Detection Of Bladder Cancer Research Articles

Abstract A072: Oncuria®-Monitor, a non-invasive test for the detection of recurrent bladder cancer: Cross sectional performance evaluation from a multicenter study

Abstract Up to 70% of patients with non-muscle invasive bladder cancer (NMIBC) experience disease recurrence, making it one of the most prevalent cancers in the US. The purpose of this study was to test the performance of Oncuria®-Monitor, a multiplex urinary biomarker assay for the monitoring of voided urine for recurrent bladder cancer. This cross-sectional, multicenter study included a total of 283 subjects with a history of bladder cancer. Voided urine specimens were collected prior to any procedures for multiplex analysis. The diagnostic performance of the biomarker panel was assessed using receiver operator curves (ROC) and descriptive statistical values (e.g., sensitivity and specificity) and compared with the patients’ clinical data and final diagnosis as defined by the results of cystoscopy. Oncuria®-Monitor achieved an overall sensitivity of 94.3%, specificity of 83.6% and negative predictive valuer (NPV) of 96.2% for the detection of recurrent bladder cancer. Patient summary: We conducted a large cross-sectional clinical study for detecting recurrent bladder cancer with a novel, simple urine test (Oncuria®- Monitor), which measures 10 bladder cancer associated proteins in a single voided urine and can be used to detect recurrent bladder cancer. We analyzed urines from 283 patients and demonstrated that Oncuria®-Monitor can detect recurrent bladder cancer with a high degree of accuracy, specifically in patients with high grade or high stage disease. In conclusion, the novel Oncuria®-Monitor urine test can be used to surveil patients with a history of bladder cancer. Citation Format: Zhen Zhang, Florence Le Calvez-Kelm, Makito Miyake, Ian Pagano, Takuto Shimizu, Sergei Tikhonenkov, Sunao Tanaka, Charles J Rosser, Steve Goodison, Hideki Furuya. Oncuria®-Monitor, a non-invasive test for the detection of recurrent bladder cancer: Cross sectional performance evaluation from a multicenter study [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A072.

Abstract A071: Performance of the Oncuria®-Detect test for the evaluation of patients presenting with hematuria: Results from a real world clinical setting

Abstract Bladder cancer is the 9th most diagnosed cancer worldwide with higher incidences reported in Europe and the United States. Here, we evaluate the performance of a novel, commercially available multiplex immunoassay querying for 10 proteins associated with a bladder cancer diagnostic signature (Oncuria®-Detect, Nonagen Bioscience Corp, Los Angeles, CA, US) for the detection of de novo bladder cancer, in a large prospectively collected real world cohort comprised of 995 patients, across seven centers, presenting with hematuria. The results were compared with the patients’ clinical data and final diagnosis as defined by the results of cystoscopy, with a prevalence of bladder cancer of 15.9%. In the test cohort, Oncuria®-Detect detected bladder tumors in 94/109 cancers, giving overall sensitivity of 86.2% and an overall negative predictive value (NPV) of 96.7% with corresponding specificity of 80.2% with favorable performance characteristics in high grade bladder cancer and muscle-invasive bladder cancer. Patient summary: We conducted a large cross-sectional clinical study for detecting de novo bladder cancer with a novel, simple urine test (Oncuria®-Detect), which measures 10 bladder cancer associated proteins in a single voided urine sample. We analyzed urines from 995 patients and demonstrated that the new urine test can detect bladder cancer with a high degree of accuracy, specifically in patients with high grade or high stage disease. In conclusion, the novel Oncuria®-Detect urine test can be used to help detect new bladder cancers. Citation Format: Ian Pagano, Sergei Tikhonenkov, Florence Le Calvez-Kelm, Steve Goodison, Zhen Zhang, Toru Sakatani, Kaoru Murakami, Howard Kim, Riko Lee, Sunao Tanaka, Charles J Rosser, Hideki Furuya. Performance of the Oncuria®-Detect test for the evaluation of patients presenting with hematuria: Results from a real world clinical setting [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A071.

TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses.

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Abstract A070: Diagnostic accuracy of Oncuria®-Detect in the detection of upper tract urothelial tumors

Abstract Due to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with upper tract urothelial carcinoma. The robust urine-based multiplex assay for diagnosis of upper tract urothelial carcinoma has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis.We previously developedOncuria®-Detect, a multiplex immunoassay capable of detecting bladder cancer using a single voided urine. In this study, its application to upper tract urothelial cancer was evaluated in a multi-institutional cohort of 61 prospectively collected subjects presenting for evaluation of upper tract mass along with 61 prospective collected matched controls. The ability of the test to identify patients harboring upper tract urothelial carcinoma was assessed. Cancer status was confirmed by endoscopy and tissue biopsy or definitive surgery. Oncuria®-Detect provided an AUC of 0.868 (95% CI: 0.802- 0.933) with an overall sensitivity of 96.7%, specificity of 59.0%, NPV 94.7% and PPV 70.2%. Sensitivity values of Oncuria®-Detect for low-grade, high-grade, non-invasive and invasive upper tract urothelial cancer were 100%, 89.7%, 93.3% and 96.2%, respectively. Voided urine cytology showed an overall sensitivity of 54.5%, specificity of 100%, NPV 31.0% and PPV 100%. As a non-invasive test, Oncuria®-Detect outperformed both voided urinary cytology and selective cytology (sensitivities 96.7% vs. 54.5% vs. 81.8%, respectively). Oncuria®-Detect enabled the accurate discrimination of upper tract urothelial carcinoma and controls non-tumor bearing individuals. The Oncuria®-Detect test can achieve the efficient and accurate detection of upper tract urothelial carcinoma in a non-invasive patient setting. Citation Format: Kazutoshi Fujita, Makito Miyake, Takuto Shimizu, Jennifer Linehan, Joshua Gottlieb, Ian Pagano, Sergei Tikhonenkov, Kaoru Murakami, Riko Lee, Toru Sakatani, Sunao Tanaka, Charles J Rosser, Hideki Furuya. Diagnostic accuracy of Oncuria®-Detect in the detection of upper tract urothelial tumors [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A070.

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.

Comparative Study of Preoperative Staging of Bladder Cancer Assessed by Contrast-enhanced Ultrasound and CT

Objective: To investigate the differences and advantages of contrast-enhanced ultrasound and CT in the preoperative staging evaluation of bladder cancer. Methods: 55 patients with bladder cancer admitted to the hospital from March 2023 to March 2024 were selected as study samples. All patients were confirmed by surgical pathology, and CT enhanced scan and contrast-enhanced ultrasound were selected for preoperative staging. The postoperative pathological staging was taken as the gold standard. The accuracy rate of CT and contrast-enhanced ultrasound in the preoperative staging diagnosis of bladder cancer was compared, and ROC curves were drawn to compare the diagnostic value of both in bladder cancer. Results: There was no significant difference in T1, T4 and T3 between contrast-enhanced ultrasound and CT before bladder cancer detection (P &gt; 0.05). The diagnostic coincidence rate of CEUS in T2 stage was higher than that of CT (P &lt; 0.05). ROC curve analysis showed that the sensitivity and specificity of CEUS in diagnosing T-stage bladder cancer were higher than those of CT (P &lt; 0.05). Conclusion: The overall coincidence rate of CEUS in the assessment of bladder cancer before surgery and in the diagnosis of T2 stage bladder cancer is higher than that of CT, and its diagnostic sensitivity and specificity are also higher than that of CT.