BTA stat®, NMP22® BladderChek®, UBC® Rapid Test, and CancerCheck® UBC® rapid VISUAL as urinary marker for bladder cancer: Final results of a German multicenter study

Liquid Biopsy Based Bladder Cancer Diagnostic by Machine Learning

Background/Objectives: The timely diagnostics of bladder cancer is still a challenge in clinical settings. The reliability of conventional testing methods does not reach desirable accuracy and sensitivity, and it has an invasive nature. The present study examines the application of machine learning to improve bladder cancer diagnostics by integrating miRNA expression levels, demographic routine laboratory test results, and clinical data. We proposed that merging these datasets would enhance diagnostic accuracy. Methods: This study combined molecular biology methods for liquid biopsy, routine clinical data, and application of machine learning approach for the acquired data analysis. We evaluated urinary exosome miRNA expression data in combination with patient test results, as well as clinical and demographic data using three machine learning models: Random Forest, SVM, and XGBoost classifiers. Results: Based solely on miRNA data, the SVM model achieved an ROC curve area of 0.75. Patient analysis' clinical and demographic data obtained ROC curve area of 0.80. Combining both data types enhanced performance, resulting in an F1 score of 0.79 and an ROC of 0.85. The feature importance analysis identified key predictors, including erythrocytes in urine, age, and several miRNAs. Conclusions: Our findings indicate the potential of a multi-modal approach to improve the accuracy of bladder cancer diagnosis in a non-invasive manner.

Development of point-of-care tests for urinary bladder cancer - an historic review and view to future prospectives

Urine is an attractive biospecimen for noninvasive tests to facilitate bladder tumor diagnostics. Three different point-of-care (POC) tests based on lateral flow immunoassays (LFAs) are currently commercially available: UBC® Rapid Test, BTA stat®, and NMP22TM BladderChek. The present review discusses these different tests based on their performance, clinical utility and the nature of the respective analytes. The level of sensitivities of UBC Rapid Test® and BTA stat® for detection of high-grade nonmuscle invasive bladder cancer using urine is in the order of 80%. Estimations of performance are highly dependent on patient selection criteria. UBC® Rapid Test shows a sensitivity of approximately 85% in patients presenting with macrohematuria which is the most common initial clinical symptom. Estimations of specificity are complicated by differences in how control groups are selected in different studies and are therefore more difficult to compare between published reports. Different POC tests differ with regard to the source of the analytes that are measured. The BTA Stat® test is based on detection of plasma proteins (Factor H/Factor H-related proteins), potentially leading to a lack of specificity during conditions of renal dysfunction. A large number of analytes to be used for urine-based bladder cancer tests have been described in the literature, including cytokines and proteases implicated in tumor invasion. These proteins, although biologically relevant, are often present at very low levels in urine that may be unsuitable for development of LFAs. Release of abundant intracellular structural proteins from cells such as cytokeratins (UBC® Rapid Test) and nuclear matrix proteins (NMP22TM) may therefore be advantageous. We conclude that available data support the use of urine-based POC tests as adjuncts during the clinical work up of suspected bladder cancer.

[Urothelial carcinoma of the upper and lower urinary tract-which risk factors make early detection worthwhile?]

Urothelial carcinoma is one of the most common malignancies both in Germany and worldwide. Due to the frequent occurrence of late-onset or nonspecific symptoms, carcinomas are often diagnosed at advanced stages. Structured early detection programs have the potential to detect urothelial carcinoma in earlier stages and to improve survival rates. Various risk factors are associated with urothelial carcinoma, most notably tobacco abuse and occupational exposure, as well as genetic disorders such as Lynch syndrome in upper urinary tract carcinoma. In clinical practice, diagnostic tools include general examinations and ultrasound imaging of the urinary tract, with microhematuria and urine cytology playing key roles. For screening purposes noninvasive urine markers have demonstrated limited evidence. Despite the lack of optimal diagnostic markers for systematic early detection in high-risk populations, it is essential to ensure that every patient presenting with hematuria undergoes appropriate and risk-adapted diagnostics.

Detection of Human Bladder Epithelial Cancerous Cells with Atomic Force Microscopy and Machine Learning

The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique's capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection.

Diagnostic performance of Uromonitor and TERTpm ddPCR urine tests for the non-invasive detection of bladder cancer

Uromonitor and urinary telomerase reverse transcriptase promoter mutation droplet digital PCR (uTERTpm ddPCR) are non-invasive tests designed to detect bladder cancer in urine. We aimed to compare the diagnostic performance of uTERTpm ddPCR, Uromonitor and urine cytology in detecting bladder cancer. Urine samples were collected prospectively from patients diagnosed with primary (n = 74) and recurrent bladder cancer (n = 20) or benign urological conditions (n = 48) prior to surgical resection. The samples were tested for bladder cancer via uTERTpm ddPCR, Uromonitor and urine cytology. The sensitivity, specificity, and predictive values were calculated for each test, including confidence intervals. The results were stratified by low-grade non-muscle-invasive bladder cancer, high-grade non-muscle-invasive bladder cancer and muscle-invasive bladder cancer. Compared with urine cytology (59.5%, p = 0.005) and Uromonitor (56.8%, p = 0.001), the uTERTpm ddPCR test had the highest sensitivity (79.7%) for the detection of primary bladder cancer. Specificity did not significantly differ. The uTERTpm ddPCR test exhibited superior diagnostic performance over urine cytology and Uromonitor, highlighting its potential for non-invasive primary bladder cancer diagnosis.

New Challenges in Bladder Cancer Diagnosis: How Biosensing Tools Can Lead to Population Screening Opportunities

Bladder cancer is one of the most common cancers worldwide. Despite its high incidence, cystoscopy remains the currently used diagnostic gold standard, although it is invasive, expensive and has low sensitivity. As a result, the cancer diagnosis is mostly late, as it occurs following the presence of hematuria in urine, and population screening is not allowed. It would therefore be desirable to be able to act promptly in the early stage of the disease with the aid of biosensing. The use of devices/tools based on genetic assessments would be of great help in this field. However, the genetic differences between populations do not allow accurate analysis in the context of population screening. Current research is directed towards the discovery of universal biomarkers present in urine with the aim of providing an approach based on a non-invasive, easy-to-perform, rapid, and accurate test that can be widely used in clinical practice for the early diagnosis and follow-up of bladder cancer. An efficient biosensing device may have a disruptive impact in terms of patient health and disease management, contributing to a decrease in mortality rate, as well as easing the social and economic burden on the national healthcare system. Considering the advantage of accessing population screening for early diagnosis of cancer, the main challenges and future perspectives are critically discussed to address the research towards the selection of suitable biomarkers for the development of a very sensitive biosensor for bladder cancer.

Microsatellite Instability in Urine: Breakthrough Method for Bladder Cancer Identification

Bladder cancer (BC) is the most common neoplasm of the urinary system and ranks tenth in global cancer incidence. Due to its high recurrence rate and the need for continuous monitoring, it is the cancer with the highest cost per patient. Cystoscopy is the traditional method for its detection and surveillance; however, this is an invasive technique, while non-invasive methods, such as cytology, have a limited sensitivity. For this reason, new non-invasive strategies have emerged, analyzing useful markers for BC detection from urine samples. The identification of tumor markers is essential for early cancer detection and treatment. Urine analysis offers a non-invasive method to identify these markers. Microsatellite instability (MSI) has been proposed as a promising marker for tumor cell detection and guided targeted therapies. Therefore, this review aims to explore the evidence supporting the identification of MSI in exfoliated bladder tumor cells (EBTCs) in the urine, emphasizing its potential as a non-invasive and clinically effective alternative for tumor identification. Furthermore, establishing clinical guidelines is crucial for standardizing its application in oncological screening and validating its clinical utility.

The UF-5000 Atyp.C parameter is an independent risk factor for bladder cancer

Bladder carcinoma (BC) accounts for > 90% of all urothelial cancers. Pathological diagnosis through cytoscopic biopsy is the gold standard, whereas non-invasive diagnostic tools remain lacking. The "Atyp.C" parameter of the Sysmex UF-5000 urine particle analyzer represents the ratio of nucleus to cytoplasm and can be employed to detect urinary atypical cells. The present study examined the association between urinary Atyp.C values and BC risk. This two-center, retrospective case-control study identified clinical primary or newly recurrent BC (study period, 2022-2023; n = 473) cases together with controls with urinary tract infection randomly matched by age and sex (1:1). Urinary sediment differences were compared using non-parametric tests. The correlations between urinary Atyp.C levels and BC grade or infiltration were analyzed using Spearman's rank correlation. The BC risk factor odds ratio of Atyp.C was calculated using conditional logistic regression, and potential confounder effects were adjusted using stepwise logistic regression (LR). Primary risk factors were identified by stratified analysis according to pathological histological diagnosis. The mean value of urinary Atyp.C in BC cases (1.30 ± 3.12) was 8.7 times higher than that in the controls (0.15 ± 0.68; P < 0.001). Urinary Atyp.C values were positively correlated with BC pathological grade and invasion (r = 0.360, P < 0.001; r = 0.367, P < 0.001). Urinary Atyp.C was an independent risk factor for BC and closely related with BC pathological grade and invasion. Elevated urinary Atyp.C values was an independent risk factor for BC. Our findings support the use of Atyp.C as a marker that will potentially aid in the early diagnosis and long-term surveillance of new and recurrent BC cases.

Urine-Based Biomarker Test Uromonitor® in the Detection and Disease Monitoring of Non-Muscle-Invasive Bladder Cancer-A Systematic Review and Meta-Analysis of Diagnostic Test Performance

Optimal urine-based diagnostic tests (UBDT) minimize unnecessary follow-up cystoscopies in patients with non-muscle-invasive bladder-cancer (NMIBC), while accurately detecting high-grade bladder-cancer without false-negative results. Such UBDTs have not been comprehensively described upon a broad, validated dataset, resulting in cautious guideline recommendations. Uromonitor®, a urine-based DNA-assay detecting hotspot alterations in TERT, FGFR3, and KRAS, shows promising initial results. However, a systematic review merging all available data is lacking. Studies investigating the diagnostic performance of Uromonitor® in NMIBC until November 2023 were identified in PubMed, Embase, Web-of-Science, Cochrane, Scopus, and medRxiv databases. Within aggregated analyses, test performance and area under the curve/AUC were calculated. This project fully implemented the PRISMA statement. Four qualifying studies comprised a total of 1190 urinary tests (bladder-cancer prevalence: 14.9%). Based on comprehensive analyses, sensitivity, specificity, positive-predictive value/PPV, negative-predictive value/NPV, and test accuracy of Uromonitor® were 80.2%, 96.9%, 82.1%, 96.6%, and 94.5%, respectively, with an AUC of 0.886 (95%-CI: 0.851-0.921). In a meta-analysis of two studies comparing test performance with urinary cytology, Uromonitor® significantly outperformed urinary cytology in sensitivity, PPV, and test accuracy, while no significant differences were observed for specificity and NPV. This systematic review supports the use of Uromonitor® considering its favorable diagnostic performance. In a cohort of 1000 patients with a bladder-cancer prevalence of ~15%, this UBDT would avert 825 unnecessary cystoscopies (true-negatives) while missing 30 bladder-cancer cases (false-negatives). Due to currently limited aggregated data from only four studies with heterogeneous quality, confirmatory studies are needed.