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ORIGINAL ARTICLE
Year : 2021  |  Volume : 17  |  Issue : 2  |  Page : 426-433

Diagnostic accuracy of Raman spectroscopy for the diagnosis of bladder cancer: A systematic review and meta-analysis


1 Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Korea
2 Department of Pathology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, Seoul, Korea
3 Korea Photonics Technology Institute, Gwangju, Korea

Date of Submission20-Oct-2019
Date of Decision16-Dec-2019
Date of Acceptance27-Jan-2020
Date of Web Publication11-Jun-2021

Correspondence Address:
Jae Heon Kim
Department of Urology, Soonchunhyang University Seoul Hospital, Soonchunhyang University Medical College, 59 Daesagwanro, Yongsangu, Seoul
Korea
Ki Hyun Kim
Korea Photonics Technology Institute, Gwangju
Korea
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.JCRT_891_19

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 > Abstract 


Introduction: Although several studies have been conducted to evaluate the feasibility of Raman spectroscopy (RS) for the diagnosis of bladder cancer (BCa), it is difficult to use RS in real clinical settings based on the current limited evidence. Therefore, we performed a systematic review and meta-analysis to assess the diagnostic accuracy of RS in BCa.
Materials and Methods: Comprehensive literature searches were performed in the PubMed/Medline, Embase, and Cochrane Library databases up to March 2019. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, this study included reports according to the participant, intervention, comparator, outcomes, and study design approach. The methodological quality of the included studies was evaluated according to questionnaires and criteria suggested by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The quantitative outcomes included diagnostic accuracy (sensitivity and specificity).
Results: Fifteen studies were included for qualitative analysis and four studies (BCa cases, n = 139; control cases n = 107) were included in this analysis by screening the full text of the remaining articles based on the inclusion and exclusion criteria through a systematic review. The pooled sensitivity and specificity of RS were 0.91 (95% confidence interval [CI]: 0.85–0.95) and 0.93 (95% CI: 0.86–0.97), respectively. The among-study heterogeneity was statistically significant in the specificity results (Cochran Q statistic, P = 0.015; I2 statistic, 71.3%) but not in the sensitivity results (Cochran Q statistic, P = 0.189; I2 statistic, 37.2%).
Conclusions: RS showed the potential to be an efficient tool with high accuracy for detecting malignant bladder lesions. More studies with in vivo real-time settings are warranted to validate our results.

Keywords: Bladder cancer, diagnosis, meta-analysis, optical, Raman spectroscopy


How to cite this article:
Kim DK, Kim YH, Lee HY, Lee S, Doo SW, Yang WJ, Song YS, Kim KH, Kim JH. Diagnostic accuracy of Raman spectroscopy for the diagnosis of bladder cancer: A systematic review and meta-analysis. J Can Res Ther 2021;17:426-33

How to cite this URL:
Kim DK, Kim YH, Lee HY, Lee S, Doo SW, Yang WJ, Song YS, Kim KH, Kim JH. Diagnostic accuracy of Raman spectroscopy for the diagnosis of bladder cancer: A systematic review and meta-analysis. J Can Res Ther [serial online] 2021 [cited 2021 Sep 23];17:426-33. Available from: https://www.cancerjournal.net/text.asp?2021/17/2/426/318118




 > Introduction Top


Bladder cancer (BCa) is the ninth-most frequently diagnosed cancer worldwide and has the thirteenth-highest mortality rate.[1] The recurrence rate of BCa is very high, with a recurrence rate of >50% within 5 years.[2] Therefore, early detection is essential to prevent recurrence and progression. Moreover, complete resection of BCa through early and thorough detection is important for improving patient prognoses.

The gold standard diagnostic method for BCa is cystoscopy, followed by histopathological examinations of biopsies, but it is invasive, unpleasant, and expensive.[3] Although voided urine cytology is a noninvasive method used to identify BCa, its sensitivity is very low and its use is limited to the detection of low-grade disease.[3] Moreover, several urine-based biomarkers have also been developed but have limited sensitivity to detect low-grade BCa.[4] Currently, transurethral resection of bladder tumors under general anesthesia is the main treatment for BCa.

Recently, Raman spectroscopy (RS) has attracted attention in the field of cancer detection.[5] RS, which can measure the inelastic scattering of a photon and conveniently investigate cellular physiology and tissue physiology on a sub-micron length scale, has been widely adopted as an analytical tool in many research fields.[6],[7] Over the past two decades, RS has been comprehensively examined for use in the diagnosis and evaluation of cancer and precancerous lesions in various body organs.[8],[9],[10],[11] The disadvantage of RS is the dramatic limitation caused by strong fluorescent signals, which hinders its clinical application.[12] The first Raman fiber probe-based analysis in the bladder was conducted by Crow et al. using frozen tissue.[13] These basic works prepared a path for the in vivo translation of the Raman-based diagnostic approach. One of the most promising recent studies in this regard was the use of high-volume Raman spectroscopic probes for the diagnosis of BCa in vivo by Draga et al.[14] The relatively low specificity of this latest in vivo study highlighted the challenge to the clinical translation of RS.

Although several studies have been conducted to evaluate the utility of RS in the diagnosis of BCa, it is difficult to use RS as a clinical tool based on the results. Moreover, those previous studies were inconclusive due to small numbers, inadequate or diverse sampling methods, different diagnostic algorithms, and analytic tools, and different settings of RS. Hence, to draw a conclusion from current evidence of RS regarding the diagnostic accuracy in BCa, both qualitative and quantitative analyses using systematic reviews and meta-analyses are necessary. Therefore, we performed a systematic review and meta-analysis to assess the diagnostic accuracy of RS in BCa in this study.


 > Materials and Methods Top


Search strategy

Comprehensive literature searches were performed in the PubMed/Medline, Embase, and Cochrane Library databases up to March 2019 The search terms used included “bladder cancer” or “bladder tumor” or “bladder carcinoma or “bladder malignancy” and “raman” or “Raman spectroscopy” or “raman spectra” or “raman spectrometry.” There were no restrictions on language or research type in the conduct of the initial literature searches. Two authors (DKK and JHK) independently reviewed the titles and abstracts according to the inclusion and exclusion criteria and reviewed the identified articles. If an opinion was split between the two reviewers, it was resolved by consensus through discussion with the other authors.

Inclusion and exclusion criteria

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, studies were included according to the Participant, Intervention, Comparator, Outcomes, Study design approach if all of the following requirements were met:[15] (1) blood samples or cancer tissues of patients with BCa; (2) performance of RS; (3) blood samples or cancer tissues of healthy volunteers as control data; (4) values for true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs) were reported in sufficient detail to determine the sensitivity and specificity at specified cutoff values for evaluating the diagnostic accuracy; and (5) the studies were reported in original articles.

Studies were excluded if any of the following criteria were met: (1) studies that involved nonhuman subjects, (2) studies without a control group, including case reports and case series, and (3) reviews or duplicate reports.

Data extraction

Two authors (DKK and JHK) reviewed the full text of included studies and extracted the data for each trial independently. The data were extracted at the trial level. Any conflicts in the extracted data between the two authors were resolved via discussion. The extracted data included details on study design, inclusion and exclusion criteria, the country in which the study was conducted, the number of patients or samples, the sample type, the main Raman algorithm type, laser diode, and the type of RS. The TPs, TN, FP, and FN were also extracted directly or calculated using the sensitivity, specificity, positive predictive value, and negative predictive value in each study.

Study quality assessments and quality of evidence

The methodological quality of the included studies was evaluated according to questionnaires and criteria suggested by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.[16] The risk of bias and applicability judgments in the QUADAS-2 tool was assessed based on four domains, including patient selection, index test, reference standard, and flow and timing. The risk of bias and concerns about applicability were rated at three levels (low, high, and unclear). The QUADAS-2 assessment was performed by Review Manager 5.3 (The Nordic Cochrane Center, The Cochrane Collaboration, Copenhagen, Denmark). Certainty was evaluated using assessments of the following criteria: study design, limitation (risk of bias), indirectness, inconsistency, imprecision, and publication bias. Based on these criteria, we assessed the quality of evidence on a four-level scale (high, moderate, low, and very low).

Statistical analysis

The accuracy of RS for the diagnosis of BCa was evaluated by pooled sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratios (NLRs) values, along with corresponding 95% confidence intervals (CIs). Moreover, a summary receiver operating characteristic (SROC) curve was also created by examining the effect of the threshold on the result using the Moses et al. method.[17] To further explore the statistical heterogeneity between the trials, the inconsistency index (I2) statistic and Chi-squared test were applied. Either a P < 0.05 for the Cochran Q statistic or an I2 statistic >50% indicated significant heterogeneity between the trials.[18] A random-effects model was applied. If we included 10 studies or more investigating a particular outcome, we planned to use Deeks' funnel plots to assess small study effects. However, there were fewer than 10 studies for this review. We also conducted Deeks' funnel plot asymmetry test to investigate publication bias.[19] All the above statistical analyses were performed using Meta-Disc Version 1.4, Spain (http://www.hrc.es/investigacion/metadisc_en.htm).[20]


 > Results Top


Systematic review process

The systematic review process using the PRISMA statement is summarized in [Figure 1]. Only published studies were included to avoid publication bias. After an initial literature search, we identified a total of 72 articles, which were reduced to 39 after the removal of duplicates. Then, 20 articles were excluded by manual review of the titles and abstracts. Finally, nineteen studies were included for qualitative analysis, and four studies were included in the final quantitative analysis though full-text screening of the remaining articles based on the inclusion and exclusion criteria. [Table 1] shows the characteristics of the fifteen studies included in the meta-analysis.
Figure 1: Preferred Reporting Items for Systematic Reviews and Meta-Analysis flowchart

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Table 1: Characteristics of the 4 studies included in the meta-analysis

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Among all included studies, three studies were available for data extraction that those studies investigated the diagnostic performance of RS in vitro[3],[12],[29] and only one study was conducted in vivo.[14] None of the eligible studies were published in English. Two studies were conducted in China. Others were conducted in the United Kingdom and the Netherlands. The sample types were tissue or peripheral blood. The type of RS used was not consistent throughout the studies.

Outcomes

The extracted data of the two studies were pooled and analyzed. We measured the overall diagnostic accuracy by calculating sensitivity, specificity, PLR, NLR, and diagnostic odds ratio (DOR). The pooled sensitivity and specificity of RS were 0.86 (95% CI: 0.72–0.94) and 0.81 (95% CI: 0.63–0.93), respectively [Figure 2]. The among-study heterogeneity was not significant in the specificity results (Cochran Q statistic, P = 0.878; I2 statistic, 0.0%) and the sensitivity results (Cochran Q statistic, P = 0.583; I2 statistic, 0.0%). The pooled PLR and NLR were 4.414 (95% CI: 2.107–9.247) and 0.177 (95% CI: 0.085–0.371), respectively. The DOR of RS demonstrated high accuracy (25.518; 95% CI: 7.312–89.058). The area under the curve (AUC) of the SROC curve was 0.97 (95% CI: 0.92–1.02) [Figure 3].
Figure 2: The pooled sensitivity and specificity of Raman spectroscopy

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Figure 3: The area under the curve of summary receiver operating characteristic curves

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Quality assessment, qualitative risk of bias, and publication bias

Two authors independently assessed the methodological quality of each study according to the QUADAS-2 tool.[16] The evaluation of the risk of bias and concerns regarding the applicability of the included studies are shown in [Figure 4] and [Figure 5]. The risk of bias in all included studies was low in all domains except patient selection. The applicability concerns were also low in all domains except for the index test of two studies.
Figure 4: Risk of bias and applicability concerns graph

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Figure 5: Risk of bias and applicability concerns summary

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The Deeks' funnel plot asymmetry test was conducted to evaluate publication bias in the included studies, and there was no significant publication bias (P = 0.29).


 > Discussion Top


The noninvasive diagnosis of BCa is an ongoing challenge. Various characteristics of cancer cells in the urine have been used in noninvasive diagnostic methods.[27] Nevertheless, urinary cytology, which is the only clinical method currently used, has high specificity but low sensitivity, especially in low-grade diseases.[32] Several urine-based examinations have been studied over the past 20 years,[33] but none of them are recommended in the current guidelines for the diagnosis and treatment of BCa.[34]

RS is a powerful analytical method that enables the measurement of chemical compounds in complex biological samples, such as biological fluids, cells, and tissues.[35] It is a proprietary noninvasive detection technology which has obtained many interesting results in various cancers.[3] The first application of RS in urology was recorded when Feld et al. found that BCa had a higher nucleic acid concentration and lower lipid content than normal bladder tissue in 1995.[36] The bladder accessibility in fiber-optic based cystoscopy has led several groups worldwide to explore the use of RS to improve real-time diagnosis in the bladder in combination with confocal microscopy. Various approaches ranging from RS urine cytology to in vivo probe-based work, with or without fluorescence cystoscopy surveillance, have been studied to improve targeting.[8] Moreover, several groups have performed studies, in which RS was used to diagnose BCa.[3],[12],[13],[14],[21],[28],[29] This meta-analysis aimed to elucidate two main clinical needs, the development of noninvasive biomarkers and the development of a real-time in vivo detection system. For both of these, identifying and establishing the validity of the current data for the use of RS in the detection of BCa are crucial. This meta-analysis provided quantitative results of the diagnostic accuracy of RS in detecting BCa, which will facilitate more studies to develop noninvasive biomarkers and real-time in vivo endoscopic devices.

We reported that RS had high sensitivity and specificity of 90% for the diagnosis of BCa, with an overall AUC of 0.97 (95% CI: 0.92–1.02). Crow et al. reported that RS provided sensitivities and specificities up to 90% in an 8-pathology group algorithm for bladder tissue samples (normal, cystitis, carcinoma in situ, various grade of urothelial cancer groups, and adenocarcinoma).[21] They validated their initial in vitro results with in vivo tests, which showed an overall accuracy of 84% for differentiating benign and malignant bladder samples using a fiber-optic-based clinical Raman system of snap-frozen bladder samples.[13] The study by Draga et al.[14] is the only in vivo translational study included in this analysis. They applied a high volume-based Raman probe to examine the invasiveness of BCa. They reported a diagnostic sensitivity of 85% and specificity of 79%. The results demonstrated the possibility of discriminating normal tissue from malignant tissue in the bladder by applying RS using a small fiber-based system. Despite the low number of samples, they demonstrated the potential use of their method for grading identified bladder wall lesions in endoscopy.[8] Barman et al.[28] used a confocal Raman probe to diagnose BCa and obtained a sensitivity of 85.7% and a specificity of 100%.

The biggest obstacle in the utilization of RS in real practice is inherent characteristics of RS is that the signal from Raman scattering is so weak. Raman photon is produced in 106–108 scattering photons due to the inherently weak cross-sections. The inefficient scattering requires high laser power and long acquisition times, which can lead to sample damage and unfeasible time constraints in real applications.[3] With the discovery of surface-enhanced RS (SERS) by Fleischman et al.,[37] RS has developed rapidly. SERS is a phenomenon whereby the Raman scattering signal is greatly improved when sample molecules are attached to the surface of a metal nanostructure.[3] The process has been able to deliver ultra-sensitive properties up to single-molecule levels, which has raised more interest for its use in cancer diagnostics. Li et al. measured the serum SERS spectra of normals and BCa patients using an artificial intelligence technique called genetic algorithm linear discriminate analysis.[3] They reported diagnostic accuracy of 94.5%, sensitivity of 90.9%, and specificity of 100% in discriminating the serum SERS spectra of BCa patients from normals. This result demonstrated that serum SERS associated with the genetic algorithm linear discriminate analysis technique had the potential to characterize and detect BCa through peripheral blood noninvasively.

Canetta et al. developed a new technology for modulated RS (MRS) that extracted Raman spectra from the background fluorescence signal to identify human urothelial cells (SV-HUC-1) and BCa cells (MGH-U1 in urine samples.[12] They reported that MRS discriminated between SV-HUC-1 and MGH cells with a sensitivity of 98% and specificity of 95%.

We included three in vitro studies and one in vivo study in the present analysis. The other 15 studies included in the qualitative analysis were excluded from the final meta-analysis because they were pilot studies or investigated the pioneer RS setting. The main limitations of our study were in the heterogeneities in the Raman settings, sample types, validation sets, analytic algorithms, and the small number of cases. It is possible that this heterogeneity lowered the quality of the data used for the analyses. In addition, our results have a low level of evidence due to a small number of included studies and significant heterogeneity. Nevertheless, to the best of our knowledge, this is the first meta-analysis evaluating the diagnostic accuracy of RS for BCa. Moreover, we conducted GRADE quality assessment of the RS evidence. This means that we made an effort to qualify the confidence of our work by using GRADE according to recent meta-analysis guidelines.[38] Given the approaches we used, our study adds further evidence in support of the utility of RS in the diagnosis of BCa. Well-designed high-quality studies will be needed to increase the level of evidence of these results.


 > Conclusions Top


RS is an optical diagnostic technology that has great potential for the detection of malignant bladder lesions. At the same time, it has the advantages of being noninvasive, conducted in real-time, and ease-of-use. However, to overcome limitations based on conclusions from a small number of included samples, more in vitro and also in vivo studies have to be explored. Moreover, before considering real-time use in real clinical settings, the performance of RS must be further analyzed and standardized.

Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) (No. 2018R1D1A1A02085980) and Basic Science Research Program through the NRF funded by the Ministry of Education (2017R1D1A1B04034840), and also supported by Soonchunhyang University Research Fund.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
 > References Top

1.
Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder cancer incidence and mortality: A global overview and recent trends. Eur Urol 2017;71:96-108.  Back to cited text no. 1
    
2.
Kirkali Z, Chan T, Manoharan M, Algaba F, Busch C, Cheng L, et al. Bladder cancer: Epidemiology, staging and grading, and diagnosis. Urology 2005;66:4-34.  Back to cited text no. 2
    
3.
Li S, Li L, Zeng Q, Zhang Y, Guo Z, Liu Z, et al. Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms. Sci Rep 2015;5:9582.  Back to cited text no. 3
    
4.
Bansal N, Gupta A, Sankhwar SN, Mahdi AA. Low- and high-grade bladder cancer appraisal via serum-based proteomics approach. Clin Chim Acta 2014;436:97-103.  Back to cited text no. 4
    
5.
Tu Q, Chang C. Diagnostic applications of Raman spectroscopy. Nanomedicine 2012;8:545-58.  Back to cited text no. 5
    
6.
Zhang Y, Hong H, Cai W. Imaging with Raman spectroscopy. Curr Pharm Biotechnol 2010;11:654-61.  Back to cited text no. 6
    
7.
Huser T, Chan J. Raman spectroscopy for physiological investigations of tissues and cells. Adv Drug Deliv Rev 2015;89:57-70.  Back to cited text no. 7
    
8.
Kallaway C, Almond LM, Barr H, Wood J, Hutchings J, Kendall C, et al. Advances in the clinical application of Raman spectroscopy for cancer diagnostics. Photodiagnosis Photodyn Ther 2013;10:207-19.  Back to cited text no. 8
    
9.
Lui H, Zhao J, McLean D, Zeng H. Real-time Raman spectroscopy for in vivo skin cancer diagnosis. Cancer Res 2012;72:2491-500.  Back to cited text no. 9
    
10.
Huang Z, McWilliams A, Lui H, McLean DI, Lam S, Zeng H. Near-infrared Raman spectroscopy for optical diagnosis of lung cancer. Int J Cancer 2003;107:1047-52.  Back to cited text no. 10
    
11.
Teh SK, Zheng W, Ho KY, Teh M, Yeoh KG, Huang Z. Diagnostic potential of near-infrared Raman spectroscopy in the stomach: Differentiating dysplasia from normal tissue. Br J Cancer 2008;98:457-65.  Back to cited text no. 11
    
12.
Canetta E, Mazilu M, De Luca AC, Carruthers AE, Dholakia K, Neilson S, et al. Modulated Raman spectroscopy for enhanced identification of bladder tumor cells in urine samples. J Biomed Opt 2011;16:37002.  Back to cited text no. 12
    
13.
Crow P, Molckovsky A, Stone N, Uff J, Wilson B, WongKeeSong LM. Assessment of fiberoptic near-infrared Raman spectroscopy for diagnosis of bladder and prostate cancer. Urology 2005;65:1126-30.  Back to cited text no. 13
    
14.
Draga RO, Grimbergen MC, Vijverberg PL, van Swol CF, Jonges TG, Kummer JA, et al. In vivo bladder cancer diagnosis by high-volume Raman spectroscopy. Anal Chem 2010;82:5993-9.  Back to cited text no. 14
    
15.
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015;4:1.  Back to cited text no. 15
    
16.
Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: A tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 2003;3:25.  Back to cited text no. 16
    
17.
Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: Data-analytic approaches and some additional considerations. Stat Med 1993;12:1293-316.  Back to cited text no. 17
    
18.
Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557-60.  Back to cited text no. 18
    
19.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088-101.  Back to cited text no. 19
    
20.
Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: A software for meta-analysis of test accuracy data. BMC Med Res Methodol 2006;6:31.  Back to cited text no. 20
    
21.
Crow P, Uff JS, Farmer JA, Wright MP, Stone N. The use of Raman spectroscopy to identify and characterize transitional cell carcinoma in vitro. BJU Int 2004;93:1232-6.  Back to cited text no. 21
    
22.
Prieto MC, Matousek P, Towrie M, Parker AW, Wright M, Ritchie AW, et al. Use of picosecond Kerr-gated Raman spectroscopy to suppress signals from both surface and deep layers in bladder and prostate tissue. J Biomed Opt 2005;10:44006.  Back to cited text no. 22
    
23.
de Jong BW, Schut TC, Maquelin K, van der Kwast T, Bangma CH, Kok DJ, et al. Discrimination between nontumor bladder tissue and tumor by Raman spectroscopy. Anal Chem 2006;78:7761-9.  Back to cited text no. 23
    
24.
Stone N, Hart Prieto MC, Crow P, Uff J, Ritchie AW. The use of Raman spectroscopy to provide an estimation of the gross biochemistry associated with urological pathologies. Anal Bioanal Chem 2007;387:1657-68.  Back to cited text no. 24
    
25.
Grimbergen MC, van Swol CF, van Moorselaar RJ, Uff J, Mahadevan-Jansen A, Stone N. Raman spectroscopy of bladder tissue in the presence of 5-aminolevulinic acid. J Photochem Photobiol B 2009;95:170-6.  Back to cited text no. 25
    
26.
Harvey TJ, Hughes C, Ward AD, Faria EC, Henderson A, Clarke NW, et al. Classification of fixed urological cells using Raman tweezers. J Biophotonics 2009;2:47-69.  Back to cited text no. 26
    
27.
Shapiro A, Gofrit ON, Pizov G, Cohen JK, Maier J. Raman molecular imaging: A novel spectroscopic technique for diagnosis of bladder cancer in urine specimens. Eur Urol 2011;59:106-12.  Back to cited text no. 27
    
28.
Barman I, Dingari NC, Singh GP, Kumar R, Lang S, Nabi G. Selective sampling using confocal Raman spectroscopy provides enhanced specificity for urinary bladder cancer diagnosis. Anal Bioanal Chem 2012;404:3091-9.  Back to cited text no. 28
    
29.
Wang L, Fan JH, Guan ZF, Liu Y, Zeng J, He DL, et al. Study on bladder cancer tissues with Raman spectroscopy. Guang Pu Xue Yu Guang Pu Fen Xi 2012;32:123-6.  Back to cited text no. 29
    
30.
Hughes C, Iqbal-Wahid J, Brown M, Shanks JH, Eustace A, Denley H, et al. FTIR microspectroscopy of selected rare diverse sub-variants of carcinoma of the urinary bladder. J Biophotonics 2013;6:73-87.  Back to cited text no. 30
    
31.
Canetta E, Riches A, Borger E, Herrington S, Dholakia K, Adya AK. Discrimination of bladder cancer cells from normal urothelial cells with high specificity and sensitivity: Combined application of atomic force microscopy and modulated Raman spectroscopy. Acta Biomater 2014;10:2043-55.  Back to cited text no. 31
    
32.
Koss LG, Deitch D, Ramanathan R, Sherman AB. Diagnostic value of cytology of voided urine. Acta Cytol 1985;29:810-6.  Back to cited text no. 32
    
33.
Vrooman OP, Witjes JA. Urinary markers in bladder cancer. Eur Urol 2008;53:909-16.  Back to cited text no. 33
    
34.
Babjuk M, Böhle A, Burger M, Capoun O, Cohen D, Compérat EM, et al. EAU guidelines on non-muscle-invasive urothelial carcinoma of the bladder: Update 2016. Eur Urol 2017;71:447-61.  Back to cited text no. 34
    
35.
Kong K, Kendall C, Stone N, Notingher I. Raman spectroscopy for medical diagnostics – From in vitro biofluid assays to in-vivo cancer detection. Adv Drug Deliv Rev 2015;89:121-34.  Back to cited text no. 35
    
36.
Feld MS, Manoharan R, Salenius J, Orenstein-Carndona J, Roemer TJ, Brennan III JF, et al. Detection and characterization of human tissue lesions with near-infrared Raman spectroscopy. Advances in Fluorescence Sensing Technology II 1995;2388.  Back to cited text no. 36
    
37.
Fleischman M, Hendra P, McQuillan A. Surface-enhanced Raman scattering from silver particles on polymer-replica substrates. Chem Phys Lett 1974;26:123-.  Back to cited text no. 37
    
38.
Brozek JL, Akl EA, Jaeschke R, Lang DM, Bossuyt P, Glasziou P, et al. Grading quality of evidence and strength of recommendations in clinical practice guidelines: Part 2 of 3. The GRADE approach to grading quality of evidence about diagnostic tests and strategies. Allergy 2009;64:1109-16.  Back to cited text no. 38
    


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