductionCystoscopy has revolutionised the process of diagnosing bladder cancer leading to better categorisation of risk levels and more precise treatment plans. Nonetheless, concerns arise about the lack of uniformity among...

Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer

Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer

Letter to the editor for the article “Pilot study for bladder cancer detection with volatile organic compounds using ion mobility spectrometry: a novel urine-based approach”

Letter to the editor for the article “Pilot study for bladder cancer detection with volatile organic compounds using ion mobility spectrometry: a novel urine-based approach”

Automated gall bladder cancer detection using artificial gorilla troops optimizer with transfer learning on ultrasound images

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer.

Analysis of urine cell-free DNA in bladder cancer diagnosis by emerging bioactive technologies and materials.

Urinary cell-free DNA (UcfDNA) is gaining recognition as an important biomarker for diagnosing bladder cancer. UcfDNA contains tumor derived DNA sequences, making it a viable candidate for non-invasive early detection, diagnosis, and surveillance of bladder cancer. The quantification and qualification of UcfDNA have demonstrated high sensitivity and specificity in the molecular characterization of bladder cancer. However, precise analysis of UcfDNA for clinical bladder cancer diagnosis remains challenging. This review summarizes the history of UcfDNA discovery, its biological properties, and the quantitative and qualitative evaluations of UcfDNA for its clinical significance and utility in bladder cancer patients, emphasizing the critical role of UcfDNA in bladder cancer diagnosis. Emerging bioactive technologies and materials currently offer promising tools for multiple UcfDNA analysis, aiming to achieve more precise and efficient capture of UcfDNA, thereby significantly enhancing diagnostic accuracy. This review also highlights breakthroughs in detection technologies and substrates with the potential to revolutionize bladder cancer diagnosis in clinic.

P06-04 Advancing bladder cancer detection: unveiling a novel urinary biomarker panel through metabolomics

P06-04 Advancing bladder cancer detection: unveiling a novel urinary biomarker panel through metabolomics

Urinary Free Glycosaminoglycans Identify Adults at High Risk of Developing Early-stage High-grade Bladder Cancer

Background and objectiveScreening for bladder cancer (BCa) could reduce mortality via early detection of early-stage high-grade (Ta/T1 N0 M0 grade 2–3) disease. Noninvasive biomarkers could aid in screening, but current markers lack the specificity required. The urinary free glycosaminoglycan profile (GAGome) is a promising biomarker for early detection of BCa metabolism. MethodsIn a prospective case-control development study, we included patients with BCa or no evidence of disease (NED) and measured the urinary GAGome. We then developed a score to predict the probability of BCa using GAGome features that correlated with BCa versus NED according to Bayesian regression. Next, in a retrospective, population-based, case-control study, we included adults from the Lifelines Cohort Study who were presumed healthy at baseline. All cases with BCa confirmed in the cancer registry by the 2-yr or 6-yr study visit were matched to randomly selected control subjects. We developed a reference logistic regression model using age and sex to predict BCa at 7 yr after baseline. We then added the GAGome score to the model and assessed model improvement using the likelihood ratio test. We dichotomized outputs for the reference model and saturated model (reference + GAGome score) into high-risk versus low-risk categories using a 99% specificity cutoff and estimated the sensitivity for association with BCa at 7 yr. Key findings and limitationsWe prospectively included 51 individuals with BCa and 38 with NED and observed alterations in three GAGome features compatible with BCa. We developed a score that discriminated BCa with an area under the receiver operating characteristic curve of 0.77 (95% confidence interval [CI] 0.67–0.87). We retrospectively selected a cohort of 1088 presumed healthy adults (median age 48 yr, 56% females), of whom 48 had developed BCa by 7 yr after baseline (median time to diagnosis 1.4 yr). The GAGome score was an independent predictor of BCa at 7 yr when added to the reference model (p < 0.001). The sensitivity for BCa at 7 yr for high-risk subjects was 31% (95% CI 20–43%) using the saturated model and 17% (95% CI 4.7–29%) using the reference model at 99% specificity (95% CI 98–99%). Conclusions and clinical implicationsThe urinary free GAGome is specifically altered in BCa and can be used for noninvasive identification of adults at high risk of developing BCa, independent of age and sex. This information could be useful for the design of risk-stratified targeted screening programs for BCa. Patient summaryWe tested whether measurement of a class of sugars called glycosaminoglycans (GAGs) in urine could be used for early detection of bladder cancer. Our results show that GAG levels in urine can distinguish people at high risk of developing bladder cancer within 7 years, even if they are healthy at the time of the urine sampling.

Role of circular RNAs (CircANXA2 and CircFBXW7) in bladder cancer

BackgroundThe most prevalent malignancy in the urinary tract is bladder cancer, which is becoming more commonplace globally. Circular RNAs, or circRNAs, have a role in cancer pathogenesis as they can be used as biomarkers to help identify new cases and predict the likelihood of recurrence. The current work examined the gene expression of CircANXA2 and CircFBXW7 and evaluated their potential utility in bladder cancer diagnosis.Subjects and methodologySeventy people with benign urologic diseases, seventy people with bladder cancer, and seventy healthy controls participated in the study. The polymerase chain reaction in real time quantified the gene expressions of CircANXA2 and CircFBXW7.ResultsCompared to the benign group and controls, bladder cancer patients had significantly greater levels of CircANXA2 expression (p < 0.001). In contrast to the benign group and controls, bladder cancer patients had significantly decreased levels of CircFBXW7 expression (p < 0.001). CircANXA2 and CircFBXW7 have comparable sensitivity and specificity; CircANXA2 had sensitivity, and specificity (77.14% and 76.43) and CircFBXW7 had sensitivity, and specificity (80.0% and 70.0%) respectively in detecting bladder cancer. CircANXA2 and CircFBXW7 were found to be the risk variables for bladder cancer using multivariate analysis. The odds ratios (OR) for CircANXA2 and CircFBXW7 were 1.240 (1.135–1.354) and 0.026 (0.006–0.107), in that order. In conclusion, CircANXA2 and CircFBXW7 may be highly specific and sensitive diagnostic markers for bladder cancer.

The upregulation of POLR3G correlates with increased malignancy of bladder urothelium

Bladder cancer remains a significant health challenge due to its high recurrence and progression rates. This study aims to evaluate the role of POLR3G in the development and progression of bladder cancer and the potential of POLR3G to serve as a novel therapeutic target. We constructed a bladder cancer model in Wistar rats by administering N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN), which successfully induced a transition from normal mucosa to hyperplasia and ultimately to urothelial carcinoma. We observed a progressive upregulation of POLR3G expression during the bladder cancer development and progression. To investigate the functional role of POLR3G, we performed functional experiments in bladder cancer cell lines. The results demonstrated that knocking down POLR3G significantly inhibited cell proliferation, migration, and invasion. We further conducted RNA sequencing on POLR3G-knockdown bladder cancer cells, and Metascape was employed to perform the functional enrichment analysis of the differentially expressed genes (DEGs). Enrichment analysis revealed the enrichment of DEGs in the RNA polymerase and apoptotic cleavage of cellular proteins pathways, as well as their involvement in the Wnt and MAPK signaling pathways. The downregulation of Wnt pathway-related proteins such as Wnt5a/b, DVL2, LRP-6, and phosphorylated LRP-6 upon POLR3G knockdown was further confirmed by Western blotting, indicating that POLR3G might influence bladder cancer behavior through the Wnt signaling pathway. Our findings suggest that POLR3G plays a crucial role in bladder cancer progression and could serve as a potential therapeutic target. Future studies should focus on the detailed mechanisms by which POLR3G regulates these signaling pathways and its potential as a biomarker for early detection and prognosis of bladder cancer.

Advanced optical imaging techniques for bladder cancer detection and diagnosis: a systematic review.

To systematically assess the current available literature concerning advanced optical imaging methods for the detection and diagnosis of bladder cancer (BCa), focusing particularly on the sensitivity and specificity of these techniques. First a scoping search was performed to identify all available optical techniques for BCa detection and diagnosis. The optical imaging techniques used for detecting BCa are: the Storz professional image enhancement system (IMAGE1 S), narrow-band imaging (NBI), photoacoustic imaging (PAI), autofluorescence imaging (AFI), photodynamic diagnosis (PDD), and scanning fibre endoscopy (SFE). The staging and grading techniques for BCa are: optical coherence tomography (OCT), confocal laser endomicroscopy (CLE), Raman spectroscopy, endocytoscopy, and non-linear optical microscopy (NLO). Then a systematic literature search was conducted using MEDLINE, EMBASE and Web of Science from inception to 21 November 2023. Articles were screened and selected by two independent reviewers. Inclusion criteria were: reporting on both the sensitivity and specificity of a particular technique and comparison to histopathology, and in the case of a detection technique comparison to white light cystoscopy (WLC). Out of 6707 articles, 189 underwent full-text review, resulting in 52 inclusions. No articles met criteria for IMAGE1 S, PAI, SFE, Raman spectroscopy, and endocytoscopy. All detection techniques showed higher sensitivity than WLC, with NBI leading (87.8-100%). Overall, detection technique specificity was comparable to WLC, with PDD being most specific (23.3-100%). CLE and OCT varied in sensitivity and specificity, with OCT showing higher specificity for BCa diagnosis, notably for carcinoma in situ (97-99%) compared to CLE (62.5-81.3%). NLO demonstrated high sensitivity and specificity (90-97% and 77-100%, respectively) based on limited data from two small ex vivo studies. Optical techniques with the most potential are PDD for detecting and OCT for staging and grading BCa. Further research is crucial to validate their integration into routine practice and explore the value of other imaging techniques.

SLC16A3 is a Prognostic Marker and Affects Immune Regulation in Bladder Cancer.

Overexpression of SLC16A3 can contribute to the development of various tumors by regulating metabolism, but a systematic analysis of SLC16A3 in bladder cancer (BC) has been rarely reported. We used the BC datasets from public databases to investigate SLC16A3 expression in BC. We first analysed the relationship between SLC16A3 expression and clinical characteristics of 412 bladder cancer patients. After that, gene function analyses and immunocorrelation analyses of SLC16A3 were conducted with the R package. For immunotherapy effect and drug sensitivity analysis, we also used the R package. We also analysed the relation between SLC16A3 expression and 20 m6A modification key genes. Finally, we determined the expression localization of SLC16A3 in bladder cancer by single-cell sequencing analysis using 3,115 BC cells. We further detected the expression of SLC16A3/MCT4 on BC samples by reversed transcriptionquantitative polymerase chain reaction and immunohistochemistry. The SLC16A3 was overexpressed in BC cells, including epithelial cells (p＜0.001). The high SLC16A3 expression level of patients with BC was significantly related to poor prognosis (p＝0.044), and we established a reliable prognosis model for BC patients. Statistically significant associations between SLC16A3 and m6A modification (ALKBH5) gene (p＜0.001), key genes in aerobic glycolysis, M2 macrophage infiltration (p＝0.0058), and immune checkpoint regulation were observed. Overexpression of SLC16A3 is an independent prognostic factor in patients with BC. SLC16A3 may influence the immune infiltration of BC by regulating BC metabolism and m6A methylation, which ultimately can lead to the progress of BC. For the detection and therapy of BC, SLC16A3 may be a potent therapeutic target for BC.

Serum and urine apolipoprotien A1 (ApoA1) as biomarkers in bladder cancer

Bladder cancer (BC) is the predominant malignant neoplasm in the urinary system and ranks as the tenth most prevalent malignant tumor worldwide. Compared with females, males displayed a four-fold more common incidence of bladder cancer. It mainly affects men. Bladder cancer is the fourth most prevalent neoplasm in males. The most important protein that makes up high density lipoprotein (HDL), ApoA-I apolipoprotein A1 is essential in regulating the right amount of cholesterol. Multiple inquiries have demonstrated that APOA1 plays a pivotal role in the progression, infiltration, and spread of tumors. Objectives. The objective of this study was to measure the level of urine to serum apolipoprotein A1 in patients suffering from bladder cancer and investigate the impact of variations to this nanotechnology on the development of the cancer. Material and methods. The study collected 45 blood and urine samples from individuals diagnosed with bladder cancer at Ghazi Hariri Hospital for Specialized Surgery. The samples included both males and females of various ages (61.47±11.28 years). Additionally, 45 blood and urine samples were collected from individuals without the disease. The samples were analyzed using an ELISA method to measure the levels of apolipoprotein A1 in the serum and urine of both groups, and the data collection period spanned from January 2023 to June 2023. Result. The average levels of serum and urine apolipoprotein A1 in the patients' group (14.18±2.62ng/ml, 20.04±4.67ng/ml) were significantly higher than the average levels in the control group (8.21±1.35, 8.94±1.74), with p-values of &lt;0.001 and ≤0.001, respectively. The blood concentration of apolipoprotein A1 showed a significant positive moderate connection with the concentration of apolipoprotein A1 in urine (r=0.45, p&lt;0.001). Conclusion. The mean and SD of serum apolipoprotein A1 in the patients group were higher than control group and urine apolipoprotein A1 in the patients group were higher than control group can be utilized as biomarkers for detecting bladder cancer. However, urine apolipoprotein A1 is a superior biomarker compared to serum apolipoprotein A1 due to its association with several other diseases.

Real-world performance of Uromonitor® in urothelial bladder cancer detection: a multicentric trial.

To compare Uromonitor® (U-Monitor Lda, Porto, Portugal), a multitarget DNA assay that detects mutated proto-oncogenes (telomerase reverse transcriptase [TERT], fibroblast growth factor receptor 3 [FGFR-3], Kirsten rat sarcoma viral oncogene homologue [KRAS]), with urine cytology in the urine-based diagnosis of urothelial carcinoma of the bladder (UCB) within a multicentre real-world setting. This multicentre, prospective, double-blind study was conducted across four German urological centres from 2019 to 2024. We evaluated the diagnostic performance of Uromonitor compared to urine cytology in a cohort of patients with UCB and in healthy controls within a real-world setting. Sensitivity, specificity, positive-predictive value (PPV), negative-predictive value (NPV), and accuracy of the tests were measured, in addition to multivariate analyses to assess the ability of individual proto-oncogene mutations in detecting UCB. The biometric sample size was designed to achieve a 10% difference in sensitivity. Patients with UCB comprised 63.7% (339/532) of the study group. Uromonitor showed a sensitivity, specificity, PPV, NPV, accuracy, and an area-under-the-curve of 49.3%, 93.3%, 92.8%, 51.1%, 65.2%, and 0.713%, respectively. These metrics did not demonstrate statistical superiority over urine cytology in terms of sensitivity (44.6%; P = 0.316). Moreover, the comparison of additional test parameters, as well as the comparison within various sensitivity analyses, yielded no significant disparity between the two urinary tests. Multivariate logistic regression underscored the significant predictive value of a positive Uromonitor for detecting UCB (odds ratio [OR] 9.03; P < 0.001). Furthermore, mutations in TERT and FGFR-3 were independently associated with high odds of UCB detection (OR 13.30 and 7.04, respectively), while KRAS mutations did not exhibit predictive capability. Despite its innovative approach, Uromonitor fell short of confirming the superior results anticipated from previous studies in this real-world setting. The search for an optimal urine-based biomarker for detecting and monitoring UCB remains ongoing. Results from this study highlight the complexity of developing non-invasive diagnostic tools and emphasise the importance of continued research efforts to refine these technologies.