Next Article in Journal
Important Requirements for Desorption/Ionization Mass Spectrometric Measurements of Temozolomide-Induced 2′-Deoxyguanosine Methylations in DNA
Next Article in Special Issue
Development and Internal Validation of a Risk Prediction Model to Identify Myeloma Based on Routine Blood Tests: A Case-Control Study
Previous Article in Journal
The Use of Artificial Intelligence in the Diagnosis and Classification of Thyroid Nodules: An Update
Previous Article in Special Issue
Factors Associated with the Breast Cancer Diagnostic Interval across Five Canadian Provinces: A CanIMPACT Retrospective Cohort Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 15
Issue 3
10.3390/cancers15030709
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis

by
Evie Papavasiliou
1,*,
Valerie A. Sills
1,
Natalia Calanzani
1,
Hannah Harrison
2,
Claudia Snudden
1,
Erica di Martino
3,
Andy Cowan
1,
Dawnya Behiyat
1,
Rachel Boscott
1,
Sapphire Tan
1,
Jennifer Bovaird
4,
Grant D. Stewart
5,
Fiona M. Walter
1,6 and
Yin Zhou
1
1
The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
2
Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
3
Division of Primary Care, Public Health & Palliative Care, Leeds Institute of Health Sciences, University of Leeds, Leeds LS2 3AA, UK
4
Patient & Public Representative c/o The Primary Care Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
5
Department of Surgery, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
6
Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry Queen Mary University of London, London EC1M 6BQ, UK
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(3), 709; https://doi.org/10.3390/cancers15030709
Submission received: 3 January 2023 / Revised: 13 January 2023 / Accepted: 17 January 2023 / Published: 24 January 2023
(This article belongs to the Special Issue Cancer Detection in Primary Care)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

Bladder cancer (BC) is one of the most common cancers worldwide. Early-stage diagnosis is associated with better survival rates and, as such, the timely referral of suspected cases is paramount. Urinary biomarkers have been developed to aid diagnosis, and are largely tested in patients who have been referred for further investigation. Evidence, however, on their diagnostic performance for both detecting and ruling out BC, especially in the general population, is limited. In this review, we systematically identified studies reporting on the diagnostic performance of biomarkers suitable for use in primary and community care settings. Three biomarkers, with relatively little difference in diagnostic performance between them, and some novel biomarkers were identified showing potential to be used as a triage tool in such settings. While promising, further validation studies in the general population are needed.

Abstract

Evidence on the use of biomarkers to detect bladder cancer in the general population is scarce. This study aimed to systematically review evidence on the diagnostic performance of biomarkers which might be suitable for use in community and primary care settings [PROSPERO Registration: CRD42021258754]. Database searches on MEDLINE and EMBASE from January 2000 to May 2022 resulted in 4914 unique citations, 44 of which met inclusion criteria. Included studies reported on 112 biomarkers and combinations. Heterogeneity of designs, populations and outcomes allowed for the meta-analysis of three biomarkers identified in at least five studies (NMP-22, UroVysion, uCyt+). These three biomarkers showed similar discriminative ability (adjusted AUC estimates ranging from 0.650 to 0.707), although for NMP-22 and UroVysion there was significant unexplained heterogeneity between included studies. Narrative synthesis revealed the potential of these biomarkers for use in the general population based on their reported clinical utility, including effects on clinicians, patients, and the healthcare system. Finally, we identified some promising novel biomarkers and biomarker combinations (N < 3 studies for each biomarker/combination) with negative predictive values of ≥90%. These biomarkers have potential for use as a triage tool in community and primary care settings for reducing unnecessary specialist referrals. Despite promising emerging evidence, further validation studies in the general population are required at different stages within the diagnostic pathway.

1. Introduction

Bladder cancer is the tenth most commonly diagnosed cancer worldwide, with 573,000 new cases and 213,000 deaths in 2020, ranking 14th in terms of cancer-associated mortality [1]. About two-thirds of patients with bladder cancer present with haematuria, a cardinal symptom for urological tract cancers including bladder cancer [2]. However, only visible haematuria has a high positive predictive value for bladder cancer, with pooled incidence reported to be as high as 17–18% in some populations [3]. Patients presenting with non-visible haematuria and other urological symptoms, such as lower urinary tract symptoms, may cause diagnostic challenges. This is because these symptoms are common in the general population and are more likely to be due to benign causes rather than cancer [4]. Identifying tools to improve the diagnostic pathway may improve diagnostic timeliness, and therefore outcomes, for patients with bladder cancer.
Empirical evidence suggests that there is scope to improve timely diagnosis and reduce missed diagnostic opportunities in symptomatic patients who are subsequently diagnosed with bladder cancer [5]. The diagnostic pathway for bladder cancer involves a combination of investigations, from urine tests in the community and primary care to specialist investigations such as upper urinary tract imaging (including ultrasound and Computed Tomography (CT) scans), urine cytology and cystoscopy. The latter remains the gold standard for bladder cancer detection in patients investigated following haematuria [6,7]. Disadvantages of these tests, such as the poor sensitivity of ultrasounds, radiation exposure associated with CT, and the invasiveness of cystoscopy, can limit their use in the general population [8]. There is, therefore, an urgent need to identify new approaches for improving risk stratification of symptomatic patients to improve the early detection of bladder cancer and reduce the burden of unnecessary investigations for patients.
Urinary biomarkers have been developed to aid detection and early diagnosis of urinary tract cancers including bladder cancer [9]. These have largely been tested in patients presenting with symptoms suggestive of cancer and referred for further investigation, and who are therefore at a higher-than-average risk of having an undetected cancer. Several reviews have been conducted focusing on individual biomarkers, biomarker panels or certain biomarker categories (e.g., proteins) seeking to describe and explore their diagnostic performance and clinical utility for bladder cancer detection [10,11,12,13]. These findings demonstrate that urinary biomarkers have the potential to improve current diagnostic strategies. However, it is not clear exactly when and how they should be used along the diagnostic pathway.
This review aimed to update the evidence on existing biomarkers for bladder cancer detection, their diagnostic performance across different population groups, and explore their clinical utility in the populations and settings studied. The main focus was to identify novel biomarkers for bladder cancer detection that might be suitable for use in the general population in community and primary care settings, often the first point of contact for patients in the healthcare system, hereafter referred to jointly as community settings. For the purpose of this review, general population refers to patients with any baseline risk at the point of presentation, in contrast with patients referred to specialist settings, who are already at a higher risk of cancer.

2. Methods

This review has been conducted in line with the guidance provided in the Cochrane Handbook for Systematic Reviews of Interventions [14] and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) Guidelines [15] (Figure S1 in Supplementary Materials). The Review Protocol has been registered on PROSPERO (registration ID: CRD42021258754).

2.1. Data Sources

MEDLINE and EMBASE were systematically searched for studies reporting on primary data published in peer-reviewed journals between 1 January 2000 and 24 May 2022. Based on initial pilot searches and findings from previous reviews showing that most studies on novel biomarkers have been published since 2005, 2000 was set as the start date/year of database searches. The search strategy was developed in consultation with an experienced subject librarian. Data sources were supplemented by hand-searching of reference lists of included studies.

2.2. Inclusion & Exclusion Criteria

A comprehensive list of inclusion and exclusion criteria to guide searches and study selection (Table S1 in Supplementary Materials) was developed using the PICOS Framework (Population, Intervention, Comparators/Context, Outcomes and Study type) [16]. Any studies i. involving adult patients presenting with clinical signs or symptoms suggestive of bladder cancer, undergoing evaluation but not having received a diagnosis; ii. comprising at least 50 bladder cancer and 50 non-bladder cancer patients; iii. recruiting participants through any healthcare system and setting; iv. reporting on at least one measure of diagnostic performance of biomarkers (either individual, multiple/panels or combinations), were included. We were interested in any biomarker feasible to use in community and primary care settings, i.e., identified in non-invasive samples such as blood (serum or plasma), urine, feces, saliva or breath.
This review was informed by the CanTest Framework, a 5-phase translational pathway for diagnostic tests, from new test discovery to health system implementation in low-prevalence populations [17]. Only studies providing measures of diagnostic accuracy beyond discovery/development (that is, Phase 2 and beyond of this framework) were included (Figure S2 in Supplementary Materials). As papers were set in different healthcare systems and settings, a diagram was developed to guide decisions on inclusion (adapted from Olesen et al. 2009) [18], using a cut-off point/boundary in the diagnostic pathway of a typical cancer patient. Studies were included up to the point where patients were referred for a first specialist visit but not diagnosed (see Figure 1).

2.3. Search Strategy

The search strategy (Table S2 in Supplementary Materials) included a list of key words and database specific subject headings (MeSH or Emtree) relating to each of the main target domains: biomarkers, performance measures, early diagnosis, and bladder cancer, combined and tailored to the relevant database. Searches were limited to include only outputs published from January 2000 onwards. No restrictions on language or methodological design were applied.

2.4. Study Selection

Following deduplication in EndNote 20 (Clarivate Analytics), unique citations were imported to Rayyan—Intelligent Systematic Review software [19]. Title and abstracts were independently screened against inclusion/exclusion criteria by two reviewers (any two of NC/CS/VS, and EP). Full texts of potentially eligible papers identified during title and abstract screening were independently assessed by EP and one other reviewer (VS/AC/CS/EdM/RB/DB/ST/HH/YZ). Any disagreements were resolved through discussion and consensus.

2.5. Data Extraction

Data extraction was performed using Microsoft Excel 2015. A data extraction template was developed to record information on study details, validation overview, biomarker characteristics, performance measures including measures for comparison to urine cytology (the standard non-invasive urological evaluation to diagnose bladder cancer), and suitability for community use. When studies reported on different phases of biomarker development, data were extracted only for eligible phases (i.e., biomarkers and measures beyond the discovery phase).
Data extraction was piloted for 10% of the included studies (EP and DB/RB) and the data extraction template was revised accordingly to ensure consistency and accuracy of the information extracted. Data extraction for the remaining 90% of studies was carried out independently by two reviewers (EP and VS/AC/CS/EdM/RB/DB/ST/HH/YZ).

2.6. Quality Assessment

QUADAS-2, a tool designed to assess the quality of primary diagnostic studies, was used to assess the risk of bias and applicability of included studies [20]. The tool covers four domains: i. patient selection; ii. index test; iii. reference standards; iv. flow and timing. Each domain comprises a series of signaling questions, aimed at identifying areas of potential bias or concerns over applicability, rated as “high”, “low” or “unclear”. Quality assessment was performed independently by two reviewers (EP and VS/AC/CS/EdM/RB/DB/ST/HH/YZ). Quality assessment ratings were compared, and any inconsistencies observed were resolved through discussion and consensus.

2.7. Data Analysis

Forest plots of accuracy measures (sensitivity and specificity) were produced for all biomarkers investigated in three studies or more, using the mada package in R [21]. For the biomarkers with their performance reported in five or more studies, a meta-analysis was conducted to calculate the Hierarchal Summary Receiver Operating Curve (HSROC) using the bivariate random-effects approach developed by Reitsma et al. 2005 [22] with a linear mixed model [23].
Narrative synthesis, selected for its potential to assess and synthesize heterogeneous and complex evidence in a rigorous and replicable way [24,25,26,27], was also performed for the meta-analysis biomarkers. Reported authors conclusions/recommendations were deductively synthesized, being thematically aligned to effects on patients and clinicians (focusing on acceptability, benefits, and harms), and effects on health care systems (focusing on referral patterns and costs), adapted from Phases 3 to 5 from the CanTest Framework (Figure S2 in Supplementary Materials). Narrative synthesis was also used to assess and synthesize diagnostic performance of biomarkers examined in fewer than three studies but with a reported high negative predictive value (NPV ≥ 90.0%). This additional analysis aimed to identify novel biomarkers for bladder cancer detection that might be suitable for use in the general population, as a high NPV can provide reassurance that cancer is unlikely to be present [28,29].

3. Results

3.1. Selection Process

Electronic database searches retrieved 6638 records, of which, following deduplication, title and abstract screening, 98 were assessed in full text for eligibility. Fifty-five records were excluded for either being outside the focus of the review (N = 8) or reporting on: (i). discovery-only findings (N = 8); (ii). numbers of cancer/non-cancer cases that were inadequate, unclear, or not possible to calculate (N = 24); (iii). populations already diagnosed with bladder cancer or under surveillance for bladder cancer recurrence (N = 14). One study was excluded due to integrity concerns (N = 1). One additional record was added, following the manual searching of reference lists of included studies, leading to a final sample of 44 studies included in the review (Figure 2). No studies were excluded during quality assessment.

3.2. Study Characteristics

Included studies (N = 44 publications) originated from all continents (Table 1), predominantly Europe (N = 17) including Germany (N = 5), the UK (N = 4), Spain (N = 2), the Netherlands (N = 2), Belgium (N = 1), Denmark (N = 1), France (N = 1), and Sweden/Spain/Netherlands (N = 1). Ten studies (N = 10) originated from Africa, all of which were conducted in Egypt. Six studies (N = 6) were carried out in Asia, including five in China (N = 5) and one (N = 1) in Pakistan. There were four studies (N = 4) originating from the USA and two studies (N = 2) from Australia and/or New Zealand. Finally, for five studies (N = 5), information on country of research was not available. Studies were largely prospective (N = 34) and/or single-centered (N = 24), with different study designs including cohort (N = 15), trials (N = 3), cross-sectional (N = 2), case–control (N = 2), observational (N = 2), and evaluation (N = 1) studies. For nineteen studies (N = 19), there was no information on study design. The most common recruitment setting was in the hospital (N = 29) whereas four studies (N = 4) recruited from more than one setting (i.e., hospital and community). No information on recruitment setting was available for eleven studies (N = 11).

3.3. Risk of Bias

Potential sources of bias were identified in all four domains of QUADAS-2 during quality assessment. Flow and timing was the domain in which most studies were assessed as being at high risk (N = 33) for failing to include all recruited patients in their analysis or not specifying whether there was an appropriate interval between index test(s) (the diagnostic test(s) that is/are evaluated against the reference standard) and reference standards (the best available method of determining whether people have a condition). Key sources of bias identified in studies classified as high risk for index test (N = 30) and patient selection (N = 22) included failing to pre-define test thresholds and failing to avoid inappropriate exclusions or not specifying the type of sampling employed. In terms of reference standard, the risk of bias was assessed as being unclear (N = 25) in most studies due to no information being provided on whether the results of the reference standard were interpreted without knowledge of the results of the index test. Concerns over applicability were not identified for any of the included studies (Figure S3 in Supplementary Materials).

3.4. Population Characteristics

Included studies (N = 44) reported on 28,527 participants including 9780 patients with cancer and 18,747 non-cancer patients (Table 1). Based on information from studies in which gender (N = 34) and age (N = 34) were reported, participants were predominantly male (63%), ranging from 18 to 110 years of age, with two studies including minors, one aged 14 and one 15 included as outliers in large samples. Of the 18,747 non-cancer patients, 9611 were further specified to include 2285 normal/healthy patients and 7326 with non-malignant or pre-malignant conditions such as cystitis, urolithiasis, dysuria, urethral stricture, and prostate hyperplasia. No such information was available for the remaining 9136 non-cancer patients. Clinical signs and/or symptoms at first presentation included haematuria (either visible or non-visible) reported in twenty-seven (N = 27) studies, and non-malignant or pre-malignant conditions reported in twenty-three (N = 23) studies. Finally, risks factors identified included smoking (N = 13), ethnicity/race (N = 4) and schistosomiasis, as well as an acute and chronic parasitic disease associated with bladder cancer (N = 9), reported in eighteen (N = 18) studies. Four (N = 4) studies reported on non-cancer patients with benign bladder tumors, a history of bladder cancer or bladder cancer diagnosis (included as outliers in large samples according to the set inclusion/exclusion criteria), these being considered as risk factors to developing bladder cancer.

3.5. Biomarker Characteristics

Included studies (N = 44) reported on 112 biomarkers (37 individual, 34 multiple/panels and 41 combinations) including biomarker/s and cytology OR biomarker/s and base models OR biomarkers/s and imaging (Table 2). Ninety-six of them (N = 96) were reported in only one study. In terms of category, 52 biomarkers were classified as proteins (including single proteins, combinations of proteins and combinations of proteins with prediction models, cytology, and other tests), 36 as DNAs and 18 as mRNAs (all following the same pattern as in proteins). There were also nine biomarkers combining proteins and mRNAs and six biomarkers combining proteins and DNAs. The discrepancy in the total number of biomarkers per category (N = 121) and the total number of biomarkers reported (N = 112) is due to different biomarker categories pertaining to the same biomarker being reported together (an example of this is Telomerase in Table 2). All biomarkers were sampled from urine—apart from one (CYFRA21-1), which was sampled from both urine and serum using a range of test platforms such as Enzyme-Linked Immunoassay (ELISA), Fluorescence In Situ Hybridization (FISH), lateral flow test, and different types of Polymerase Chain Reaction (PCR).

3.6. Meta-Analyses

3.6.1. Assessing Heterogeneity

Forest plots for sensitivity and specificity were produced for all biomarkers reported in three or more studies (NMP-22, UroVysion, uCyt+ (also referred to as ImmunoCyt+), BTAstat and FGFR3) (Figure S4 in Supplementary Materials). Variation in the accuracy measures (sensitivity and specificity) between studies may be explained by either the use of different thresholds/cut-off values to distinguish between a positive and negative result or heterogeneity in the study design (for example, differences in study setting or study design). The performance of the three biomarkers reported in five or more studies (NMP-22, UroVysion, uCyt+) was summarized by calculating the HSROC, which accounts for the variation in cut-off values (Figure 3).
The twelve studies reporting accuracy measures for NMP22 use four different thresholds (ranging from 3.6–10 IU/mL), so the large range of sensitivities (0.27–0.90) and specificities (0.31–0.98) reported are unsurprising. However, most of the studies fall outside of the 95% confidence region of the HSROC model (Figure 3a), which suggests that other differences between the studies are also causing the variation in performance. This could be due to the diverse study populations in which NMP-22 was tested, including the following categories: country (five in Germany, four in the UK, one in Pakistan, and one in Australia/New Zealand; country of research in three studies was not provided), population age (with some studies enrolling participants of much wider age range than others), ethnicity, symptoms at presentation, and the extent to which risk factors such as smoking were addressed (Table 1).
The ten studies reporting performance for UroVysion all used the same platform (FISH), which indicates that the range of reported sensitivity (0.38–0.96) may be due to other differences between the studies (the range of reported specificities is, however, relatively small (0.76–0.99)). This is supported by the meta-analysis, which finds that several of the UroVysion studies fall outside the 95% confidence region of the HSROC model (Figure 3b). This could be due to variation in the study population between studies, in terms of the country of research (four in Germany, two in China, two in the USA, one in Belgium; country of research in one study was not provided), age range of included population and symptoms at presentation (Table 1).
The six studies reporting performance for uCyt+ all use the same threshold to determine positive test results (“at least one clear positive cell”), therefore, the relatively narrow range of sensitives (0.62–0.92) and specificities (0.72–0.81) reported is unsurprising. In the HSROC model, most of the studies fall in the 95% prediction region (Figure 3c). This suggests that the studies are relatively homogenous, likely carried out in similar settings and using comparable populations. This finding is supported by examination of the study characteristics (Table 1); all six studies were conducted in Europe (five in Germany and one in France), enrolled populations of similar age ranges (participants aged from 18 to 97) and patients had similar symptoms at first presentation.

3.6.2. Overall Performance and Sensitivity Analysis

The HSROC models for NMP-22, UroVysion and uCyt+ are compared in Figure 3d, and summary measures of discrimination (how well the test distinguishes between those with and without bladder cancer) are given. The estimated summary ROC curves show UroVysion has the best discrimination (AUC estimate: 0.876), slightly outperforming uCyt+ (AUC estimate: 0.827) and considerably outperforming than NMP-22 (AUC estimate: 0.748). However, the adjusted partial AUC estimates (accounting for the observed ranges of accuracy measures and normalized) have similar results for all three (0.650, 0.707 and 0.689 respectively). The overlap of the prediction regions (the 95% prediction region of the HSROC model estimates), further demonstrates that in this meta-analysis, no significant differences in discrimination are found between these three biomarkers.
The performance of NMP-22 was reported for two different platforms, ELISA (N = 10) and BladderChek (N = 3). In a sensitivity analysis (Figure S5 in Supplementary Materials), the performance of NMP-22 across both platforms was compared to the performance for ELISA only (there were insufficient BladderChek studies (n < 5) for a separate HSROC analysis). Although the three BladderChek studies all report relatively high specificities (0.81–0.96)—compared to the ELISA studies (0.34–0.88)—the reported sensitivities are variable (0.26–0.76); the HSROC analysis finds only minimal differences in discrimination between the ELISA-only studies and all NMP-22 studies (adjusted partial AUC of 0.701 and 0.689 respectively).

3.7. Narrative Synthesis

3.7.1. For Biomarkers Reported in Three Studies or More

In terms of acceptability, two biomarkers (NMP-22, using the BladderChek platform, and BTAstat) were highlighted as operator-independent, simple, and fast to analyze during patient visits, and, therefore, are suitable for use in the outpatient clinic [32,48,59]. However, acceptability could be compromised, as the diagnostic performance of all identified biomarkers was reported to be widely dependent on the severity of haematuria as quantified by urine dipstick analysis (NMP-22 ELISA, UroVysion, uCyt+) [65] the presence or absence of haematuria (BTAstat) [32] and in cases with acute clot retention (FGFR3) [67].
Considering the benefits, all biomarkers were reported to either improve bladder cancer detection or reduce burden on patients and health care providers when used in conjunction with urine cytology [56,59,63]. FGFR3 was observed to efficiently detect bladder cancer in patients with low grade tumors [50] whereas NMP-22 (unspecified test platform) and BTAstat were shown to outperform cytology in detecting G3 tumors, with the former also showing significantly higher detection rates for G1 and G2 tumors [58]. As for harmful results, increased rates of false positive results were reported for four out of six (including NMP-22 ELISA and BladderChek) biomarkers in the following cases: (i). in patients with urinary tract inflammation and/or infection (NMP-22 ELISA [49,55,57] and BTAstat [32,57]); (ii). haematuria (NMP-22 ELISA, uCyt+) [65], or microscopic haematuria (UroVysion) [69]; (iii). atypical urinary cytology and other risk factors such as older age or significant tobacco use (NMP-22 ELISA, uCyt+, UroVysion) [49,69].
In terms of referral patterns, UroVysion and FGFR3 were reported to help with triaging rapid referrals for haematuria [50,61] or to reduce the frequency (uCyt+) [56] or the number of unnecessary cystoscopy/cytology tests (NMP-22 ELISA) [57] in the healthcare systems in which they were assessed. Further details of their specific use within the diagnostic pathway, however, were not provided. Finally, when it comes to costs, BTAstat was reported to have the lowest estimated cost compared to NMP-22 (unspecified test platform), cytology, and flexible and rigid cystoscopy [58] as opposed to UroVysion [69,72] and uCyt+ that were reported to come at increased cost [64].

3.7.2. For Biomarkers with High Negative Predictive Value

A summary of key findings pertaining to novel biomarkers that were investigated in fewer than three studies and which show potential for early detection of bladder cancer in the general population can be found in Table 3. Eight novel biomarkers/tests from all biomarker categories were purposively selected based on their reported high negative predictive value (NPV ≥ 90.0%), indicating their potential use in the general population for triaging patients for further investigations (see Table 3).

4. Discussion

4.1. Summary of Main Findings

This systematic review identified 44 studies reporting on 112 different biomarkers and combinations for bladder cancer detection. Most of the biomarkers identified were only reported in one study, with only three biomarkers (NMP-22, UroVysion and uCyt+) in a sufficient number of studies (n ≥ 5) to be included in the HSROC calculations. These biomarkers showed similar discriminative ability (adjusted AUC estimates ranging from 0.650 to 0.707). Narrative synthesis revealed the potential of some of these biomarkers for use in the general population, based on their reported clinical utility including diagnostic performance and effects on clinicians, patients, and the healthcare system. Finally, several novel biomarkers showed high negative predictive value indicating their potential for use in the general population presenting in community settings.

4.2. Comparison with Existing Literature

The calculated adjusted HSROC revealed small variations in discrimination across the three biomarkers included in the meta-analysis, all of which are well-established FDA-approved biomarkers, that have, either individually or comparatively, been explored in systematic reviews and/or meta-analyses before [13,73,74,75,76]. Heterogeneity beyond variation in adopted thresholds was also confirmed for these three biomarkers. This reported variation can be largely associated with a range of confounding factors mainly pertaining to the heterogeneity of included studies—this has also been identified as the main limitation in most meta-analyses conducted to date [74,75]. In addition to different probability thresholds, a series of methodological factors including the diversity in study designs and population samples, and the extent to which risk factors were also addressed in the study could potentially be influencing performance variation. Considering population samples in more detail, variation persisted not only in the numbers of participants enrolled but also in the composition of the cohorts studied (e.g., non-cancer patients ranging from healthy general population participants to hospital urology patients with or without benign pathology) and the range of symptoms reported at first presentation. There is, therefore, a risk of spectrum bias given the observed variation in the population samples in which the biomarkers were tested [77,78]. This risk was particularly evident for some of the biomarkers included in the meta-analysis such as NMP-22, which included studies that enrolled heterogeneous populations (particularly in terms of age, ethnicity, symptomatology at presentation, and risk factors). Therefore, extrapolating results to reaffirm the potential applicability of the reported biomarkers in the general population is challenging.
The complementary narrative synthesis aimed to ascertain such applicability, by investigating further the population, contextual and implementation factors. Findings indicated variation similar to that of the meta-analysis and are in line with evidence reported in previous reviews [12,79] and meta-analyses [13,80] about the potential of these biomarkers to effectively supplement cytology in bladder cancer detection or help with appropriate rapid referrals and reduce the number of unnecessary cystoscopies in the studied populations. However, certain barriers, such as diagnostic performance measures being affected by the degree of haematuria [32,65,67] or the inability of certain biomarkers to detect low grade tumors [38,58], were also identified, compromising their utility in the general population. Hence, the reported promising value of these biomarkers needs to be treated with caution.
A number of novel biomarkers (such as ADXBLADDER, CxBladder Triage and Xpert) or combinations (FGFR3 + TERT + HRAS + OXT1 + ONECUT2 + TWIST) were also reported to have high negative predictive values, indicating potential utility in community settings, as reassurance can be provided that cancer is an unlikely outcome [28,29]. This potential was also reaffirmed by the narrative synthesis. However, considering that these biomarkers were tested in either one or two studies only, validation studies in the general population are still required.

4.3. Strengths and Limitations

Comprehensive literature searches were performed, strict eligibility criteria were set for study selection and explicit methods were employed for data extraction and data analysis. Heterogeneity, however, is the main limitation of this review, pertaining to various aspects of included studies such as study design, population samples, thresholds used and outcome measures. Such heterogeneity may distort meta-analysis and, as a result, reported results should be interpreted with caution. Another limitation that is relatively common to systematic reviews of biomarker performance is a lack of clarity or a low quality of reporting, with most included studies, when critically appraised, being assessed as either unclear or at high risk of bias in at least one domain. Finally, considering that narrative synthesis was based on original authors’ conclusions, an impartial assessment of those results reported as potentially promising might, to some extent, be compromised depending on how positives/negatives of each biomarker were portrayed by authors in different studies.

4.4. Implication for Research and Practice

This review identified biomarkers that could potentially be beneficial for use in community settings based on their diagnostic performance. Similar to conclusions from previous reviews [12,79] while there are promising results (particularly regarding high NPVs) for some biomarkers, additional validations are still needed in the community setting. Although an attempt to limit heterogeneity was made by only including patients with a suspicion of cancer at the point of recruitment, it is likely that levels of cancer risk vary even for this group across different studies. Furthermore, the included studies were evaluated as being at a higher risk of bias in more than one QUADAS-2 domain. Therefore, caution is warranted when generalizing performance results.
It is also important to consider the role of the biomarker within the cancer diagnostic pathway. In the general population, there is the need for a test to help better risk stratify patients with urological symptoms to facilitate clinical decision-making regarding the need for referral for subsequent cancer-specific investigations, similar to the use of fecal immunochemical testing for possible colorectal cancer [81,82,83]. We found no studies reporting the use of biomarkers for bladder cancer in this context. To assess the clinical utility of these biomarkers in the community, there is therefore a need to evaluate these biomarkers in the general population, at the pre-referral stage of the diagnostic process.
Finally, evidence on the effects on patients, clinicians, and health care systems (reported in the narrative synthesis) was not widely reported across included studies. Therefore, despite years of biomarker development and testing, implementation and cost-effectiveness (Phases 3–5 in the CanTest framework) are still not often investigated [17]. No single biomarker with excellent diagnostic performance and corresponding implementation data was identified and the current findings do not allow for firm recommendations of any of the identified biomarkers for use in the general population. Novel biomarkers showing promising results need to be further evaluated, preferably prospectively, with consistency regarding populations, care settings and thresholds/cut-off points used.

5. Conclusions

In conclusion, findings from this systematic review suggest that certain biomarkers show potential to complement or improve current bladder cancer diagnostic strategies. Limited evidence on novel biomarkers shows that those with high NPV could be promising for use in community settings as a triage tool for appropriate and necessary referrals. More prospective studies are needed to further validate this promising evidence in the general population before establishing the exact place/role of these biomarkers within the diagnostic pathway.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15030709/s1, Figure S1. PRISMA-DTA Checklist; Table S1. PICOS Framework—Inclusion & Exclusion Criteria; Figure S2. CanTest Framework; Table S2. Ovid MEDLINE(R) and Epub Ahead of Print, In-Process, In-Data-Review & Other Non-Indexed Citations, Daily and Versions(R) 1946 to 2 June 2021; Figure S3. QUADAS-2: Summary of Results; Figure S4. Forest plots of sensitivity and specificity for NMP-22, UroVysion, uCyt+, BTAstat and FGFR3; Figure S5. Sensitivity analysis for NMP-22 ELISA vs. BladderChek.

Author Contributions

Funding acquisition, F.M.W.; conceptualization, F.M.W., Y.Z. and V.A.S.; methodology F.M.W., V.A.S., N.C. and E.P.; data screening and data extraction D.B., R.B., A.C., N.C., H.H., E.d.M., E.P., V.A.S., C.S. and Y.Z.; visual aids design A.C., E.d.M. and E.P.; formal analysis, E.P., H.H., V.A.S., D.B., R.B. and S.T.; data curation, E.P. and V.A.S.; PPI & expert advice J.B., F.M.W., Y.Z. and G.D.S.; E.d.M. and H.H.; writing—original draft preparation, E.P. All authors have reviewed and edited of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research arises from the CanTest Collaborative, which is funded by Cancer Research UK [C8640/A23385]. YZ is funded by a Wellcome Trust Primary Care Clinician PhD Fellowship (203921/Z/16/Z) and HH is supported by an International Alliance for Cancer Early Detection Project Award (ACEDFR3 0620I135PR007).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The Search Strategy used in this systematic review has been provided in Supplementary Materials. The datasets generated during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef]
  2. Van Valenberg, F.J.P.; Hiar, A.M.; Wallace, E.; Bridge, J.A.; Mayne, D.J.; Beqaj, S.; Sexton, W.J.; Lotan, Y.; Weizer, A.Z.; Jansz, G.K.; et al. Validation of an mRNA-based Urine Test for the Detection of Bladder Cancer in Patients with Haematuria. EAU 2021, 4, 93–101. [Google Scholar] [CrossRef]
  3. Rai, B.P.; Escrig, J.L.D.; Vale, L.; Kuusk, T.; Capoun, O.; Soukup, V.; Bruins, H.M.; Yuan, Y.; Violette, P.D.; Santesso, N.; et al. Systematic Review of the Incidence of and Risk Factors for Urothelial Cancers and Renal Cell Carcinoma Among Patients with Haematuria. Eur. Urol. 2022, 82, 182–192. [Google Scholar] [CrossRef]
  4. Zhou, Y.; Funston, G.; Lyratzopoulos, G.; Walter, F.M. Improving the Timely Detection of Bladder and Kidney Cancer in Primary Care. Adv. Ther. 2019, 36, 1778–1785. [Google Scholar] [CrossRef] [Green Version]
  5. Zhou, Y.; Walter, F.M.; Singh, H.; Hamilton, W.; Abel, G.A.; Lyratzopoulos, G. Prolonged Diagnostic Intervals as Marker of Missed Diagnostic Opportunities in Bladder and Kidney Cancer Patients with Alarm Features: A Longitudinal Linked Data Study. Cancers 2021, 13, 156. [Google Scholar] [CrossRef]
  6. Tan, W.S.; Tan, W.P.; Tan, M.Y.; Khetrapal, P.; Dong, L.; deWinter, P.; Dong, L.; deWinter, P.; Feber, A.; Kelly, J.D. Novel urinary biomarkers for the detection of bladder cancer: A systematic review. Cancer Treat Rev. 2018, 69, 39–52. [Google Scholar] [CrossRef]
  7. Ng, K.; Stenzl, A.; Sharma, A.; Vasdev, N. Urinary biomarkers in bladder cancer: A review of the current landscape and future directions. Urol. Oncol. Semin. Orig. Investig. 2021, 39, 41–51. [Google Scholar] [CrossRef]
  8. Zhu, C.Z.; Ting, H.N.; Ng, K.H.; Ong, T.A. A review on the accuracy of bladder cancer detection methods. J. Cancer 2019, 10, 4038–4044. [Google Scholar] [CrossRef] [Green Version]
  9. Di Martino, E.; Ayaz-Shah, A.; Shinkins, B.; Jain, S.; Neal, R.D. A urine test for bladder cancer: Available soon in primary care? Trends Urol. Men’s Health 2020, 11, 18–21. [Google Scholar] [CrossRef]
  10. Giordano, A.; Soria, F. Role and efficacy of current biomarkers in bladder cancer. AME Med. J. 2020, 5, 6. [Google Scholar] [CrossRef]
  11. Dong, Y.; Zhang, T.; Li, X.; Yu, F.; Yu, H.; Shao, S. Urine biomarkers for the diagnosis of bladder cancer: A network meta-analysis. Urol. J. 2021, 18, 623–632. [Google Scholar]
  12. Hong, M.; He, G.; Goh, S.; Wei, A.; Low, X.; Tay, K.J.; Lim, T.K.H.; Yeong, J.; Khor, L.; Lim, T.S. Biomarkers for precision urothelial carcinoma diagnosis: Current approaches and the application of single-cell technologies. Cancers 2021, 13, 260. [Google Scholar] [CrossRef]
  13. Wang, J.; Zhao, X.; Jiang, X.L.; Lu, D.; Yuan, Q.; Li, J. Diagnostic performance of nuclear matrix protein 22 and urine cytology for bladder cancer: A meta-analysis. Diagn. Cytopathol. 2022, 50, 300–312. [Google Scholar] [CrossRef]
  14. Higgins, J.P.T.; Thomas, J.; Chandler, J.; Cumpston, M.; Li, T.; Page, M.J.; Welch, V.A. (Eds.) Cochrane Handbook for Systematic Reviews of Interventions, 2nd ed.; John Wiley & Sons: Chichester, UK, 2019. [Google Scholar]
  15. McInnes, M.D.F.; Moher, D.; Thombs, B.D.; McGarth, T.A.; Bossuyt, P.M.; PRISMA-DTA Group. Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 2018, 319, 388–396. [Google Scholar] [CrossRef]
  16. Methley, A.M.; Campbell, S.; Chew-Graham, C.; McNally, R.; Cheraghi-Sohi, S. PICO, P.ICOS and SPIDER: A comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv. Res. 2014, 14, 579. [Google Scholar] [CrossRef] [Green Version]
  17. Walter, F.M.; Thompson, M.J.; Wellwood, I.; Abel, G.A.; Hamilton, W.; Johnson, M.; Lyratzopoulos, G.; Messenger, M.P.; Neal, R.D.; Rubin, G.; et al. Evaluating diagnostic strategies for early detection of cancer: The CanTest framework. BMC Cancer 2019, 19, 586. [Google Scholar] [CrossRef]
  18. Olesen, F.; Hansen, R.P.; Vedsted, P. Delay in diagnosis: The experience in Denmark. Br. J. Cancer 2009, 101, S5–S8. [Google Scholar] [CrossRef] [Green Version]
  19. Ouzzani, M.; Hammady, H.; Fedorowicz, Z.; Elmagarmid, A. Rayyan—A web and mobile app for systematic reviews. Syst. Rev. 2016, 5, 210. [Google Scholar] [CrossRef] [Green Version]
  20. Whiting, P.F.; Rutjes, A.W.S.; Westwood, M.E.; Mallett, S.; Deeks, J.J.; Reitsma, J.B.; Leeflang, M.M.G.; Sterne, A.C.; Bossuyt, M.M.; QUADAS-2 Group. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann. Intern. Med. 2011, 155, 529–536. [Google Scholar] [CrossRef]
  21. Doebler, P.; Holling, H. Meta-Analysis of Diagnostic Accuracy with Mada. R Packag. 2015. Available online: https://bit.ly/36agmQy (accessed on 27 July 2022).
  22. Reitsma, J.B.; Glas, A.S.; Rutjes, A.W.; Scholten, R.J.; Bossuyt, P.M.; Zwinderman, A.H. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 2005, 58, 982–990. [Google Scholar] [CrossRef]
  23. Arends, L.R.; Hamza, T.H.; van Houwelingen, J.C.; Heijenbrok-Kal, M.H.; Hunink, M.G.M.; Stijnen, T. Bivariate Random Effects Meta-Analysis of ROC Curves. Med. Decis. Mak. 2008, 28, 621–638. [Google Scholar] [CrossRef]
  24. Petticrew, M.; Roberts, H. Systematic Reviews in the Social Sciences; Blackwell: Oxford, UK, 2006. [Google Scholar]
  25. Popay, J.; Roberts, H.; Sowden, A.; Petticrew, M.; Arai, L.; Rodgers, M.; Britten, N.; Roen, K.; Duffy, S. Guidance on the Conduct of Narrative Synthesis in Systematic Reviews: A Product from the ESRC Methods Programme. 2006, p. 92. Available online: https://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/fhm/dhr/chir/NSsynthesisguidanceVersion1-April2006.pdf (accessed on 12 October 2022).
  26. Greenhalgh, T.; Robert, G.; Macfalane, F.; Bate, P.; Kyriakidou, O. Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations. Millbank Q. 2004, 82, 581–629. [Google Scholar] [CrossRef] [Green Version]
  27. Greenhalgh, T.; Peacock, R. Effectiveness and efficiency of search methods in systematic reviews of complex evidence: Audit of primary sources. BMJ 2005, 331, 1064–1065. [Google Scholar] [CrossRef] [Green Version]
  28. Jellema, P.; van der Windt, D.A.W.M.; Bruinvels, D.J.; Mallen, C.D.; van Weyenberg, S.J.B.; Mulder, C.J.; de Vet, H.C.W. Value of symptoms and additional diagnostic tests for colorectal cancer in primary care: Systematic review and meta-analysis. BMJ 2010, 340, c1269. [Google Scholar] [CrossRef] [Green Version]
  29. Baeyens, J.P.; Serrien, B.; Goossens, M.; Clijsen, R. Questioning the “SPIN and SNOUT” rule in clinical testing. Arch. Physiother. 2019, 7, 4. [Google Scholar] [CrossRef] [Green Version]
  30. Attallah, A.; El-Far, M.; Abdallah, S.O.; El-Waseef, A.M.; Omran, M.M.; Abdelrazek, M.A.; Attallah, A.A.; Saadh, M.J.; Radwan, M.; El-waffaey, K.A.; et al. Combined use of epithelial membrane antigen and nuclear matrix protein 52 as sensitive biomarker for detection of bladder cancer. Int. J. Biol. Markers 2015, 30, e407–e413. [Google Scholar] [CrossRef]
  31. Barbieri, C.E.; Cha, E.K.; Chromecki, T.F.; Dunning, A.; Lotan, Y.; Svatek, R.S.; Scherr, D.S.; Karakiewicz, P.I.; Sun, M.; Mazumdar, M.; et al. Decision curve analysis assessing the clinical benefir of NMP22 in the detection of bladder cancer: Secondary analysis of a prospective trial. BJUI 2011, 109, 685–690. [Google Scholar] [CrossRef] [Green Version]
  32. Bhuiyan, J.; Akhter, J.; O’Kane, D.J. Performance characteristics of multiple urinary tumor markers and sample collection techniques in the detection of transitional cell carcinoma of the bladder. Clin. Chim. Acta 2003, 331, 69–77. [Google Scholar] [CrossRef]
  33. Critselis, E.; Rava, M.; Marquez, M.; Lygirou, V.; Chatzicharalambous, D.; Liapi, P.; Lictinghagen, R.; Brand, K.; Cecchini, L.; Vlahou, A.; et al. Diagnostic and Prognostic Performance of Secreted Protein Acidic and Rich in Cysteine (SPARC) Assay for Detecting Primary and Recurrent Urinary Bladder Cancer. Proteom. Clin. Appl. 2019, 13, e1800148. [Google Scholar] [CrossRef]
  34. Dahmcke, C.M.; Steven, K.E.; Larsen, L.K.; Poulsen, A.L.; Abdul-Al, A.; Dahl, C.; Guldberg, P. A prospective blinded evaluation of urine-DNA testing for detection of urothelial bladder carcinoma in patients with gross hematuria. Eur. Urol. 2016, 70, 916–919. [Google Scholar] [CrossRef]
  35. Davidson, P.J.; McGeoch, G.; Shand, B. Assessment of a clinical pathway for investigation of haematuria that reduces the need for cystoscopy. NZMA 2020, 133, 71–82. [Google Scholar]
  36. Deininger, S.; Todenhöfer, T.; Hennenlotter, J.; Gerber, V.; Schwarz, J.; Bedke, J.; Schwentner, C.; Stenzl, A.; Rausch, S. Impact of variant microscopic interpretation of the uCyt+ immunocytological urine test for the detection of the bladder cancer. Diagn. Cytopathol. 2018, 46, 111–116. [Google Scholar] [CrossRef]
  37. Dudderidge, T.; Stockley, J.; Nabi, G.; Mom, J.; Uzem-Eronini, N.; Hrouda, D.; Cresswell, J.; McCracken, S.R.C. A novel, non-invasive test enabling bladder cancer detection in urine sediment of patients presenting with haematuria—A prospective multicentre performance evaluation of ADXBLADDER. Eur. Urol. Oncol. 2019, 3, 42–46. [Google Scholar] [CrossRef] [PubMed]
  38. Eissa, S.; Swellam, M.; Ali-Labib, R.; Mansour, A.; El-Malt, O.; Tash, F.M. Detection of telomerase in urine by 3 methods: Evaluation of diagnostic accuracy for bladder cancer. J. Urol. 2007, 178 Pt 1, 1068–1072. [Google Scholar] [CrossRef] [PubMed]
  39. Eissa, S.; Ali-Labib, R.; Swellam, M.; Bassiony, M.; Tash, F.; El-Zayat, T.M. Noninvasive diagnosis of bladder cancer by detection of matrix metalloproteinases (MMP-2 and MMP-9) and their inhibitor (TIMP-2) in urine. Eur. Urol. 2007, 52, 1388–1396. [Google Scholar] [CrossRef]
  40. Eissa, S.; Salem, A.M.; Zohny, S.F.; Hegazy, M.G.A. The diagnostic efficacy of urinary TGF-β1 and VEGF in bladder cancer: Comparison with voided urine cytology. Cancer Biomark. 2007, 3, 275–285. [Google Scholar] [CrossRef]
  41. Eissa, S.; Swellam, M.; Ali-Labib, R.; El-Zayat, T.; El Ahmady, O. A panel of angiogenic factors for early bladder cancer detection: Enzyme immunoassay and western blot. J. Urol. 2009, 181, 1353–1360. [Google Scholar] [CrossRef]
  42. Eissa, S.; Swellam, M.; Shehata, H.; El-Khouly, I.M.; El-Zayat, T.; El-Ahmady, O. Expression of HYAL1 and Survivin, R.NA as diagnostic molecular markers for bladder cancer. J. Urol. 2010, 183, 493–498. [Google Scholar] [CrossRef]
  43. Eissa, S.; Swellam, M.; Amin, A.; Balbaa, M.E.; Yacout, G.A.; El-Zayat, T.M. The clinical relevance of urine-based markers for diagnosis of bladder cancer. Med. Oncol. 2011, 28, 513–518. [Google Scholar] [CrossRef]
  44. Eissa, S.; Zohny, F.S.; Shehata, H.H.; Hegazy, M.G.A.; Salem, A.M.; Esmat, M. Urinary retinoic acid receptor-β2 gene promoter methylation and hyaluronidase activity as noninvasive tests for diagnosis of bladder cancer. Clin. Biochem. 2012, 45, 402–407. [Google Scholar] [CrossRef]
  45. Eissa, S.; Matboli, M.; Mansour, A.; Mohamed, S.; Awad, N.; Kotb, Y.M. Evaluation of urinary HURP mRNA as a marker for detection of bladder cancer: Relation to bilharziasis. Med. Oncol. 2014, 31, 804. [Google Scholar] [CrossRef]
  46. Eissa, S.; Shawky, S.M.; Matboli, M.; Mohamed, S.; Azzazy, H.M. Direct detection of unamplified hepatoma upregulated protein RNA in urine using gold nanoparticles for bladder cancer diagnosis. Clin Biochem. 2014, 47, 104–110. [Google Scholar] [CrossRef] [PubMed]
  47. Fu, F.; Zeng, F.; Sun, Z.; Jin, F. Head-to head comparison of serum and urine cytokeratin-19 fragnents (CYFRA 21-1) for bladder cancer diagnosis. Transl. Cancer Res. 2018, 7, 55–59. [Google Scholar] [CrossRef]
  48. Grossman, H.B.; Messing, E.; Soloway, M.; Tomera, K.; Katz, G.; Berger, Y.; Shen, Y. Detection of bladder cancer using a point-of-care proteomic assay. JAMA 2005, 293, 810–816. [Google Scholar] [CrossRef] [PubMed]
  49. Horstmann, M.; Todenhöfer, T.; Hennenlotter, J.; Aufdeklamm, S.; Mischinger, J.; Kuehs, U.; Gakis, G.; Stenzl, A.; Schwentener, C. Influence of age on false positive rates of urine-based tumor markers. World J. Urol. 2013, 31, 935–940. [Google Scholar] [CrossRef] [PubMed]
  50. Karnes, J.R.; Fernandez, C.A.; Shuber, A.P. A noninvasive multianalyte urine-based diagnostic assay for urothelial cancer of the bladder in the evaluation of haematuria. Mayo Clin. Proc. 2012, 87, 835–842. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Kelly, J.D.; Dudderidge, T.J.; Wollenschlaeger, A.; Okoturo, O.; Burling, K.; Tulloch, F.; Halsall, I.; Prevost, T.; Prevost, A.T.; Vasconcelos, J.C.; et al. Bladder cancer diagnosis and identification of clinically significant disease by combined urinary detection of Mcm5 and Nuclear matric protein 22. PLoS ONE 2012, 7, e40305. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Liu, C.; Shi, B.; Hao, C.; Wang, Q.; Lv, Q.; Xing, N.; Shou, J.; Qu, L.; Gao, Y.; Qin, C.; et al. Urine gamma-synuclein as a biomarker for the diagnosis of bladder cancer. Oncotarget 2016, 7, 43432–43441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Meiers, I.; Singh, H.; Hossain, D.; Lang, K.; Liu, L.; Qian, J.; Verhest, A.P.; Bostwick, D.G. Improved filter method for urine sediment detection of urothelial carcinoma by fluorescence in situ hybridization. Arch. Pathol. Lab. Med. 2007, 131, 1574–1577. [Google Scholar] [CrossRef]
  54. O’Sullivan, P.; Sharples, K.; Dalphin, M.; Davidson, P.; Gilling, P.; Cambridge, L.; Harvey, J.; Toro, T.; Giles, N.; Luxmanan, C.; et al. A multigene urine test for the detection and stratification of bladder cancer in patients presenting with haematuria. J. Urol. 2012, 188, 741–747. [Google Scholar] [CrossRef]
  55. Oertl, A.; Balan, S.; Okoński, P.K.; Jonas, D.; Oremek, G.M. Nuclear matrix protein 22 (NMP 22) a tumour marker for detection of bladder cancer. Clin. Exp. Med. Lett. 2007, 48, 15–17. [Google Scholar]
  56. Piaton, E.; Daniel, L.; Verriele, V.; Dalifard, I.; Zimmermann, U.; Renaudin, K.; Gobet, F.; Caratero, A.; Desvaux, D.; Pouille, Y.; et al. Improved detection of urothelial carcinomas with fluorescence immunocytochemistry (uCyt+ Assay) and urinary cytology: Results of a French prospective multicenter study. Lab. Investig. 2003, 83, 845–852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Poulakis, V.; Witzsch, U.; De Vries, R.; Altmannsberger, H.M.; Manyak, M.J.; Becht, E. A comparison of urinary nuclear matrix protein-22 and bladder tumour antigen tests with voided urinary cytology in detecting and following bladder cancer: The prognostic value of false-positive results. BJU Int. 2001, 88, 692–701. [Google Scholar] [CrossRef] [Green Version]
  58. Saad, A.; Hanbury, D.C.; McNicholas, T.A.; Boustead, G.B.; Morgan, S.; Woodman, A.C. A study comparing various noninvasive methods of detecting bladder cancer in urine. BJU Int. 2002, 89, 369–373. [Google Scholar] [CrossRef] [PubMed]
  59. Sajid, M.T.; Zafar, M.R.; Ahmad, H.; Ullah, S.; Mirza, Z.I.; Shahzad, K. Diagnostic accuracy of the NMP 22 and urine cytology for detection of transitional cell carcinoma urine bladder taking cystoscopy as gold standard. Pak. J. Med. Sci. 2020, 36, 705–709. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  60. Sánchez-Carbayo, M.; Urrutia, M.; González de Buitrago, J.M.; Navajo, J.A. Evaluation of two urinary tumor markers: Bladder tumor fibronectin and cytokeratin 18 for the diagnosis of bladder cancer. Clin. Cancer Res. 2000, 6, 3585–3594. [Google Scholar]
  61. Sarosdy, M.F.; Kahn, P.R.; Ziffer, M.D.; Love, M.R.; Barkin, J.; Abara, E.O.; Jansz, K.; Bridge, J.A.; Johansson, S.L.; Person, D.L.; et al. Use of a multitarget fluorescence in situ hybridization assay to diagnose bladder cancer in patients with haematuria. J. Urol. 2006, 176, 44–47. [Google Scholar] [CrossRef]
  62. Shang, D.; Liu, Y.; Xu, X.; Chen, Z.; Wang, D. Diagnostic value comparison of CellDetect, fluorescent in situ hybridization (FISH), and cytology in urothelial carcinoma. Cancer Cell Int. 2021, 21, 465. [Google Scholar] [CrossRef]
  63. Todenhöfer, T.; Hennenlotter, J.; Esser, M.; Mohrhardt, S.; Tews, V.; Aufderklamm, S.; Gakis, G.; Kuehs, U.; Stenzl, A.; Schwentner, C. Combined application of cytology and molecular urine markers to improve the detection of urothelial carcinoma. Cancer Cytopathol. 2013, 121, 252–260. [Google Scholar] [CrossRef]
  64. Todenhöfer, T.; Hennenlotter, J.; Aufderklamm, S.; Kühs, U.; Gakis, G.; Germann, M.; Stenzl, A.; Schwentner, C. Individual risk assessment in bladder cancer patients based on a multi-marker panel. J. Cancer Res. Clin. Oncol. 2013, 139, 49–56. [Google Scholar] [CrossRef]
  65. Todenhöfer, T.; Hennenlotter, J.; Tews, V.; Gakis, G.; Aufderklamm, S.; Kuehs, U.; Stenzl, A.; Schwenter, C. Impact of different grades of microscopic hematuria on the performance of urine-based markers for the detection of urothelial carcinoma. Urol. Oncol. Semin. Orig. Investig. 2013, 31, 1148–1154. [Google Scholar] [CrossRef] [PubMed]
  66. Van Kessel, K.E.M.; Van neste, L.; Lurkin, I.; Zwarthoff, E.C.; Criekinge, W.V. Evaluation of an epigenetic profile for the detection of bladder cancer in patients with hematuria. J. Urol. 2016, 195, 601–607. [Google Scholar] [CrossRef]
  67. Van Kessel, K.E.M.; Beukers, W.; Lurkin, I.; Ziel-van der Made, A.; van der Keur, K.A.; Boormans, J.L.; Dyrskjøt, L.; Márquez, M.; Ørntoft, T.F.; Real, F.X.; et al. Validation of a DNA methylation-mutation urine assay to select patients with hematuria for cystoscopy. J. Urol. 2017, 197 Pt 1, 590–595. [Google Scholar] [CrossRef] [PubMed]
  68. Van Kessel, K.E.M.; de Jong, J.J.; Ziel-van der Made, A.C.J.; Roshani, H.; Haensel, S.M.; Wolterbeek, J.H.; Boevé, E.R.; Oomens, E.H.G.M.; van Casteren, N.J.; Krispin, M.; et al. A urine based genomic assay to triage patients with hematuria for cystoscopy. J. Urol. 2020, 204, 50–57. [Google Scholar] [CrossRef]
  69. Virk, R.K.; Abro, S.; Muus Martinez de Ubago, J.; Pambuccian, S.E.; Quek, M.L.; Wojcik, E.M.; Mehrotra, S.; Chatt, G.U.; Barkan, G.A. The value of the UroVysion® FISH assay in the risk-stratification of patients with “atypical urothelial cells” in urinary cytology specimens. Diagn. Cytopathol. 2017, 45, 481–500. [Google Scholar] [CrossRef] [PubMed]
  70. Ward, D.G.; Baxter, L.; Ott, S.; Gordon, N.S.; Wang, J.; Patel, P.; Piechocki, K.; Silcock, L.; Sale, C.; Zeegers, M.P.; et al. Highly Sensitive and Specific Detection of Bladder Cancer via Targeted Ultra-deep Sequencing of Urinary, D.NA. Eur. Urol. Oncol. 2022. [Google Scholar] [CrossRef]
  71. Wu, Y.; Jiang, G.; Zhang, N.; Liu, S.; Lin, X.; Perschon, C.; Zheng, L.S.; Ding, Q.; Wang, X.; Na, R.; et al. HOXA9, P.CDH17, P.OU4F2, and ONECUT2 as a Urinary Biomarker Combination for the Detection of Bladder Cancer in Chinese Patients with Hematuria. Eur. Urol. Focus 2020, 15, 284–291. [Google Scholar] [CrossRef]
  72. Zhou, L.; Yang, K.; Li, X.; Ding, Y.; Mu, D.; Li, H.; Yan, Y.; Li, J.; Wang, D.; Li, W.; et al. Application of fluorescence in situ hybridization in the detection of bladder transitional-cell carcinoma: A multi-center clinical study based on Chinese population. Asian J. Urol. 2019, 6, 114–121. [Google Scholar] [CrossRef]
  73. Guo, A.; Wang, X.; Gao, L.; Shi, J.; Sun, C.; Wan, Z. Bladder tumour antigen (BTA stat) test compared to the urine cytology in the diagnosis of bladder cancer: A meta-analysis. Can. Urol. Assoc. J. 2014, 8, e347–e352. [Google Scholar] [CrossRef] [Green Version]
  74. Chou, R.; Gore, J.L.; Buckley, D.; Fu, R.; Gustafon, K.; Griffin, J.C.; Grusing, S.; Selph, S. Urinary biomarkers for diagnosis of bladder cancer. Ann. Intern. Med. 2015, 163, 922–931. [Google Scholar] [CrossRef] [Green Version]
  75. He, H.; Han, C.; Hao, L.; Zang, G. ImmunoCyt test compared to cytology in the diagnosis of bladder cancer: A meta-analysis. Oncol. Lett. 2016, 12, 83–88. [Google Scholar] [CrossRef] [Green Version]
  76. Wang, Z.; Que, H.; Suo, C.; Han, Z.; Tao, J.; Huang, Z.; Ju, X.; Tan, R.; Gu, M. Evaluation of the NMP22 BladderChek test for detecting bladder cancer: A systematic review and meta-analysis. Oncotarget 2017, 8, 100648–100656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  77. Willis, B.H. Spectrum bias—Why clinicians need to be cautious when applying diagnostic test studies. Fam. Pract. 2008, 25, 390–396. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. Usher-Smith, J.A.; Sharp, S.J.; Griffin, S.J. The spectrum effect in tests for risk prediction, screening, and diagnosis. BMJ 2016, 353, i3139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. Sciarra, A.; Di Lascio, G.; Del Giudice, F.; Leoncini, P.P.; Salciccia, S.; Gentilucci, A.; Porreca, A.; Chung, B.I.; Di Pierro, G.; Busetto, G.M.; et al. Comparison of the clinical usefulness of different urinary tests for the initial detection of bladder cancer: A systematic review. Curr. Urol. 2021, 15, 22–32. [Google Scholar] [CrossRef]
  80. Sathianathen, N.J.; Butaney, M.; Weight, C.J.; Kumar, R.; Konety, B.R. Urinary biomarkers in the evaluation of primary hematuria: A systematic review and meta-analysis. Bladder Cancer 2018, 4, 353–363. [Google Scholar] [CrossRef] [Green Version]
  81. D’Souza, N.; Hicks, G.; Benton, S.C.; Abulafi, M. The diagnostic accuracy of the faecal immunochemical test for colorectal cancer in risk-stratified symptomatic patients. Ann. R. Coll. Surg. Engl. 2020, 102, 174–179. [Google Scholar] [CrossRef] [PubMed]
  82. Bailey, S.E.R.; Abel, G.A.; Atkins, A.; Byford, R.; Davies, S.J.; Mays, J.; McDonald, T.J.; Miller, J.; Neck, C.; Renninson, J.; et al. Diagnostic performance of a faecal immunochemical test for patients with low-risk symptoms of colorectal cancer in primary care: An evaluation in the South West of England. Br. J. Cancer 2021, 124, 1231–1236. [Google Scholar] [CrossRef]
  83. Booth, R.; Carten, R.; D’Souza, N.; Westwood, M.; Kleijnen, J.; Abulafi, M. Role of the faecal immunochemical test in patients with risk-stratified suspected colorectal cancer symptoms: A systematic review and meta-analysis to inform the ACPGBI/BSG guidelines. Lancet Reg. Health Eur. 2022, 23, 100518. [Google Scholar] [CrossRef]
Cancers 15 00709 g001 550
Figure 1. Cut-off/boundary in the diagnostic pathway for study inclusion.
Figure 1. Cut-off/boundary in the diagnostic pathway for study inclusion.
Cancers 15 00709 g001
Cancers 15 00709 g002 550
Figure 2. Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart: Selection process for systematic review on the diagnostic performance of biomarkers for bladder cancer suitable for community settings.
Figure 2. Preferred Reported Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart: Selection process for systematic review on the diagnostic performance of biomarkers for bladder cancer suitable for community settings.
Cancers 15 00709 g002
Cancers 15 00709 g003 550
Figure 3. Hierarchical Summary of Receiving Operating Characteristics (HSROC) models for NMP-22 (a), UroVysion (b), uCyt+ (c) separately (top) and combined (d) (bottom).
Figure 3. Hierarchical Summary of Receiving Operating Characteristics (HSROC) models for NMP-22 (a), UroVysion (b), uCyt+ (c) separately (top) and combined (d) (bottom).
Cancers 15 00709 g003
Table
Table 1. Study Characteristics—Context, Population & Intervention.
Table 1. Study Characteristics—Context, Population & Intervention.
ContextPopulationIntervention
StudyCountryRecruitment TimelineDesignClinical Setting: (Hospital (H), Community (Co), Unclear (U))CasesControlsAgeGender
(M/F)		Risk FactorsSymptomsBiomarker/Test
Cases (C)All (Con)Non-Malignant (NM)Healthy Controls (HC)RangeMean (SD) or Median 1Smoking (N)Other (N)HaematuriaUTIsOther
VHNVH
Attallah et al. 2015 [30]EgyptNAProspectiveSingle centreNAH66945440C (44–81) NM
(45–70) HC
(45–65)		C (60.6 ± 10.6)
NM (58.6 ± 11.2) HC (56.2 ± 8.2)		106M:54F--x--CystitisEMA
NMP-52
EMA + NMP-52
Barbieri et al. 2011 [31]USA2001–2002RetrospectiveMulti-centreCohortH&Co721231---66 ± 13.6738M:565F474Y:829N-x-- Base model (age, gender, smoker, race, haematuria) + NMP-22
Base model (age, gender, smoker, race, haematuria) + NMP-22 + Cytology
Bhuiyan et al. 2003 [32]NANANANANAU70163-----------Telomerase
BTAstat
NMP-22
Telomerase + Hb Dipstick
Critselis et al. 2019 [33]Spain2009–2012ProspectiveSingle centreCross-sectionalH17985850-69.4 ± 12.2218M:46F--x--SPARC
Dahmcke et al. 2016 [34]Denmark2013–2015ProspectiveSingle centreEvaluationH99376--C (26–91) Con
(18–91)		C 69
Con 64		355M:120F--x---TERT, FGFR3 and SALL3, ONECUT2, CCNA1, BCL2, EOMES, and VIM
TERT
ONECUT2
VIM
SALL3
CCNA1
BCL2
EOMES
FGFR3
TERT + FGFR3
CCNA1 + ONECUT2 + BCL2 + EOMES + SALL3 + VIM
TERT + FGFR3 + CCNA1 + ONECUT2 + BCL2 + EOMES + SALL3 + VIM
TERT + FGFR3 + N + A1 + ONECUT2 + BCL2 + EOMES + SALL3
TERT + FGFR3 + CCNA1 + ONECUT2 + BCL2 + EOMES
TERT + FGFR3 + CCNA1 + ONECUT2 + BCL2
TERT + FGFR3 + CCNA1 + ONECUT2
TERT + FGFR3 + CCNA1
TERT + ONECUT2
Davidson et al. 2020 [35]New Zealand2018-2019ProspectiveMulti-centreCohortH&Co518338330(14–97)63.1 ± 16.2; median:65 71 Current: 293 Former: 520 Never1 Previous cancer diagnosis;
1 Radiation therapy of pelvis		xxxCystitis; Upper tract stones; vascular Prostate; Anticoagulation; Renal disease; Primary amyloidosisCxBladder Triage (CxbT)
CxbT + Imaging
Deininger et al. 2017 [36]Germany2006–2009RetrospectiveSingle centreCohortH68376--(18–93)median:67376M:68F61 Current: 82 Former: 274 Never: 27 n/a-xxBenign prostatic hyperplasia; Prostate canceruCyt+ 7
Dudderidge et al. 2020 [37]UK2016–2017ProspectiveMulti-centreCohortH74782782-(54–73)median:64487M:369F--xxx-ADXBLADDER
Eissa et al. 2007 [38]EgyptNAProspectiveSingle centreNAH2001158530C (37–78)
NM
(34–70)
HC
(30–78)		C (57.8 ± 10)
NM
(51 ± 11.8)
HC (54.1 ± 9.7)		-136Y:64N 285 Benign schistosomal urological lesions----RTA
hRT
hTERT
RTA + hRT
RTA + hTERT
hTR + hTERT
RTA + hRT + hTERT
HRT + Cytology
hRT + Cytology
RTA + hRT + Cytology
RTA + hTERT + Cytology
RTA + hRT + HTERT + Cytology
Eissa et al. 2007 [39]EgyptNAProspectiveSingle centreNAH154906030C (25–82) NM
(25–79)
HC
(27–50)		C (57 ± 12)
NM (51± 14)
HC (45.36 ± 5.2)		164M:80F-60 Benign schistosomal urologic lesions----MMP-2
MMP-9
TIMP-2
MMP-2 + TIMP-2
MMP-9 + TIMP2
MMP zymography
MMP-2 + Cytology
MMP-9 + Cytology
TIMP-2 + Cytology
MMP-2+ TIMP-2 ratio + Cytology
MMP-9+ TIMP-2 ratio + Cytology
MMP zymography + Cytology
Eissa et al. 2007 [40]EgyptNAProspectiveSingle centreNAH1201095455C (40–80)
NM
(17–80)
HC
(17–50)		C (55.0 ± 7.76)
NM
(49.8 ± 15.75)
HC
(21.8 ± 8.47)		------Cystitis; Ureteral stone; Renal stone; Nephritis; Combined disordersTGF-β1
VEGF
TGF-β1 + VEGF
TGF-β1 + VEGF + Cytology
TGF-β1 + Cytology
VEGF + Cytology
Eissa et al. 2009 [41]EgyptNAProspectiveSingle centreCase-controlH240218108110182 <45: 276 >45-278M:180F217Y:241N70 Schistosomal dysplasia; 28 schistosomal cystitis---benign prostatic hyperplasiaVEGF
bFGF
ANG
HGF
Eissa et al. 2010 [42]EgyptNANANANAU166212112100C (26–83)
NM
(18–75)
HC
(25–60)		C (60 ± 11) NM (48 ± 15) HC (49 ± 10)-------HYAL-1 Qual
HYAL-1 Semi-quant
Survivin Qual
Survivin Semi-quant
HYAL-1 + Survivin Qual
HYAL-1 + Survivin Semi-quant
HYAL-1 Qual + Cytology
HYAL-1 Semi-quant + Cytology
Survivin Qual + Cytology
Survivin Semi-quant + Cytology
HYAL-1 Qual + Survivin Qual + Cytology
HYAL-1 Semi-quant + Survivin Semi-quant + Cytology
Eissa et al. 2011 [43]EgyptNAProspectiveSingle centreNAH1321086048C (30–78) NM
(25–72) HC
(25–45)		C (56.60 ± 9.60)
NM (51.30 ± 14.0) HC (29.20 ± 10.60)		-------FN
RTA
CK20
CK20 + RTA
CK20 + FN + RTA
FN + Cytology
CK20 + Cytology
RTA+ Cytology
FN+ CK20+ Cytology
CK20 + RTA + Cytology
FN + CK20 + RTA + Cytology
Eissa et al. 2012 [44]EgyptNAProspectiveSingle centreNAH1001166551C (37–78)
NM
(31–75)
HC
(29–67)		C (57.40 ± 9.60)
NM
(52.6 ± 13.8)
HC
(46.9 ± 9.87)		-64Y:36N265 Schistosomiasis positive---Cystitis; Ureteral stone; Renal stone; Nephritis; Combined disordersRAR-B2
HYAL-1
RAR-β2 + HYAL-1
RAR-β2 + Cytology
HYAL-1 + Cytology
RAR-β2 + HYAL-1 + Cytology
Eissa et al. 2014 [45]Egypt2011-2012ProspectiveSingle centreNAH2111337162C (25–79)
NM
(25–66)
HC
(29–76)		C (52 ± 10) NM (50 ± 8) HC (51 ± 10)252M:91F
1 Unknown		101Y:243N193 Schistosomiasis positive----HURP
HURP + Cytology
Eissa et al. 2014 [46]Egypt2011–2012ProspectiveSingle centreNAH50502525C (37–79)
NM
(25–68)
HC
(28–79)		C (59.8 ± 9.3)
NM
(52.88 ± 9.4)
HC
(45.44 ± 11.9)		78M:22F38Y:62NSchistosomiasis dysplastic lesionsx-Renal stone; Urethral stricture; Chronic irritative symptomsHURP
HURP + Cytology
Fu et al. 2018 [47]China2016ProspectiveSingle centreNAH15282820-C (63 ± 13) Con (64 ± 13)179M:55F----xCystitis; urolithiasis; kidney carcinoma; benign bladder tumor 3CYFRA21-1
Grossman et al. 2005 [48]USA2001-2002ProspectiveMulti-centreTrialH&Co791252685567(18–96)58.7 ± 14.3759M:572F-120 Black non-Hispanic; 1089 White non-Hispanic; 84 Hispanic; 26 Asian;6 Other;6 Unknownx-xBenign prostatic hypertrophy; prostatitis; cystitis; inflammation; trigonitis; urinary tract infection; erythema; hyperplasia; squamous; metaplasia; cysts and polyps; calculi; trabeculations; other benign disease; kidney and genitourinary; other cancer history; non bladder; other active cancer; non bladderNMP-22
Horstmann et al. 2012 [49]GermanyNAProspectiveSingle centreCohortU3771177--(23–96)671169M:385F-605 with previous bladder cancer diagnosis4x-xUroVysion
uCyt+
NMP-22
Karnes et al. 2012 [50]NA2009–2011ProspectiveMulti-centreObservationalH&C58690---C (71 ± 10.0)
Con (64 ± 9.6)		450M:298F444Y:300N:
4 Unknown		-x--MADR Assay
FGFR3
FGFR3 + Cytology
Kelly et al. 2012 [51]UKNAProspectiveMulti-centreObservationalH2221455755700(49–73)60.7 ± 16.3
median:63		1040M:637F--xxxUrolithiasis; other malignancies; other benign conditions 3Mcm5
NMP-22
Mcm5 + NMP-22
Liu et al. 2016 [52]China2010–2014ProspectiveMulti-centreCohortH141135-135---------Gamma-synuclein (SNCG)
Meiers et al. 2007 [53]BelgiumNANANANAU1704544540---------UroVysion
O’Sullivan et al. 2012 [54]Australasia (NZ/Australia)NAProspectiveMulti-centreCohortH66419255164(59–77)median:69389M:96F76 Current: 215 Former: 194 Never423 European; 33 Māori; 29 Otherxx--uRNA-D
NMP-22
NMP-22 BladderChek
Cxbladder
Oertl et al. 2007 [55]NANAProspectiveNANAU5651510----Benign bladder tumour---Cystitis; pyelonephritis; urethritisNMP-22
Piaton et al. 2003 [56]FranceNAProspectiveMulti-centreNAH591561560(32–92)66.2 ± 12.8550M:144F 5--xxxDysuria; pollakiuria; cystitis; cystalgia; other conditionuCyt+
uCyt+ (+) Cytology
Poulakis et al. 2001 [57]NANAProspectiveNANAU406333212121(67–90)66.7485M:254F-386 with previous bladder carcinoma 4x--NMP-22
BTAstat
Saad et al. 2002 [58]UKNAProspectiveSingle centreNAH52685315(30–88)69.5 ± 11.1100M:20F----xBenign prostatic hyperplasia; urethral strictures; renal stone; cystodistension; 12 prostate cancer; 2 renal cell carcinomaBTAstat
NMP-22
Sajid et al. 2020 [59]Pakistan2018–2019NASingle centreCross-sectionalH215165--(24–75)53.08 ± 12.41300M:80F------NMP-22
NMP-22 + Cytology
Sanchez-Carbayo et al. 2000 [60]SpainNAProspectiveSingle centreNAH521374532--311M:90F 5---xxBenign diseases; lithiasis; Stenosis; benign prostate hyperplasiaBTF
BTF normalised
CK18
CK18 normalised
Sarosdy et al. 2006 [61]NANAProspectiveMulti-centreTrialU51422--(40–97)63298M:199F 6265 Current or former: 207 Never:
1 Unknown		440 White;
26 Black;
15 Hispanic;
4 Asian; 12 Other race 6		x-Inflammation or infection; benign prostatic hyperplasia; stones; urethral stricture; bladder polypUroVysion
Shang et al. 2021 [62]China2020–2021ProspectiveSingle centreNAH1281361360C (32–89) Con
(35–84)		C (67.5 ± 10.2)
Con (68.9 ± 13)		190M:74F--x-Irritative bladder symptoms, abdominal pain, hydronephrosis on the affected sideUroVysion
Todenhöfer et al. 2013 [63]GermanyNANASingle centreCohortU115693--(21–92)median:67645M:163F--xxIrritative voiding symptomsUroVysion
uCyt+
NMP-22
UroVysion + uCyt+
UroVysion + NMP-22
uCyt+ (+) NMP-22
UroVysion + uCyt+ (+) NMP-22
UroVysion + Cytology
uCyt+ (+) Cytology
NMP-22 + Cytology
uCyt+ (+) UroVysion + Cytology
UroVysion + NMP-22+ Cytology
uCyt+ (+) NMP-22 + Cytology
UroVysion + uCyt+ (+) NMP22+ Cytology
Base model (age and grade of haematuria) + UroVysion
Base model (age and grade of haematuria) + UroVysion + Cytology
Base model (age and grade of haematuria) + UroVysion + uCyt+
Base model (age and grade of haematuria) + UroVysion + uCyt+ (+) Cytology)
Todenhöfer et al. 2013 [64]Germany2004–2009NASingle centreCohortU502161116110(23–96)median:661599M:514F----x-UroVysion
uCyt+
NMP-22
Todenhöfer et al. 2013 [65]Germany2004–2009RetrospectiveSingle centreCohortU5431822--(18–97)median:651776M:589F--x-Irritative voiding symptomsUroVysion
uCyt+
NMP-22
van Kessel et al. 2016 [66]The Netherlands2006–2013ProspectiveSingle centreCohortH7480800C (38–91)
Con
(21–86)		C 68
Con 58		109M:45F--x--FGFR3
TERT
FGFR3, TERT, HRAS
TWIST1
OTX1 probe 1
OTX1 probe 2
OTX1 probe 3
OTX1 probe 4
ONECUT 2 probe 1
ONECUT 2 probe 2
ONECUT 2 probe 3
ONECUT 2 probe 4
ONECUT 2 probe 5
OTX1 all combined
ONECUT2 all combined
ONECUT probes 1 + 4
Optimal (age, FGFR3, TERT, HRAS, ONECUT2 probes 1 + 4, OTX1 probe 2, Twist)
van Kessel et al. 2017 [67]Sweden, Spain, The NetherlandsNAProspectiveMulti-centreCohortH971031030C
(38–110)
Con
(50–82)		C 71 Con 62181M:19F--x--FGFR3, TERT, HRAS and OTX1, ONECUT2, TWIST1
van Kessel et al. 2020 [68]The Netherlands2015–2017ProspectiveMulti-centreCohortH112726--(19–96)median:66485M:353F221 Current:
185 Former:
258 Never:
174 Not reported		-xx--FGFR3, TERT, Harvey HRAS and OTX1, ONECUT2, TWIST1
Extended Model consisting of: Existing model (univariate analysis incl. age, mutation, methylation) + type of haematuria + gender
Optimal model consisting of: Existing model (univariate analysis incl. age, mutation, methylation) + type of haematuria
van Valenberg et al. 2021 [2]USA2015ProspectiveMulti-centreCohortU59769--(23–84)65 ± 13467M:361F139 Current: 288 Former: 401 Never698 Caucasian; 80 Black;
32 Hispanic;
18 Other		xx--Xpert
UroVysion
Virk et al. 2017 [69]USA2008–2014RetrospectiveSingle centreCohortH1811961960(24–93)mean & median: 67259M:118F--x-Other Symptoms; miscellaneous indicationsUroVysion
Ward et al. 2022 [70]UKNAProspectiveMulti-centreNAH68147---median:
C 72.5:
Con 60		137M:78F-----Calculi,
benign prostatic hyperplasia, cystitis, inflammation, prostate cancer, and kidney cancer		TERT (promoter), FGFR3, PIK3CA, TP53, ERCC2, RHOB, ERBB2, HRAS, RXRA, ELF3, CDKN1A, KRAS, KDM6A, AKT1, FBXW7, ERBB3, SF3B1, CTNNB1, BRAF, C3orf70, CREBBP, CDKN2A, and NRAS
Wu et al. 2020 [71]China2015–2016ProspectiveSingle centreCase-controlH5358580C (61.5–74): Con (48–63.3)median:
C 68:
Con 55.5		83M:28F--xxUrinary calculi; benign prostatic hyperplasiaHOXA9
PCDH17
POU4F2
ONECUT2
HOXA9 + PCDH17 + POU4F2 + ONECUT2
Zhou et al. 2019 [72]China2007–2008ProspectiveMulti-centreTrialH3640319 (15–97)63.04 ± 13.313021M:938F--x--Inflammation; Renal tumor; benign bladder tumor; bladder tumor with non-transitional cell origin 3UroVysion
C cases, HC healthy controls, Con controls, NM non-malignant, NA not available, Y yes, N no, VH visible haematuria, NVH non-visible haematuria, UTIs urinary tract infections. 1 mean (SD) reported where applicable, unless otherwise stated, 2 numbers reported only for cases, 3 malignancies/tumors identified following examination, 4 number of participants with prior cancer diagnosis (less than half of included sample), 5 number of all participants recruited (cases and controls reported only for the group of interest), 6 number of all participants enrolled (prior exclusion), 7 also referred to as ImmunoCyt+.
Table
Table 2. List of Biomarkers/tests identified in the included studies.
Table 2. List of Biomarkers/tests identified in the included studies.
NoBiomarker/TestBiomarker CategoryDescriptionStudyTest PlatformReportSample SourceThreshold Used (Where Available/Appropriate)Performance Measures ReportedIncluded in Meta-Analysis
ProteinsmRNADNAIndividual MarkersMultiple MarkersUrineSerum
INDIVIDUAL MARKERS
1ADXBLADDER/Mcm5x Protein expression of Minichromosome Maintenance protein 5[37]ELISA 1x x SN, SP, NPV, AUC, Accuracy, FP, FNNo
[51]Immunofluorometric Assayx x 2150-cell;8500-cellSN, SP, PPV, NPVNo
2ANGx Protein expression of Angiogenin[41]ELISAx x 425 pg/mgSN, SP, PPV, NPV, AccuracyNo
3BCL2 xMethylation of BCL2 gene[34]qPCR 2x x SN, SP, PPV, NPVNo
4bFGFx Protein expression of Basic Fibroblast Growth Factor[41]ELISAx 19,444 fmol/mgSN, SP, PPV, NPV, AccuracyNo
5BTAstatx Protein expression of Complement factor H-related protein[32]Lateral Flow Testx x SN, SP, FPYes
[57]Lateral Flow Testx x SN, SP, PPV, NPV, Accuracy
[58]Lateral Flow Testx x SN, SP, PPV, Accuracy, FP, FN
6BTFx Protein expression of Bladder Tumour Fibronectin[60]Chemiluminescence Immunoassayx x 52.8 µg/LSN, SP, PPV, NPV, AccuracyNo
77 µg/g (when normalising with urine CREA)
7CCNA1 xMethylation of CCNA1 gene[34]qPCRx x SN, SP, PPV, NPVNo
8CK18x Protein expression of Cytokeratin 18[60]Chemiluminescence Immunoassayx x 7.4 µg/LSN, SP, PPV, NPV, AccuracyNo
7 µg/g (when normalising with urine CREA)
9CK20 x mRNA expression of Cytokeratin 20[43]RT-PCR 3x x SN, SP, PPV, NPV, AccuracyNo
10CYFRA21-1x Protein expression of Cytokeratin 19 fragments[47]Chemiluminescence Immunoassayx x 2.28 ng/mLSN, SP, AUCNo
x62.74 ng/mLSN, SP, AUC
xx0.66 ng/mLSN, SP, AUC
11EMAx Protein expression of Epithelial Membrane Antigen[30]ELISAx x 1.8 Ug/mLSN, SP, PPV, NPV, AUC, EfficiencyNo
12EOMES xMethylation of EOMES gene[34]qPCRx x SN, SP, PPV, NPVNo
13FGFR3 xMutation of FGFR3 geneddPCR 4x x 3SN, SP, PPV, NPV
[50]qPCRx x “negative”SN, SP, NPV
[66]SNaPshot assayx x SN, SP, Accuracy, FP, FN
14FNx Protein expression of Fibronectin[43]ELISAx x 41.7 ng/mgSN, SP, PPV, NPV, AccuracyNo
15 HGFx Protein expression of Hepatocyte Growth Factor[41] ELISAx x 1820 pg/mgSN, SP, PPV, NPV, AccuracyNo
16HOXA9 xMethylation of HOXA9 gene[71]High Resolution Meltingx x SN, SP, PPV, NPVNo
17HURP x mRNA expression of Hepatoma Up-regulated Protein[45]RT-PCRx x 0.0132SN, SP, PPV, NPVNo
[46]RT-PCR & AuNP 5 assayx x SN, SP, PPV, NPV, Accuracy, FP, FN
18HYAL-1x Hyaluronidase 1 activity[44]Zymographyx x 13.8 µU/mgSN, SP, PPV, NPV, AUC, AccuracyNo
x mRNA expression of Hyaluronidase 1 (Qual/Semi-quant)[42]RT-PCRx x 0.25 (Qual)SN, SP, PPV, NPV, Accuracy
19MMP-2x Protein expression of Matrix metalloproteinase 2[39]ELISAx x 1.9 ng/mgSN, SPNo
20MMP-9x Protein expression of Matrix metalloproteinase 9ELISAx x 8.7 ng/mgSN, SPNo
21NMP-22x Protein expression of Nuclear Matrix Protein 22[48]Lateral Flow Test (BladderChek)x x 10 IU/mLSN, SP, PPV, NPVYes
[54]Lateral Flow Test (BladderChek)x x SN, SP
[59]Lateral Flow Test (BladderChek)x x SN, SP, PPV, NPV, AUC, Accuracy, FP, FN
[32]ELISAx x 3.6 IU/mLSN, SP, FP
[49]ELISAx x 10 IU/mLSN, SP, Accuracy, FP
[51]ELISAx x 10 IU/mLSN, SP, PPV, NPV, AUC, Accuracy
[54]ELISAx x 7.5 IU/mLSN, SP, AUC
[55]ELISAx x 7.5 IU/mLSN
[57]ELISAx x 8.25 IU/mLSN, SP, PPV, NPV, Accuracy
[63]ELISAx x ≥10 IU/mLSN, SP, PPV, NPV, AUC
[64]ELISAx x ≥10 IU/mLSN, SP
[65]ELISAx x ≥10 IU/mLSN, SP
[58]Unclearx x ≥10 IU/mLSN, SP, PPV, Accuracy, FP, FN
22NMP-52x Protein expression of Nuclear Matrix Protein 52[30]ELISAx x 2.8 Ug/mLSN, SP, PPV, NPV, AUC, EfficiencyNo
23OTX1 xMethylation of OTX1 gene (probe 1/probe 2/probe 3/probe 4/all combined)[66]SNaPshot assayx x SN, SP, AUCNo
24ONECUT2 xMethylation of ONECUT2 gene[34]qPCRx x SN, SP, PPV, NPVNo
[71]High Resolution Meltingx x SN, SP, PPV, NPVNo
Methylation of ONECUT2 gene (probe 1/probe 2/probe 3/probe 4/probe 5/probe 1 + 4/all combined)[66]SNaPshot assayx x SN, SP, AUCNo
25PCDH17 xMethylation of PCDH17 gene[71]High Resolution Meltingx x SN, SP, PPV, NPVNo
26POU4F2 xMethylation of POU4F2 geneHigh Resolution Meltingx x SN, SP, PPV, NPVNo
27RAB-B2 xMethylation of RAB-B2 gene[44]PCRx x 0.065SN, SP, PPV, NPV, AUC, AccuracyNo
28SALL3 xMethylation of SALL3 gene[34]qPCRx x 0.5SN, SP, PPV, NPVNo
29SNCGx Protein expression of Gamma-Synuclein[52]ELISAx 1.874 ng/mLSN, SP, PPV, NPV, AUC, AccuracyNo
30SPARCx Protein expression of Secreted Protein Acidic and Rich in Cysteine[33]ELISAx x >0 ng/mL−1SN, SP, PPV, NPV, AUCNo
31Survivin x mRNA expression of Survivin (Qual/Semi-quant)[42]RT-PCRx x 0.25 (Qual)SN, SP, PPV, NPV, AccuracyNo
32Telomerase (TERT; hRT; hTERT; RTA) xMutation of TERT gene[34]ddPCRx x 5 (TERT C250T)/3 (TERT C228T)SN, SP, PPV, NPVNo
[66]SNaPshot assayx x SN, SP, AUC
x mRNA expression of Telomerase (hRT/hTERT)[38]RT-PCRx x SN, SP, PPV, NPV, Accuracy, FP, FNNo
x Telomerase activity (RTA)qPCRx x 18.08 copies per 2µlSN, SP, PPV, NPV, AUC, Accuracy, FP, FN
TRAP 6 assayx x 0.046SN, SP, PPV, NPV, AUC, Accuracy, FP, FN
Telomerase activity[43]TRAP assayx SN, SP, PPV, NPV, AccuracyNo
[32]TRAP assayx x SN, SP, FP
33TGF-β1x Protein expression of Transforming Growth Factor Beta 1[40]ELISAx 25 pg/mgSN, SP, PPV, NPV, AUC, AccuracyNo
34TIMP-2x Protein expression of Tissue Inhibitor of Metalloproteinase 2[39]ELISAx x 4.49 ng/mgSN, SPNo
35TWIST1 xMethylation of TWIST1 gene[66]qPCRx x SN, SP, AUCNo
36VEGFx Protein expression of Vascular Endothelial Growth Factor[40]ELISAx x 38 pg/mgSN, SP, PPV, NPV, AUC, AccuracyNo
[41]ELISAx x 52ng/mgSN, SP, AUC, FP, FNNo
37VIM xMethylation of VIM gene[34]qPCRx x 2SN, SP, PPV, NPVNo
MULTIPLE MARKERS
1CCNA1 + ONECUT2 + BCL2 + EOMES + SALL3 + VIM xMethylation of CCNA1, ONECUT2, BCL2, EOMES, SALL3 and VIM genes[34]qPCR xx SN, SPNo
2CK20 + RTAxx mRNA expression of Cytokeratin 20 + telomerase activity[43]RT-PCR + TRAP assay xx SN, SP, PPV, NPV, AccuracyNo
3CK20 + FN + RTAxx mRNA expression of Cytokeratin 20 + protein expression of Fibronectin + telomerase activityRT-PCR + ELISA + TRAP assay xx SN, SP, PPV, NPV, AccuracyNo
4CxBladder x mRNA expression of CDK1, HOXA13, MDK, IGFBP5 and CXCR2 genes[35]qPCR xx <4SN, SP, NPV, AccuracyNo
[54]predefined as the classifier scores which gave 85% to 90% SP in this cohortSN, SP, AUC
5EMA + NMP-52x Protein expression of Epithelial Membrane Antigen and Nuclear matrix protein 52[30]ELISA xx 4.0 Ug/mLSN, SP, PPV, NPV, AUC, EfficiencyNo
6FGFR3, TERT, HRAS xMutation of FGFR3, TERT and HRAS genes[66]SNaPshot assay xx SN, SP, AUCNo
7FGFR3, TERT, HRAS and OTX1, ONECUT2, TWIST1 xMutation of FGFR3, TERT and HRAS genes and methylation of OTX1, ONECUT2 and TWIST1 genes[67]SNaPshot assay and qPCR xx 0.1917196SN, SPNo
[68]SNaPshot assay and qPCR xx SN, SP, PPV, NPV, AccuracyNo
8HOXA9 + PCDH17 + POU4F2 + ONECUT2 xMethylation of HOXA9, PCDH17, POU4F2 and ONECUT2 gene[71]High Resolution Melting x SN, SP, PPV, NPV, AUCNo
9HYAL-1 + Survivin x mRNA expression of Hyaluronidase 1 + Survivin (Qual/Semi-quant)[42]RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
10MADR Assayx xMethylation of TWIST1 and NID2 genes, mutation of FGFR3 gene and protein expression of matrix metalloproteinase 2[50]PCR + qPCR + ELISA xx TWIST1 < 139K; NID2 < 680; MMP-2 < 1.100 ng/mL; FGF3 negativeSN, SP, NPVNo
11Mcm5 + NMP-22x Protein expression of Minichromosome Maintenance protein 5 and Nuclear Matrix Protein 22[51]Immunofluorometric Assay + ELISA xx 4200-cell; 2800-cell; 1900-cell; 1000-cell + 10 IU/mLSN, SPNo
12MMP-2 + TIMP-2x Protein expression of Matrix metalloproteinase 2 + Tissue inhibitor of metalloproteinase 2[39]ELISA xx 0.93 ng/mgSN, SPNo
13MMP-9 + TIMP-2x Protein expression of Matrix metalloproteinase 9 + Tissue inhibitor of metalloproteinase 2[39]ELISA xx 3.81 ng/mgSN, SPNo
14MMP-2/MMP-9x MMP-2/MMP-9 activity[39]Zymography xx SN, SP No
15RAB-B2 + HYAL-1x xMethylation of RAB-B2 + Hyaluronidase 1 activity[44]PCR + Zymography xx SN, SP, PPV, NPV, AccuracyNo
16Telomerase (RTA + hRT; RTA + hTERT; hTR + hTERT; RTA + hRT + hTERT)xx Telomerase activity (RTA) + telomerase mRNA expression (hRT)[38]TRAP assay + RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
Telomerase activity (RTA) + telomerase mRNA expression (hTERT)TRAP assay + qPCR xx SN, SP, PPV, NPV, AccuracyNo
x Telomerase mRNA expression (hTR + hTERT)RT-PCR + qPCR xx SN, SP, PPV, NPV, AccuracyNo
xx Telomerase activity (RTA) + telomerase mRNA expression (hTR + hTERT)TRAP assay + RT-PCR + qPCR xx SN, SP, PPV, NPV, AccuracyNo
17TERT (promoter), FGFR3, PIK3CA, TP53, ERCC2, RHOB, ERBB2, HRAS,
RXRA, ELF3, CDKN1A, KRAS, KDM6A, AKT1, FBXW7, ERBB3, SF3B1,
CTNNB1, BRAF, C3orf70, CREBBP, CDKN2A, and NRAS		 xMutation of TERT (promoter), FGFR3, PIK3CA, TP53, ERCC2, RHOB, ERBB2, HRAS,
RXRA, ELF3, CDKN1A, KRAS, KDM6A, AKT1, FBXW7, ERBB3, SF3B1,
CTNNB1, BRAF, C3orf70, CREBBP, CDKN2A, and NRAS		[70]NGS 7 xx a positive test was
defined as detection of any one of the 443 mutations in a cpDNA sample at >0.9% VAF for chr5:129528A/G or >0.5% VAF for all other coordinates.		SN, SPNo
18TERT, FGFR3, SALL3, ONECUT2, CCNA1, BCL2, EOMES, and VIM xMutation of TERT and FGFR3 genes plus methylation of SALL3, ONECUT2, CCNA1, BCL2, EOMES, and VIM genes[34]ddPCR/qPCR xx SN, SP, PPV, NPV, AUCNo
19TERT + ONECUT2 xMutation of TERT gene plus methylation of ONECUT2 genesddPCR/qPCR xx SN, SPNo
20TERT + FGFR3 xMutation of TERT and FGFR3 genesddPCR/qPCR xx SN, SPNo
21TERT + FGFR3 + CCNA1 xMutation of TERT and FGFR3 genes plus methylation of CCNA1 genesddPCR/qPCR xx SN, SPNo
22TERT + FGFR3 + CCNA1 + ONECUT2 xMutation of TERT and FGFR3 genes plus methylation of CCNA1 and ONECUT2 genesddPCR/qPCR xx SN, SPNo
23TERT + FGFR3 + CCNA1+ ONECUT2 + BCL2 xMutation of TERT and FGFR3 genes plus methylation of CCNA1, ONECUT2 and BCL2 genesddPCR/qPCR xx SN, SPNo
24TERT + FGFR3 + CCNA1 + ONECUT2 + BCL2 + EOMES xMutation of TERT and FGFR3 genes plus methylation of CCNA1, ONECUT2, BCL2 and EOMES genesddPCR/qPCR xx SN, SPNo
25TERT + FGFR3 + CCNA1 + ONECUT2 + BCL2 + EOMES + SALL3 xMutation of TERT and FGFR3 genes plus methylation of CCNA1, ONECUT2, BCL2, EOMES and SALL3 genesddPCR/qPCR xx SN, SPNo
26TGF-β1 + VEGFx Protein expression of Transforming growth factor beta 1 + Vascular endothelial growth factor[40]ELISA xx SN, SP, PPV, NPV, AccuracyNo
27uCyt+9x Protein expression of Carcinoembryonic antigen and sulphate mucin glycoproteins[36]Immunofluorescence xx SN, SP, FP, FNYes
[49]Immunofluorescence xx at least 1 clear positive cellSN, SP, Accuracy, FP
[56]Immunofluorescence xx SN, SP, FP
[63]Immunofluorescence xx at least 1 clear positive cellSN, SP, PPV, NPV, AUC
[64]Immunofluorescence xx at least 1 clear positive cellSN, SP
[65]Immunofluorescence xx at least 1 clear positive cellSN, SP, Accuracy, FP, FN
28uCyt+ (+) NMP-22x Protein expression of Carcinoembryonic antigen and Sulphate mucin glycoproteins and nuclear matrix protein 22[63]Immunofluorescence + ELISA xx uCyt+ => 1 clear positive cell/NMP22 = > 10 IU/mL SN, SP, PPV, NPV, AUCNo
29uRNA-D x mRNA expression of CDC2, HOXA13, MDK and IGFBP5 genes[54]qPCR xx predefined as the classifier scores which gave 85% SP in this cohortSN, SP, AUCNo
30UroVysion xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21[49]FISH 8 xx SN, SP, Accuracy, FPYes
[53]FISH xx SN, SP
[61]FISH xx SN, SP, PPV, NPV, Accuracy
[62]FISH xx SN, SP
[63]FISH xx SN, SP, PPV, NPV, AUC
[64]FISH xx SN, SP
[65]FISH xx SN. SP,
[2]FISH xx SN, SP, PPV, NPV, Accuracy
[69]FISH xx SN, SP, PPV, NPV, Accuracy
[72]FISH xx SN, SP, PPV, NPV, AUC, FP, FN
31UroVysion + uCyt+x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and Sulphate mucin glycoproteins[63]FISH + Immunofluorescence xx SN, SP, PPV, NPV, AUCNo
32UroVysion + NMP-22x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of nuclear matrix protein 22[63]FISH + ELISA xx NMP-22 => ≥10IU/mLSN, SP, PPV, NPV, AUCNo
33UroVysion + uCyt+ (+) NMP-22x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and Sulphate mucin glycoproteins and nuclear matrix protein 22[63]FISH + Immunofluorescence + ELISA xx SN, SP, PPV, NPV, AUCNo
34Xpert x mRNA expression of ABL1, CRH, IGF2, UPK1B and ANXA10[2]qPCR xx SN, SP, PPV, NPV, AUC, AccuracyNo
BIOMARKER/TESTS and CYTOLOGY
1CK20 + Cytology x mRNA expression of Cytokeratin 20 + Cytology[43]RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
2CK20 + RTA + Cytologyxx mRNA expression of Cytokeratin 20 + telomerase activity + CytologyRT-PCR + TRAP assay xx SN, SP, PPV, NPV, AccuracyNo
3FGFR3 + Cytology xMutation of FGFR3 gene + Cytology[50]qPCR xx SN, SP, PPVNo
4FN + Cytologyx Protein expression of Fibronectin + Cytology[43]ELISA xx SN, SP, PPV, NPV, AccuracyNo
5FN + CK20 + Cytologyxx Protein expression of Fibronectin + mRNA expression of Cytokeratin 20 + CytologyELISA + RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
6FN + CK20 + RTA + Cytologyxx Protein expression of Fibronectin + mRNA expression of Cytokeratin 20 + telomerase activity + CytologyELISA + RT-PCR + TRAP assay xx SN, SP, PPV, NPV, AccuracyNo
7HURP + Cytology x mRNA expression of Hepatoma Up-regulated Protein + Cytology[45]RT-PCR xx 0.0132SN, SP, PPV, NPV, AccuracyNo
[46]RT-PCR & AuNP assay xx SN, SP, PPV, NPV, AccuracyNo
9HYAL-1 + Cytology x mRNA expression of Hyaluronidase 1 + Cytology (Qual/Semi-quant)[42]RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
x Hyaluronidase activity + Cytology[44]Zymography xx SN, SP, PPV, NPV, AccuracyNo
10HYAL-1 + Survivin + Cytology x mRNA expression of Hyaluronidase 1 and Survivin + Cytology (Qual/Semi-quant)[42]RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
11MMP-2 and MMP-9 zymography + Cytology x Activity of Matrix metalloproteinase 2 and Matrix metalloproteinase 9 + Cytology[39]Zymography xx SN, SP No
12 MMP-2 + Cytologyx Protein expression of Matrix metalloproteinase 2 + CytologyELISA xx 1.9 ng/mgSN, SP No
13 MMP-9 + Cytology x Protein expression of Matrix metalloproteinase 9 + CytologyELISA xx 8.7 ng/mgSN, SP No
14MMP-2 + TIMP-2 ratio + Cytology x Protein expression of Matrix metalloproteinase 2 and Tissue inhibitor of metalloproteinase 2 + CytologyELISA xx 0.93 ng/mgSN, SP No
15MMP-9 + TIMP-2 ratio + Cytology x Protein expression of Matrix metalloproteinase 9 + Tissue inhibitor of metalloproteinase 2 + CytologyELISA xx 3.81 ng/mgSN, SP No
16NMP-22 + Cytology x Protein expression of Nuclear Matrix Protein 22 + Cytology[59]Lateral Flow Test (BladderChek) xx SN, SP, PPV, NPV, AUC, Accuracy, FP, FNNo
[63]ELISA xx ≥10 IU/mLSN, SP, PPV, NPV, AUCNo
17RAB-B2 + Cytology xMethylation of RAB-B2 gene + Cytology[44]PCR xx 0.065SN, SP, PPV, NPV, AccuracyNo
18RAB-B2 + HYAL-1 + Cytologyx xMethylation of RAB-B2 gene + Hyaluronidase activity + Cytology PCR + Zymography xx SN, SP, PPV, NPV, AccuracyNo
19Survivin + Cytology x mRNA expression of Survivin + Cytology[42]RT- PCR xx SN, SP, PPV, NPV, AccuracyNo
20Telomerase + Cytology (RTA + Cytology; hRT + Cytology; hTERT + Cytology; RTA + hRT + Cytology; RTA + hTERT + Cytology; hTR + hTERT + Cytology; RTA + hRT + HTERT + Cytology)x Telomerase activity (RTA) + Cytology [38]TRAP assay xx SN, SP, PPV, NPV, AccuracyNo
[43]TRAP assay x SN, SP, PPV, NPV, AccuracyNo
x mRNA expression of Telomerase (hRT/hTERT) + Cytology [38]RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
qPCR xx SN, SP, PPV, NPV, AccuracyNo
xx Telomerase activity (RTA) + mRNA expression of Telomerase (hRT/hTERT) + Cytology TRAP assay + RT-PCR xx SN, SP, PPV, NPV, AccuracyNo
TRAP assay + qPCR xx SN, SP, PPV, NPV, AccuracyNo
x mRNA expression of Telomerase (hTR + hTERT) + Cytology RT-PCR + qPCR xx SN, SP, PPV, NPV, AccuracyNo
xx Telomerase activity (RTA) + mRNA expression of Telomerase (hTR + hTERT) + Cytology TRAP assay + RT-PCR + qPCR xx SN, SP, PPV, NPV, AccuracyNo
21TGF-β1 + Cytology x Protein expression of Transforming Growth Factor Beta 1 + Cytology[40]ELISA xx SN, SP, PPV, NPV, AccuracyNo
22 TGF-β1 + VEGF + Cytology x Protein expression of Transforming Growth Factor Beta 1 + Vascular Endothelial Growth Factor + CytologyELISA xx SN, SP, PPV, NPV, AccuracyNo
23 TIMP-2 + Cytology x Protein expression of Tissue inhibitor of metalloproteinase 2 + Cytology[39]ELISA xx 4.49 ng/mgSN, SP No
24uCyt+ + Cytologyx Protein expression of Carcinoembryonic Antigen and Sulphate Mucin Glycoproteins + Cytology[56]Immunofluorescence xx SNNo
[63]Immunofluorescence xx SN, SP, PPV, NPV, AUCNo
25uCyt+ (+) NMP-22 + Cytology x Protein expression of Carcinoembryonic antigen, Sulphate mucin glycoproteins and Nuclear matrix protein 22 + CytologyImmunofluorescence + ELISA xx SN, SP, PPV, NPV, AUCNo
26UroVysion + Cytology xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 FISH xx SN, SP, PPV, NPV, AUCNo
27UroVysion + uCyt+ (+) Cytologyx xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and sulphate mucin glycoproteins + CytologyFISH + Immunofluorescence xx SN, SP, PPV, NPV, AUCNo
28UroVysion + NMP-22 + Cytology x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of nuclear matrix protein 22 + CytologyFISH + ELISA xx SN, SP, PPV, NPV, AUCNo
29UroVysion + uCyt+ (+) NMP-22 + Cytologyx xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen, Sulphate mucin glycoproteins and nuclear matrix protein 22 + CytologyFISH + Immunofluorescence + ELISA xx SN, SP, PPV, NPV, AUCNo
30VEGF + Cytology x protein expression of Vascular endothelial growth factor + Cytology[40]ELISA xx SN, SP, PPV, NPV, AccuracyNo
BIOMARKER/TESTS and PREDICTION MODELS
1Base model (age and grade of haematuria) + UroVysion xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21[63]FISH x AUCNo
2Base model (age and grade of haematuria) + UroVysion + uCyt+
x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and Sulphate mucin glycoproteinsFISH + Immunofluorescence x AUCNo
3Base model (age and grade of haematuria) + UroVysion + Cytology
 xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and Sulphate mucin glycoproteinsImmunofluorescence x AUCNo
4Optimal (age + FGFR3, TERT, HRAS, ONECUT2 probes 1 + 4, OTX1 probe 2, TWIST) xMethylation of ONECUT2 and OXT1 genes and mutation of FGFR3, TERT and HRAS genes[66]SNaPshot assay xx Various cut-offs: 0.1213372; 0.1917196; 0.3547327; 0.4975214SN, SP, PPV, NPVNo
5Extended Model consisting of: Existing model (univariate analysis incl. age, mutation, methylation) + type of haematuria + gender xMutation of FGFR3, TERT and HRAS and methylation of OTX1, ONECUT2 and TWIST1[68]SNaPshot assay and qPCR xx AUCNo
6Optimal model consisting of: Existing model (univariate analysis incl. age, mutation, methylation) + type of haematuria xMutation of FGFR3, TERT and HRAS and methylation of OTX1, ONECUT2 and TWIST2SNaPshot assay and qPCR xx SN, SP, PPV, NPV, AUC, FP, FNNo
BIOMARKER/TESTS, PREDICTION MODELS and CYTOLOGY
1Base model (age, gender, smoker, race, haematuria) + NMP-22 x Protein expression of Nuclear Matrix Protein 22[31]Lateral Flow Test (BladderChek) xx AUC, AccuracyNo
2Base model (age, gender, smoker, race, haematuria) + NMP-22+ Cytology x Protein expression of Nuclear Matrix Protein 22 + CytologyLateral Flow Test (BladderChek) xx AUC, AccuracyNo
3Base model (age, gender, smoker, race, haematuria) + UroVysion + uCyt+ (+) Cytology x xAneuploidy of Chromosomes 3, 7 and 17 and loss of chromosome locus 9p21 + protein expression of Carcinoembryonic antigen and sulphate mucin glycoproteins + Cytology[63]FISH + Immunofluorescence x AUCNo
BIOMARKER/TESTS and OTHER
1CxBladder Triage (CxBT) + Imaging x mRNA expression of CDK1, HOXA13, MDK, IGFBP5, CXCR2[35]qPCR xx <4.0 to indicate specialist assessment was requiredSN, SP, NPV, AccuracyNo
2Telomerase + Hb Dipstickx Telomerase activity and haematuria[32]PCR x SN, SPNo
SN: Sensitivity; SP: Specificity; PPV: Positive Predictive Value; NVP: Negative Predictive Value; AUC: Area Under Curve; FP: False Positive; FN: False Negative; 1 ELISA: Enzyme-linked immunosorbent assay; 2 qPCR: Real time Polymerase Chain Reaction; 3 RT-PCR: Reverse Transcript Polymerase Chain Reaction; 4 ddPCR: droplet digital Polymerase Chain Reaction; 5 AuNPs: Gold nanoparticles; 6 TRAP: Telomeric Repeat Amplification Protocol; 7 NGS: Next Generation Sequencing; 8 FISH: Fluorescence In Situ Hybridisation; 9 also referred to as ImmunoCyt+.
Table
Table 3. Reported Outcomes for Novel Biomarkers by Biomarker Category.
Table 3. Reported Outcomes for Novel Biomarkers by Biomarker Category.
Biomarker CategoryBiomarkers/TestsStudies IdentifiedCountry of Research, Total Population (N) 1Clinical FeaturesRisk Factors Short Term Outcomes Authors’ Conclusions (Applicable in the Health System Where the Biomarker Was Evaluated)
HaematuriaUTIsOther Smoking (N) 1Other (N) 1SNSPPPVNPV
VHNVH
PROTEINSADXBLADDER/Mcm5Kelly 2012 [51] UK, 1677xxxUrolithiasis; other malignancies; other benign conditionsNANA73.0%68.4%NA96.4%
-
Useful predictive clinical role e.g., to target imaging and cystoscopic diagnostic procedures for higher risk patients
Dudderidge 2020 [37]UK, 856xxxNANANA36.0–85.0% 247.0–96.0% 217.0–65.0% 290.0–96.0% 2
-
Not affected by benign conditions, such as urinary tract infections or the presence of Haematuria
-
Easy-to perform ELISA test, compatible with general laboratory equipment available in most hospital settings
-
Can provide results within 3 h, without the need for a pathologist
-
Demonstrates potential to replace cytology as an adjunctive test in bladder cancer diagnosis
-
Cheaper than cytology (no pathologist needed)
EMA + NMP-52Attallah 2015 [30]Egypt, 160x NACystitis NANA94.0%80.0%75.0%95.0%NA
uCyt+ + NMP-22Todenhöfer 2013 [63]Germany, 808xxIrritative voiding symptomsNANA90.4%35.9%19.0%95.8%NA
mRNAsCxBladder TriageDavidson 2020 [35]New Zealand, 884xxxCystitis; upper tract stones; vascular prostate; anticoagulation; renal disease; primary amyloidosisNAPrevious cancer diagnosis N = 1; Radiation therapy of pelvis N = 189.4%59.0%NA98.9%
-
Reduces the need for referral to secondary care
-
Reduces the need for cystoscopies which are invasive
-
Risk of avoiding a cystoscopy and missing a significant bladder cancer
CxBladder Triage + ImagingDavidson2020 [35]98.1%98.4%NA99.9%
Xpertvan Valenberg 2021 [2]USA, 828xxNANACurrent N = 139; Former N = 288; Never; N = 401Caucasian N = 698; Black N = 80; Hispanic N = 32; Other N = 1878.0%84.0%27.0%98.0%
97.0% 3
99.0% 4
-
Fast non-invasive method of discriminating BC from other non-serious causes of HM
-
Reliable, easy to use, fast, and operator independent
-
Can potentially justly avoid 98% of cystoscopies
-
Promising tool for identifying Haematuria patients with a low likelihood of BC who might not need to undergo cystoscopy
DNAsFGFR3 + TERT + HRAS + OXT1 + ONECUT2 + TWISTvan Kessel 2017 [67]Sweden, Spain, Netherlands, 200xNANANANA93.2%85.6%42.2%99.2%
-
Preferred over current clinical practice (i.e., cystoscopy)
-
Reduces cystoscopies in the primary diagnostic workup
-
Not feasible in acute Haematuria cases (takes several days to complete)
-
Availability of urine assay to GPs could expedite referrals
-
In primary setting may add value as could reduce diagnostic delay for female patients with Haematuria
van Kessel 2020 [68]Netherlands, 838xxNANACurrent N = 221; Former N = 185; Never N = 258; Not reported N = 174NA92.0%73.0%34.0%98.0%
PROTEIN + DNAUroVysion + NMP-22 + uCyt+Todenhöfer 2013 [63]Germany, 808xxIrritative voiding symptomsNANA83.5%74.1%34.9%96.4%
-
Relatively high costs of performing multiple urine tests
VH: Visible Haematuria; NVH: non-visible Haematuria; UTIs: Urinary Tract Infections; N: (number); SN: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value. 1 Numbers reported where available. 2 Range reported to include all different cut-off points used for testing. 3 In patients with non-visible Haematuria. 4 In patients with visible Haematuria.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Papavasiliou, E.; Sills, V.A.; Calanzani, N.; Harrison, H.; Snudden, C.; di Martino, E.; Cowan, A.; Behiyat, D.; Boscott, R.; Tan, S.; et al. Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis. Cancers 2023, 15, 709. https://doi.org/10.3390/cancers15030709

AMA Style

Papavasiliou E, Sills VA, Calanzani N, Harrison H, Snudden C, di Martino E, Cowan A, Behiyat D, Boscott R, Tan S, et al. Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis. Cancers. 2023; 15(3):709. https://doi.org/10.3390/cancers15030709

Chicago/Turabian Style

Papavasiliou, Evie, Valerie A. Sills, Natalia Calanzani, Hannah Harrison, Claudia Snudden, Erica di Martino, Andy Cowan, Dawnya Behiyat, Rachel Boscott, Sapphire Tan, and et al. 2023. "Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis" Cancers 15, no. 3: 709. https://doi.org/10.3390/cancers15030709

APA Style

Papavasiliou, E., Sills, V. A., Calanzani, N., Harrison, H., Snudden, C., di Martino, E., Cowan, A., Behiyat, D., Boscott, R., Tan, S., Bovaird, J., Stewart, G. D., Walter, F. M., & Zhou, Y. (2023). Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis. Cancers, 15(3), 709. https://doi.org/10.3390/cancers15030709

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop