








































This is an open access article under the terms of a license that permits non-commercial use, provided the original work is properly cited.  
© 2023 The Authors. Société Internationale d'Urologie Journal, published by the Société Internationale d'Urologie, Canada.

SIUJ.ORG SIUJ  •  Volume 4, Number 4  •  July 2023

Key Words Competing Interests Article Information

Urinary bladder neoplasms, diagnosis, 
spectroscopy, Raman spectroscopy,  
infrared spectroscopy

None declared. Received on January 15, 2023 
Accepted on March 17, 2023 
This article has been peer reviewed. 
 
Soc Int Urol J. 2023;4(4):321–334

DOI: 10.48083/NCZW3015

321

REVIEW

Point-of-Care Diagnosis of Bladder Cancer With 
Vibrational Spectroscopy: A Systematic Review

Arthur Yim,1,5  Matthew Alberto,1  Varun Sharma,2,3 Bayden Wood,4 Jaishankar Raman3 
Lih-Ming Wong,1 Joseph Ischia,1 Damien Bolton1

1Department of Urology, Austin Health, Australia 2Department of Surgery, The University of Melbourne, Australia 3Department of Cardiac Surgery, Austin Health, 
Australia 4Centre for Biospectroscopy, School of Chemistry, Monash University, Australia 5Young Urology Researchers Organisation (YURO), Melbourne, Australia

Abstract

Introduction Vibrational spectroscopy (VS) is a new and rapidly evolving technology in cancer diagnostics. 
Originating from analytical chemistry, VS evaluates vibrations of nuclei to produce a unique “biological fingerprint.” 
While multiple studies have been published on this technology and physician awareness has increased, no systematic 
review has evaluated the role of VS in bladder cancer (BCa) tissue diagnosis.

Methods To conduct this systematic review, we searched the MEDLINE, Embase, and Cochrane databases for 
studies that used Raman spectroscopy (RS), surface-enhanced RS (SERS), infrared spectroscopy (IR) or near-infrared 
spectroscopy (NIRS) to analyze human BCa specimens. Studies using animal tissue or liquid biopsies were excluded. 
We synthesized the evidence by comparing modalities, study design, data analysis techniques, and diagnostic 
accuracy. The quality of evidence was evaluated by the QUADAS-2 tool.

Results Out of 362 results, 20 studies met our inclusion criteria. There has been growing interest in VS use in BCa, 
with 50% of the studies published in the past 5 years. RS was the most commonly used modality (65%), followed by IR 
(20%) and SERS (10%). Only one study compared RS to IR (5%). The mean sample size was 44 patients (range, 6–214). 
To date, there have been only 2 in vivo studies, with the remaining ex vivo studies performed with large variation 
in tissue preparation, data analysis, and reporting. Advancements in fiber optic probes and machine-learning data 
analysis techniques, and increased computational power have improved diagnostic accuracy up to 98% sensitivity and 
100% specificity.

Conclusions VS shows high potential for BCa diagnosis, but there is a need for uniform reporting methods 
and studies with adequate sample sizes to validate the models. RS has shown promising results, with ongoing 
improvements in fiber optic probes allowing its integration into conventional cystoscopes. While no single VS 
modality has proven to be perfect, a multimodal approach is likely required to establish its value in clinical practice.

Introduction

Challenges in bladder cancer diagnosis
Bladder cancer (BCa) is among the 10 most common cancer types worldwide, with approximately 550 000 new cases 
annually[1]. The recurrence and progression rates vary greatly, based on factors such as tumour grading, size, depth 
of invasion, and presence of carcinoma in situ (CIS). At 5 years, the recurrence rates range from 31% to 78% and the 
progression rates range from 1% to 45%[2]. The stage of cancer is the most important prognostic factor, highlighting 

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the importance of techniques for accurately and 
efficiently diagnosing BCa stage in controlling disease 
progression.

The current gold standard diagnostic method is 
white-light cystoscopy, followed by biopsies or transure-
thral resection of bladder tumour (TURBT) for histo-
pathological examination. While white-light cystoscopy 
is reliable for papillary tumours, it has limitations in 
detecting flat carcinomas such as CIS, dysplasia, and 
multifocal lesions. Integrated findings from 2 fluores-
cence cystoscopy registration studies revealed that only 
38% of CIS lesions[3] and 71% of CIS cases were detected 
using white-light cystoscopy[3,4]. Although newer opti-
cal techniques such as fluorescence and narrow-band 
imaging can improve tumour visualization, they do 
not contribute to histopathologic diagnosis[5]. Thus, 
repeated biopsies are often performed, a procedure that 
not only is costly but also does not provide real-time 
point-of-care diagnosis. Delays in diagnosis and treat-
ment may lead to increased morbidity and mortality, 
particularly for high-risk invasive BCa. A more rapid 
and cost-effective diagnostic method would potentially 
enhance management of BCa patients.

Vibrational spectroscopy
Spectroscopy has attracted attention in cancer diagnosis 
in recent years. Originating from analytical chemistry, 
vibrational spectroscopy (VS) is a powerful technique 
that measures the vibrational energy of molecules[6]. 
The 3 most common techniques used in cancer detection 
are infrared (IR), Raman (RS), and near-infrared (NIR) 
spectroscopy. The key characteristics, advantages, 
and limitations of each technique are summarized in 
Table 1[7].

Abbreviations 
ANN artificial neural network
AUC area under the curve
BCa bladder cancer
CIS carcinoma in situ
FFPE formalin-fixed paraffin-embedded
FT-IR Fourier transform infrared
IR infrared
NIRS near-infrared spectroscopy
PCA principal component analysis
QCL quantum-cascade laser
QUADAS-2 Quality Assessment of Diagnostic Accuracy Studies
RS Raman spectroscopy
SERS  surface-enhanced Raman scattering
TURBT transurethral resection of bladder tumour
VS vibrational spectroscopy

such as protein/lipid ratio, tumour characteristics, 
and immune cell interactions, encompassing a multi-
marker approach to cancer diagnostics[13]. In recent 
years, proof-of-concept studies in breast, colon, skin, 
and bladder cancers have demonstrated that VS can be 
employed as a label-free, non-destructive, and non-inva-
sive approach to specimen analysis, facilitating the iden-
tification of specific “spectral biomarkers”[14].

Objective of this review
Despite the increasing literature and public awareness 
into the role of VS in BCa tissue diagnosis, no systematic 
review has covered the topic. This review aims to

• Provide a historical overview of the development of 
VS in BCa diagnosis.

• Compare the 3 most common VS techniques—IR, RS, 
and NIRS.

• Assess the feasibility and diagnostic accuracy of 
studies.

• Identify future areas of research based on the current 
literature.

TABLE 1. 

Overview and comparison of the three common vibrational spectroscopy methods used in tissue analysis

Mid-IR NIR Raman

Basis

Method of detection 
Mid-infrared light absorption 

using polychromic light source
Near-infrared light absorption 
using polychromic light source

Inelastic light scattering using 
monochromic laser excitation

Wavenumber 400–4000 cm-1 4000–10 000 cm-1 50–4000 cm-1

Wavelength 2500–25 000 nm 1000–2500 nm 2500–20 0000 nm

Sampling Methods

Sample interface Historically complicated
Straightforward  

point-and-shoot, versatile
Straightforward  
point-and-shoot

Sample preparation Complex Minimal Minimal 

Penetration of glass / 
quartz / plastic

No Yes Yes

Probes Probes fragile Fiber optic probe compatible Fiber optic probe compatible 

Sample Size

Area 1–2 mm Up to several cm 0.3–1 mm

Depth < 15 µm < 1 mm to several mm
Surface spectra from bulk 

material possible 

Sensitivity
Water High Medium Low 

Coloured samples No issue No issue Susceptible to fluorescence 

Specificity Chemical High Medium High 

Observed Bands Fundamentals—narrow
Combinations and overtones—

overlapping 
Fundamentals—narrow

IR: Infrared; NIR: Near-infrared

IR absorption spectroscopy relies on the absorption 
of mid-IR radiation by the sample, where molecules 
absorb specific frequencies of light based on their unique 
structure. This allows for identification and quanti-
fication of the molecular compound in a sample. The 
exact frequency required to excite a molecular vibra-
tion depends on the mass of the atoms involved in the 
vibration and the type of chemical bonds between these 
atoms, which can be influenced by a molecule’s structure 
and chemical microenvironment[8].

RS is a complementary method based on inelastic 
light scattering. In this method, the sample is illumi-
nated with monochromatic laser light, and the inter-
actions between molecules and photons leads to the 
scattering of light. The energy of the scattered light 
reflects the molecular composition of the sample[8]. RS 
offers several advantages over IR spectroscopy, includ-
ing less interference from water and glass, less sample 
preparation, and higher spatial resolution. However, RS 
is inherently weaker and requires longer spectral scan-
ning times to achieve an adequate signal-to-noise ratio. 
Another significant limitation of RS is the presence of 
strong fluorescent signals, particularly in the analysis of 
organic tissues, dramatically reducing its specificity and 
hampering its clinical translation.

Surface-enhanced Raman scattering (SERS) spec-
troscopy is the newest technique that aims to overcome 
the limitations of conventional RS. SERS uses plasmonic 
substrates, such as silver or gold colloids, to amplify the 
Raman signal of molecules adsorbed onto the metal 
surface[9]. This technique holds great potential in identi-
fying BCa in liquid biopsies such as urine or serum, but 
its use in tissue is still emerging[10].

Compared with its counterparts, mid-IR and RS, 
near-infrared (NIR) spectroscopy has received less 
historical research attention. Contrary to the mid-IR 
region, which relies on distinct fundamental absorption 
bands, the NIR region contains overlapping overtone 
and combination bands that have lower intensity and 
reduced specificity[11]. However, recent advancements 
in quantum mechanical calculations and computational 
power have greatly expanded the use of NIR in modern 
analytical applications[12]. NIR offers advantages such 
as easier sample handling, low cost, greater sample pene-
tration, and rapid acquisition times.

All 3 techniques—IR, RS, and NIRS—can analyze 
biological tissues, which comprise the superposition 
of biochemical components such as DNA, proteins, 
lipids, and carbohydrates. VS can capture the unique 
“biological fingerprint” of the entire sample under 
analysis, rather than focusing on single elements like 
cell morphology in histopathology or tumour DNA in 
assays. VS has the potential to evaluate the entire pheno-
typic response of the host, including tissue changes 

Methods
This review was performed in accordance with the 
PRISMA 2020 statement[15]. It was registered with the 
International Prospective Register of Systematic Reviews 
(PROSPERO #CRD42022349369), where the protocol 
and search strategy are available.

Eligibility criteria
A summary of eligibility criteria for this review, 
fol low i ng t he PIC O f r a me work (Popu l at ion, 
Inter vention, Comparison, Outcome) is detailed 
in  Table 2. Studies of humans with  either ex vivo  or 
in vivo vibrational spectral analysis of bladder tissue 
for the detection of cancer were included. There were 
no demographic restrictions. Tissue samples required 
analysis by VS in a laboratory or operating theatre for 
inclusion. Types of VS considered included RS, NIRS, 
and IR spectroscopy. Histopathology was required 
as the reference standard. Publications reporting any 
diagnostic capability of VS were included. There were 
no restrictions on language or publication date. Studies 
involving animal tissue, pooled cells, tissue markers, 
or liquid biopsies were excluded. Only peer-reviewed 

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REVIEW Point-of-Care Diagnosis of Bladder Cancer With Vibrational Spectroscopy: A Systematic Review

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articles were considered, with review articles, opinion 
papers, and commentaries excluded.

Search strategy
An online electronic database search was undertaken 
using the platforms of MEDLINE, Embase, and 
Cochrane Library. The search encompassed the entire 
database content. The initial search employed broad 
MeSH terms including “Urinary Bladder Neoplasms/” 
and (Spectrum analysis, Raman OR spectroscopy, near-
infrared/ OR Spectrophotometry, Infrared/)while also 
extracting key terminology/key words from reviews and 
a sample of potentially relevant primary data studies. A 
gold test set of relevant studies was used to ensure the 
search terms retrieved all of the gold test set. The results 
of the literature search were downloaded into EndNote 
X9 software (Clarivate Analytics, London, UK) and 
exact article duplicates were removed using the duplicate 
tool in that software program. Subsequently, a reference 
review of identified articles and reviews was conducted 
to identif y any additional relevant articles. Grey 
literature was searched via guidelines from the European 
Association of Urology (EAU), American Urological 
Association (AUA), and National Institute for Health 
and Care Excellence (NICE) and ongoing clinical trials 
through ClinicalTrials.gov, The ISRCTN registry, and 
the World Health Organization International Clinical 
Trials Registry Platform (ICTRP) portal. The authors 
of trials were contacted for preliminary or unpublished 
results for potential inclusion in the review. Full search 
strategy and results are provided in Online Appendix 1.

Selection process
Following completion of the search, all identified 
citations were uploaded into Covidence systematic 
review software (Veritas Health Innovation, Melbourne, 
Australia) and duplicates were removed. The screening 

Results
A total of 363 articles were identified through liter-

ature search, of which 263 were excluded on screening.  
Of 29 full-text articles assessed for eligibility, 20 were 
included in this review (Figure 1).

Characteristics of the included studies
Table 3 provides a summary of the characteristics of the 
included studies, while Table 4 contains a summary of 
all data collected. There has been a growing interest in 
spectroscopy and BCa since the publication of the first 
study in 2004, with 10 of 20 studies published in the 
past 5 years. The most commonly used modality was RS 

FIGURE 1.

PRISMA 2020 diagram of study selection

Records removed before screening:
Duplicate records removed 

(n = 71)

Records screened
(n = 292)

Records excluded
(n = 263)

Reports not retrieved
(n = 1) 

Reports sought for retrieval
(n = 29)

Reports excluded:
Wrong study design (n = 3)
Wrong tumour type (n = 3)
Wrong intervention (n = 1)
Conference poster (n = 1)

Reports assessed for eligibility
(n = 28)

Studies included in review
(n = 20)

S
cr

ee
ni

ng

Identi�cation of studies via databases and registers

Id
en

ti
�c

at
io

n
In

cl
ud

ed

Records identi�ed from:
Databases (n = 362)

Medline (n = 160)
Embase (n = 200)
Cochrane (n = 2)

Registers (n = 1)

TABLE 2.

Criteria for studies included in this systematic review 

Inclusion criteria

Population Human bladder cancer tissue 

Investigation

Vibrational spectroscopy modalities:
1. Raman (RS)
2. Fourier transform infrared (FT-IR)
3. Near-infrared (NIRS)

Control Histopathology 

Outcomes
Quantitative: Diagnostic accuracy, sample size, scan time, and excitation laser wavelength 
Qualitative: Study design and limitations, tissue preparation, algorithm for analysis, and the pathological groups compared

Setting Intraoperative, bedside, or laboratory 

(65%), followed by IR (20%), and SERS (10%). No studies 
using NIRS were found. Two comparator studies were 
included. One compared FT-IR and RS on the same 
bladder specimens[17], while another compared a novel 
superficial RS fiber optic probe with a non-superficial 
probe[18].

Sample sizes of the studies were low, with a mean of  
44 patients (range, 6–214). Only 2 studies were conducted 
in vivo[18,19], while the remaining studies were ex vivo  
(n = 18). There was considerable variation in tissue prepa-
ration methods, including snap-freezing bladder speci-
mens post-TURBT and subsequently thawing (40%), 

for inclusion was conducted in 2 phases. The first phase 
involved screening titles and abstracts from the initial 
search results. The second phase involved reviewing 
full-text articles based on the previously stated inclusion 
criteria. Both phases of screening were conducted by 
2 independent reviewers (A.Y. and M.A.). In cases of 
unresolved disagreements, a third senior reviewer (D.B.) 
acted as an adjudicator. The same approach was used to 
screen all grey literature sources.

Data collection process
Two reviewers (A.Y. and M.A.) independently conducted 
data extraction onto a predefined extraction sheet. The 
extracted data were cross-checked independently. The 
primary outcome measures extracted for assessing 
the effectiveness of a diagnostic modality included 
quantitative measures of accuracy such as sensitivity, 
specif icity, overall accuracy, and area under the 
curve (AUC) values. Secondary outcome measures 
encompassed bot h qua nt itat ive a nd qua litat ive 
data covering study design and limitations, tissue 
preparation, scan time, excitation laser wavelength, 
data analysis technique, and comparison of pathological 
groups. If multiple data analysis techniques were 
evaluated within a study, the data described are based 
on the most effective technique used. When data 
were presented for both a training set and test/cross-
validation set, the data from the test set are presented, as 
it reflects the performance of the test in clinical practice 
most closely.

Study risk of bias assessment
Two reviewers independently assessed each eligible 
study using the Quality Assessment of Diagnostic 
Accuracy Studies (QUADAS-2) tool[16]. Any areas of 
conflict between the 2 reviewers were resolved through 
arbitration involving a third reviewer (D.B.), if necessary.

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immediate analysis of fresh specimens post-TURBT 
(30%), or placing specimens into formalin that was vari-
ably reversed before spectral scanning (30%).

Regarding data analysis techniques, principal compo-
nent fed linear discriminant analysis (PCA-LDA) was 
the most commonly used technique (45%). Other anal-
ysis methods included support vector machines, partial 
least squares linear discriminant analysis, cluster-aver-
aged spectra, ordinary least squares regression, princi-
pal component analysis, and artificial neural networks. 
The quantitative measures of accuracy varied greatly 
across studies. While not all studies reported sensitiv-
ity and specificity, some chose to report overall accuracy 
and AUC. Additionally, 25% of the studies provided 
only descriptive analysis of tissue constituents such as 
proteins, lipids, DNA, collagen, and cholesterol.

Comparing the performance of spectroscopic 
techniques
Quantitative measures of diagnostic accuracy, such 
as sensitivity and specificity, were reported in 12 
of 14 studies that used RS. The remaining 2 studies 
reported descriptive analysis of tissue constituents 
instead of accuracy[17,20]. Diagnostic endpoints 
varied significantly across studies, depending on 
the histopathological categories chosen for analysis. 
For example, 90% of the studies compared benign to 
malignant tissues, while 30% compared low-grade with 
high-grade urothelial cancer. This variability made 
direct comparisons between studies difficult. Overall, 
the sensitivity and specificity for detecting malignancy 
ranged from 71% to 97% and from 72% to 100%, 
respectively.

In contrast, only 1 of 5 FT-IR studies reported on 
accuracy. Hughes et al. (2013) used support vector 
machines to achieve a class accuracy of 98% to 99% 
when distinguishing conventional urothelial cancer 
from rare subvariants. They did not compare with 
benign tissues[21]. Pezzei et al. (2013) used FT-IR micro-
scopic imaging with tissue microarray technology to 
correlate with stained histological BCa tissue sections, 
opening up new possibilities for spectroscopic analyses 
and exploration of the molecular changes associated 
with histopathological morphology[22]. The remaining 
3 FT-IR studies reported only on concentrations of tissue 
constituents.

Two studies with markedly different study designs 
used SERS. The first study, conducted by Jin et al. (2019), 
compared luminal and basal-like subtypes of BCa and 
reported an overall accuracy of 94%[23]. That study used 
50 snap-frozen specimens without any benign controls. 
In a subsequent study, Zacharovas et al. (2022) applied 
SERS to freshly excised bladder tissue and extracellular 
fluid. Their 3-group algorithm achieved a sensitivity of 

FIGURE 2.

Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) analysis of the included studies 

Study

Risk of bias Applicability concerns

Patient 
selection

Index test
Reference 
standard

Flow and 
timing

Patient 
selection

Index test
Reference 
standard

Crow et al., 2004 Low Low Low Low Low Low Low

Crow et al., 2005 High High Low High Low Low Low

de Jong et al., 2006 High Low Low Unclear High High Low

Stone et al., 2007 High Low Low Unclear Low High Low

Draga et al., 2010 Low High High High Low Low Low

Ahmed et al., 2010 Unclear High Low Unclear High High Low

Barman et al., 2012 Low Low Low Low Low Low Low

AI-Musletet al., 2012 Unclear Low Low Unclear High High Low

Pezzeiet al., 2013 High High Low High High High Low

Hugheset al., 2013 High High Low Unclear High High Low

Chenet al., 2018 Low Low Low Low Low Low Low

Jinet al., 2019 High High Unclear Unclear High High Unclear

Pavlovet al., 2019 Low Low Unclear Low Low Low Unclear

Yousifet al., 2020 High High Low Low Low High Low

Placzeket al., 2020 Low Low High High Low Low Low

Corderoet al., 2020 Low Low High High Low Low Low

Morselliet al., 2021 Low Low Low High Low High Low

Zacharovas et al. (2022)[10] Low High Low High Low High Low

Stomp-Agenantet al., 2022 High High Low High High Low Low

Taieb et al., 2022 High High Low High High High Low

TABLE 3.

Characteristics of studies included in the systematic 
review

Characteristic Studies n (%)

Period of 
publication

2004–2009 5 (25)

2010–2016 7 (35)

2017–2022 10 (50)

Spectroscopy 
modality

Raman 14 (65)

FT-IR 5 (20)

SERS 2 (10)

Raman & FT-IR 1 (5)

NIRS 0 (0)

Sample size

< 20 6 (30)

20–50 10 (50)

50 4 (20)

Tissue 
preparation

In vivo 2 (10)

Ex vivo: frozen 8 (40)

Ex vivo: fresh 6 (30)

Ex vivo: formalin 6 (30)

Data analysis 
technique

PCA-LDA 9 (45)

PCA-SVM 2 (10)

PLS-LDA 2 (10)

PCA 2 (10)

OLS 1 (5)

PCA-ANN 1 (5)

Constituents only 5 (25)

Histopathological 
categories 
compared

Benign vs. cancer 18 (90)

Grade characterization 6 (30)

Stage characterization 3 (15)

Subtype characterization 2 (10)

ANN: artificial neural networks; CAS: cluster-averaged spectra; FT-IR: 
Fourier transform infrared; LDA: linear discriminant analysis; NIRS: 
near-infrared spectroscopy; OLS: ordinary least squares regression; PCA: 
principal component analysis; PLS: partial least squares; SERS: surface-
enhanced Raman spectroscopy; SVM: support vector machines.

Flow and timing

Reference standard

Index test

Patient selection

Q
U

A
D

A
S-

2 
D

om
ai

n

0% 20% 40% 60% 80% 100%
Proportion of studies with low, high or unclean

RISK of BIAS

0% 20% 40% 60% 80% 100%
Proportion of studies with low, high or unclean

CONCERNS regarding APPLICABILITY 

Low High Unclear

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REVIEW Point-of-Care Diagnosis of Bladder Cancer With Vibrational Spectroscopy: A Systematic Review

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TABLE 4.

Summary of all data collected for the included studies 

Study/ Year Country
Patients/ 

Specimens
Malignant/ 

Control 
Tissue preparation 

Spectroscopy modality/
Mean scan time 

Laser 
wavelength

Data analysis 
technique

Histopathological categories 
compared 

Diagnostic endpoint

Sensitivity / Specificity Accuracy / Other

Crow et al.
(2004)[24] 

United Kingdom 72/75 3/22 Ex vivo—Snap-frozen post-TURBT Raman / 10 sec 830 nm PCA-LDA
• (i) normal, (ii) cystitis, (iii) malignant
• (i) low grade, (ii) high grade
• (i) pTa, (ii) pT1, (iii) pT2 

91% / 98%
93% / 98%
96% / 96%

n/a

Crow et al.
(2005)[40] 

United Kingdom 24/29 10/19 Ex vivo—Snap-frozen post-TURBT Raman, fibreoptic probe / 5–30 sec 785 nm PCA-LDA • (i) benign, (ii) malignant 79% / 89% Accuracy 84%

de Jong et al. 
(2006)[41] 

Netherlands 15/15 9/6 Ex vivo –Snap-frozen post-TURBT Raman map / 20 sec 845 nm
PCA-LDA

CAS
• (i) benign, (ii) malignant 94% / 92% Accuracy 98%

Stone et al.  
(2007)[20] 

United Kingdom 24/73 41/32 Ex vivo—Snap-frozen post-TURBT Raman / 20 sec 830 nm OLS • (i) benign, (ii) malignant n/a 
Constituents: actin, 

collagen, choline, triolein, 
DNA, cholesterol

Draga et al. 
(2010)[19]

The Netherlands 38/63 23/29 In vivo—Live tissue prior to TURBT Raman and PDD / 1–5 sec 785 nm PCA-LDA
• (i) normal, (ii) cystitis, (iii) malignant
• (i) normal, (ii) cancer
• (i) normal, (ii) pTa, (iii) pT1 + pT2 

71% / 87%
85% / 79%
58% / 76%

n/a

Ahmed et al. 
(2010)[17] 

Sudan 7/14 4/0
Ex vivo—formalin, dried, grinded,  

KBr additive
Raman and FT-IR / Scan time n/a

1064 nm
Constituents only • (i) benign, (ii) malignant n/a

Constituents: proteins, 
lipids, nucleic acids

Barman et al. 
(2012)[25] 

United Kingdom 14/28 14/14 Ex vivo—fresh post-TURBT Raman,confocal probe / 5 sec 785 nm PCA-LDA • (i) benign, (ii) malignant 86% / 100%
Accuracy 93%

AUC 0.91
Al-Muslet et al. 
(2012)[42] 

Sudan 11/22 11/11
Ex vivo—formalin, dried, grinded,  

KBr additive
FT-IR / Scan time n/a n/a Constituents only • (i) benign, (ii) malignant n/a

Constituents: proteins, 
lipids, nucleic acids

Pezzei et al. 
(2013)[22] 

Austria 214 214/0 Ex vivo—formalin, dried
FT-IR micro-spectroscopy /  

Scan time n/a
n/a PCA • (i) benign, (ii) malignant n/a n/a

Hughes et al. 
(2013)[21] 

United Kingdom 6/6 6/0 Ex vivo—reversal of FFPE tissue 
FT-IR micro-spectroscopy / 

Scan time n/a
n/a PCA-SVM

• Rare subvariants only: 
(i) conventional urothelial cancer,  
(ii) micro-papillary, (iii) stroma,  
(iv) lymphocyte-rich, (v) clear cell,  
(vi) lipoid

n/a Accuracy 98%

Chen et al. 
(2018)[26] 

China 10/32 21/11 Ex vivo—snap-frozen in liquid nitrogen Raman, fiber optic probe / 1 sec 785 nm PCA-ANN • (i) normal, (ii) low grade, (iii) high grade
90% / 98% LG
98% / 96% HG

Accuracy 93%

Jin et al. 
(2019)[23] 

China 50 50/0 Ex vivo—snap-frozen SERS / 10 sec 633 nm PCA-LDA
Two subtypes:
• (i) luminal, (ii) basal-like

n/a
Accuracy 94%

AUC 97
Pavlov et al.  
(2016)[43] 

Russia 22 22/13 Ex vivo—fresh post-TURBT Raman / Scan time n/a 785 nm PCA-LDA • (i) benign, (ii) malignant 97% / 96% n/a

Yousif et al. 
(2020)[44] 

Iraq 46/46 23/23 Ex vivo—formalin ATR-FT-IR / Scan time n/a n/a Constituents only • (i) benign, (ii) malignant n/a
Constituents: proteins, 

lipids, collagen
Placzek et al. 
(2020)[27] 

Denmark 44/119 53/66 Ex vivo—fresh or snap-frozen Raman and OCT / Scan time n/a 785 nm PLS-LDA
• (i) benign, (ii) malignant
• (i) low grade, (ii) high grade

95% / 88%
81% / 61%

n/a

Cordero et al. 
(2020)[35] 

Denmark 28/67 37/11 Ex vivo—fresh or snap-frozen Raman, fiber optic mapping / 3 sec 785 nm PLS-LDA
• (i) benign, (ii) malignant
• (i) low grade, (ii) high grade

92% / 93%
85% / 83%

Accuracy 92%
Accuracy 84%

Morselli et al. 
(2021)[28] 

Italy 114/169 40/129 Ex vivo—fresh post-TURBT
Raman, fiber optic & fluorescence 

& reflectance / Scan time n/a 
785 nm PCA-LDA

• (i) benign, (ii) malignant
• (i) low grade, (ii) high grade
• (i) pTa, (ii) pT1
• (i) pTa, (ii) pT2
• (i) pT1, (ii) pT2

77% / 72%
73% / 65%
65% / 71%
81% / 81%
75% / 76%

Accuracy 77% 

Zacharovas et al. 
(2022)[10] 

Lithuania 30/58 25/28 Ex vivo—fresh post-TURBT SERS /5 min 1064 nm PCA • (i) normal, (ii) cystitis, (iii) malignant 85% / 97% n/a

Stomp-Agenant  
et al. (2022)[18] 

The Netherlands 75/117 51/66 In vivo—Live tissue prior to TURBT
Raman fiber optic, superficial vs 

normal probe / 0.5 sec
785 nm PCA-LDA

• (i) benign, (ii) malignant
• (i) normal, (ii) low grade, (iii) high grade

90% / 87% superficial probe
80% / 85% normal probe

AUC 0.95 superficial probe
AUC 0.80 normal probe

Taieb et al. 
(2022)[45] 

Israel Unknown Unknown Ex vivo—reversal of FFPE tissue Raman mapping / 60 sec 561 nm PCA-SVM • (i) benign, (ii) malignant 84% / 88% n/a

ANN: artificial neural networks; ATR: attenuated total reflection; AUC: area under the curve; CAS: cluster-averaged spectra; FFPE: formalin-fixed  
paraffin-embedded; FT-IR: Fourier transform infrared; HG: high grade; LDA: linear discriminant analysis; LG: low grade; n/a: not available;  
NIRS: near-infrared spectroscopy; 

OLS: ordinary least squares regression; PCA: principal component analysis; PDD: photodynamic diagnosis; PLS: partial least squares;  
SERS: surface-enhanced Raman spectroscopy; SVM: support vector machines; TURBT: transurethral resection of bladder tumour.

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multimodal approach improved accuracy up to 90%[28], 
the additional time and resource expenditure is signifi-
cant, limiting this approach in a clinical setting.

Comparison of VS techniques
In the context of BCa diagnosis, RS has received the 
most attention in recent years. A systematic review 
of 9 original studies conducted between 2004 and 
2015 demonstrated an impressive pooled diagnostic 
sensitivity of 94% and specificity of 92%[29]. However, 
the studies included in this meta-analysis were highly 
heterogeneous in terms of sample type, instrument used, 
excitation wavelength, and algorithm for analysis. Some 
studies used snap-frozen or formalin-fixed paraffin-
embedded (FFPE) tissue, while others used cell lines, 
peripheral blood, or urine.

There has been great interest in using RS to analyze 
urine samples for molecular signatures associated with 
BCa to develop a truly non-invasive screening test. 
Huttanus et al. (2020) developed an RS-based chemom-
etric urinalysis (Rametrix) as a direct method for screen-
ing urine samples. Using a model built with 22 principal 
components, BCa was detected with 82.4% sensitivity 
and 79.5% specificity[30]. The reduced accuracy of this 
label-free method could be attributed to the diversity of 
urine composition, concentration, and pH[31]. With the 
surface enhancement provided by SERS, greater diag-
nostic accuracy could be achieved, as demonstrated a 
recent study by Hu et al. (2021) with 100% sensitivity 
and 98.85% specificity[32].

FT-IR analysis of bladder washings has also been 
proposed as a sensitive, rapid, non-destructive, and 
operator-independent analytical diagnostic method 
for BCa compared with traditional urine cytology. 
In a study by Gok et al. (2016), bladder washings were 
analyzed from 136 patients, demonstrating a sensitivity 
of 100% but modest specificity of 73.5%. Interestingly, 
traditional urine cytology had a sensitivity of only 45% 
on the same specimens in this study[33].

The combination of FT-IR with microscopy has also 
led to the development of IR imaging. Studies have 
demonstrated accuracies > 90% compared with immu-
nohistochemical (IHC) diagnostics by pathologists[34]. 
However, clinical translation of this powerful integrated 
technique has been hindered by long measuring times 
and complex FT-IR setup requiring liquid nitrogen cool-
ing. Quantum-cascade laser (QCL)-based microscopes 
have shown promise in overcoming these limitations, 
enabling IR imaging to be performed within minutes. 
Kuepper et al. (2018) demonstrated that QCL-based IR 
imaging could identify colorectal cancer in the same 
time frame as a frozen thin section diagnosis by pathol-
ogists, boasting a sensitivity of 96% and specificity  
of 100%[34].

Limitations of evidence and review process
Despite the high levels of diagnostic accuracy achieved 
with VS recently, this review has identified several 
limitations that indicate the need for further work before 
the clinical use of VS as a minimally invasive tool for 
cancer investigation. The inclusion criteria of this review 
did not impose limitations on sample size to provide 
a broader overview of all available literature on VS.  
As a result, one-third of studies included had fewer than 
20 participants, offering a low level of evidence with 
significant heterogeneity in study design. Furthermore, 
variation in tissue sample preparation techniques, 
pathological grouping, and data analysis make direct 
comparison between studies difficult. This prevents any 
meaningful pooling of results through meta-analysis 
to obtain statistical estimates of overall diagnostic 
accuracies.

The reporting methods of the included studies were 
inconsistent and often incomplete. While many stud-
ies often reported sensitivity and specificity, they often 
omitted reporting accuracy and AUC, or vice versa. 
Only 1 study reported all 4 of measures of accuracy[25]. 
The use of the term “optimal” sensitivity in some stud-
ies raises concerns about reporting bias, as the authors 
may have been selecting the best results for reporting. 
Concerningly, none of the studies provided complete 
data for all key areas of diagnostic accuracy: true and 
false positivity and negativity, sensitivity, specificity, and 
positive and negative predictive values. It is paramount 
to publish larger studies that comprehensively report 
these values to further evaluate spectroscopy.

Implications on clinical practice and 
challenges for future research
While many of the studies included in this review 
analyzed ex vivo bladder specimens, the true potential 
of non-invasive VS lies in its application in a real-time 
in vivo setting. This will undoubtedly depend upon the 
development and optimization of fiber optic probes 
that can be introduced via the urologist’s everyday 
cystoscope. This trend is already evident in the studies 
included in this review, with 4 of the 5 studies published 
in the past 5 years using a fiber optic probe[18,26,28,35]. 
Another major challenge is the presence of f luids 
such as urine or glycine, which can interfere with the 
spectroscopic signal and reduce specificity. More in 
vivo research is needed to evaluate the feasibility of VS 
in the operating theatre. The results of a phase 1 trial by 
Hermann et al. are awaited (ClinicalTrials.gov identifier: 
NCT05124106), as the study utilizes fiber optic probes to 
take RS measurements inside the bladder of 30 patients.

It is noteworthy that no studies using NIRS to analyze 
BCa were included in this review, despite the success-
ful use of this technique in evaluating prostate cancer, 
breast cancer, and cardiac fibrosis specimens[36–38]. 

85% and specificity of 97% in distinguishing malignancy 
from cystitis and normal tissue[10].

Quality assessment and risk of bias
All articles were evaluated for risk of bias and concerns 
regarding applicability using the QUADAS-2 quality 
assessment tool (Figure 2). Up to 45% and 50% of 
the studies showed a high risk of bias regarding 
patient selection and index test, respectively. This was 
predominantly due to non-random patient selection 
and knowledge of reference standard results prior to 
interpreting the index test.

Discussion
Evolution of spectroscopy in clinical practice
Since the first ex vivo RS study by Crow et al. (2004) 
using frozen tissue (Figure 3), significant technological 
advancements in optoelectronics, computationa l 
capacity, and machine-learning data analysis techniques 
have facilitated rapid and real-time applications[24].  
In the first in vivo study, Draga et al. (2010) introduced 
a fiber optic probe with a 2.1-mm external diameter 
via a cystoscope to acquire RS measurements imme-
diately before TURBT[19]. Their algorithm achieved 
a sensitivity of 85% and a modest specificity of 79% 
in distinguishing BCa from normal tissue, thus 
highlighting the challenges in clinical translation for RS.

Subsequent RS studies have implemented hard-
ware and software improvements to optimize diag-
nostic accuracy. Barman et al. (2012) introduced a 
confocal fiber optic probe that limited sampling to 300 μm.  

FIGURE 3.

Figures from the first ex vivo Raman Spectroscopy study by Crow et al. (2004) using frozen tissue. A) The mean Raman 
spectra measured for each of the pathological groups. B) Scatter plots of the scores of linear discriminant function 1 
vs. 2 showing clustering in the eight-group algorithm. C) The prediction power of the eight-group diagnostic algorithm 
demonstrating a sensitivity and specificity of 93% and 98%, respectively. (Reproduced with permission.[24])

Adeno: adenocarcinoma; a.u.: absorption units; CIS: carcinoma in situ; Cyst: cystitis; ldf; linear discriminant function; Sq: squamous;  
TCC: transitional cell carcinoma.

By suppressing spectral information from deeper tissue 
layers beyond the region of interest, diagnostic accuracy 
improved to 86% sensitivity and 100% specificity[25]. 
Following a similar principle of shallow tissue sampling, 
Stomp-Agenant et al. (2022) developed a superficial fiber 
optic probe with a measuring depth of 200 μm. This 
significantly reduced the signal-to-noise ratio compared 
to a regular probe, improving accuracy to 90% sensitiv-
ity and 87% specificity[18].

In add it ion to t he ha rdwa re i mprovements 
described, advancements in computational capacity and 
machine-learning data analysis techniques continue to 
enhance the diagnostic accuracy of spectroscopy. Chen 
et al. (2018), analyzed 32 snap-frozen bladder specimens 
using a fiber optic probe, similar to previous studies, but 
combined PCA with artificial neural network (ANN) 
modelling to achieve a sensitivity of 98% and speci-
ficity of 96% in detecting high-grade BCa. ANN is a 
powerful, self-adaptive, and data-driven pattern recog-
nition method capable of capturing non-linear char-
acteristics of the data[26]. After comparing ANN with 
other popular classifications methods such as linear 
discriminant analysis (LDA) or support vector machines 
(SVM) in dozens of studies, the authors noted that ANN 
constantly outperformed other techniques.

While no single spectroscopy technique has proven 
to be perfect, a multimodal approach is likely to be 
required. RS has been combined with a concurrent diag-
nostic method in 3 studies, including photodynamic 
diagnosis[19], optical coherence tomography[27], and 
f luorescence and diffuse ref lectance[28]. Although a 

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References

1. Richters A, Aben KKH, Kiemeney LALM. The global burden of urinary 
bladder cancer: an update. World J Urol.2020;38(8):1895 –1904. 
doi: 10.1007/s00345-019-0298 4 - 4. PMID: 31676912; PMCID: 
PMC7363726.

2. Sylvester RJ, van der Meijden APM, Oosterlinck W, et al. Predicting 
recurrence and progression in individual patients with stage Ta T1 
bladder cancer using EORTC risk tables: a combined analysis of 2596 
patients from seven EORTC trials. Eur Urol.2006;49(3):466– 477; 
discussion 475 – 477. doi: 10.1016/j.eururo.2005.12.031. PMID: 
16442208.

3. Jocham D, Witjes F, Wagner S, Zeylemaker B, van Moorselaar J, Grimm 
MO, et al. Improved detection and treatment of bladder cancer using 
hexaminolevulinate imaging: a prospective, phase III multicenter 
study. J Urol.2005;174(3):862–866; discussion 866. doi: 10.1097/01.
ju.0000169257.19841.2a. PMID: 16093971.

4. Schmidbauer J, Witjes F, Schmeller N, Donat R, Susani M, Marberger 
M; Hexvix PCB301/01 Study Group. Improved detection of urothelial 
carcinoma in situ with hexaminolevulinate fluorescence cystoscopy. 
J Urol.2004;171(1):135–138. doi: 10.1097/01.ju.0000100480.70769.0e. 
PMID: 14665861.

5. Cauberg ECC, de Bruin DM, Faber DJ, van Leeuwen TG, de la Rosette 
JJ, de Reijke TM. A new generation of optical diagnostics for bladder 
cancer: technology, diagnostic accuracy, and future applications. Eur 
Urol.2009;56(2):287–297. doi: 10.1016/j.eururo.2009.02.033. PMID: 
19285787.

6. AA, Aboul-Enein HY. Vibrational spectroscopy applications in drugs 
analysis. In: Lindon JC, Tranter GE, Koppenaal DW, eds. Encyclopedia 
of Spectroscopy and Spectrometry. 3rd ed. Academic Press; 
2017:575–581.

7. European Pharmaceutical Review. Pharmaceutical analysis with 
portable spectrometers. Published February 22, 2022. Available at: 
https://www.europeanpharmaceuticalreview.com/article/168732/
pharmaceutical-analysis-with-portable-spectrometers/. Accessed 
June 21, 2023.

8. Ly ng F M, Ramos IRM, Ibr ahim O, B y r ne H J. V ibr ational 
microspectroscopy for cancer screening. Appl Sci.2015;5(1):23–35. 
doi: 10.3390/app5010023.

9. Moisoiu V, Iancu SD, Stefancu A, Moisoiu T, Pardini B, Dragomir MP, 
et al. SERS liquid biopsy: an emerging tool for medical diagnosis. 
Colloids Surf B Biointerfaces.2021;208:112064. doi: 10.1016/j.
colsurfb.2021.112064. PMID: 34517219.

10. Zacharovas E, Velička M, Platkevičius G, Čekauskas A, Želvys AN, 
Niaura G,et al. Toward a SERS diagnostic tool for discrimination 
between cancerous and normal bladder tissues via analysis of the 
extracellular fluid. ACS Omega.2022;7(12):10539–10549. doi: 10.1021/
acsomega.2c00058. PMID: 35382275; PMCID: PMC8973049.

11. Afara IO, Shaikh R, Nippolainen E, Querido W, Torniainen J, Sarin 
JK, et al. Characterization of connective tissues using near-infrared 
spectroscopy and imaging. Nat Protoc.2021;16(2):1297–1329. doi: 
10.1038/s41596-020-00468-z. PMID: 33462441.

12. Beć KB, Huck CW. Break through potential in near-infrared 
spectroscopy: spectra simulation. A review of recent developments. 
Front Chem.2019;7:48. doi: 10.3389/fchem.2019.00048. PMID: 
30854368; PMCID: PMC6396078.

13. Anderson DJ, Anderson RG, Moug SJ, Baker MJ. Liquid biopsy for 
cancer diagnosis using vibrational spectroscopy: systematic review. 
BJS Open.2020;4(4):554 –562. doi: 10.1002/bjs5.50289. PMID: 
32424976; PMCID: PMC7397350.

14. 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(3):207–219. 
doi:10.1016/j.pdpdt.2013.01.008. PMID: 23993846.

15. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow 
CD, et al. The PRISMA 2020 statement: an updated guideline for 
reporting systematic reviews. BMJ.2021;372:n71. doi:10.1136/bmj.
n71. PMID: 33782057; PMCID: PMC8005924.

16. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, 
Reitsma JB, et al.; QUADAS-2 Group. QUADAS-2: a revised tool 
for the quality assessment of diagnostic accuracy studies. Ann 
Intern Med.2011;155(8):529–536. doi: 10.7326/0003-4819-155-8-
201110180-00009. PMID: 22007046.

17. Ahmed EG, Al-Muslet NA, Ahmed MM, Moharam M, Musaad W. The 
use of fourier infrared spectroscopy and laser-Raman spectroscopy 
in bladder malignancy diagnosis, a comparative study. Appl Phys 
Res.2010;2(1):108–117.

18. Stomp-Agenant M, van Dijk T, R. Onur A, Grimbergen M, van 
Melick H, Jonges T, et al. In vivo Raman spectroscopy for bladder 
cancer detection using a superficial Raman probe compared to a 
nonsuperficial Raman probe. J Biophotonics.2022;15(6):e202100354. 
doi: 10.1002/jbio.202100354. PMID: 35233990.

19. Draga ROP, Grimbergen MCM, Vijverberg PLM, van Swol CF, Jonges 
TG, Kummer JA, et al. In vivo bladder cancer diagnosis by high-
volume Raman spectroscopy. Anal Chem.2010;82(14):5993–5999. 
doi: 10.1021/ac100448p. PMID: 20524627.

20. 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(5):1657–1668. doi: 10.1007/s00216-006-0937-9. 
PMID: 17123068.

21. 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(1):73–87. doi: 
10.1002/jbio.201200126. PMID: 23125109.

22. Pezzei C, Brunner A, Bonn GK, Huck CW. Fourier transform infrared 
imaging analysis in discrimination studies of bladder cancer. 
Analyst.2013;138(19):5719–5725. doi: 10.1039/c3an01101a. PMID: 
23897512.

Recent improvements in machine-learning analytical 
techniques have led to substantial progress in research 
and industry[11,12]. NIRS has the potential to provide 
real-time molecular data, analogous to handheld ultra-
sound devices, with low computational requirements 
(6 Kb per spectrum), making it possible to be performed 
on mobile devices in line with the evolution toward 
ambulatory and personalized care[38]. Compared 
to the aforementioned techniques, NIRS spectra can 
be obtained from greater sample thickness, allowing 
for easier sample handling, and it is fast without the 
need for a laser, unlike RS. Additionally, near-infra-
red light penetrates deeper into human tissues, caus-
ing less photodamage and safer tissue probing[39].  
Considering that NIRS and RS provide complementary 
information when analyzing the same sample, combin-
ing the 2 techniques in a multimodal approach could 
potentially further enhance diagnostic accuracy.

With ongoing advancements in spectroscopy tech-
nology, machine-learning analytical techniques, and  
multimodal approaches to improve accuracy, VS offers 
several potential advantages over standard histopathol-
ogy: rapid, label-free, and operator independent. When 
used in conjunction with fiber optic probes in endoscopy, 
it may help reduce cases of incomplete tumour resection 
and lower the risk for recurrence. It can serve as a tool 
to aid clinical decision-making in real time, providing 
a quick and safe assessment of stage and grade, allowing 

urologists to reduce over- or under-treatment of BCa. 
In an increasingly frail population with rising antico-
agulant use, these improvements could reduce adverse 
events related to surgery and expedite the staging and 
grading of urothelial cancer of the bladder.

Conclusions
Although VS is a mature technology in analytical 
chemistry, its use in medical diagnostics is still in its 
infancy. Recent advances in technology and computing 
power and reductions in equipment costs and size have 
facilitated a shift in focus from the laboratory to the 
bedside. As fiber optic probes for spectroscopy become 
commercially available, their use in combination 
with a conventional cystoscope opens up the exciting 
possibility for real-time diagnostic imaging of BCa. RS 
has demonstrated high levels of diagnostic accuracy, 
which continue to improve with advancements in SERS. 
However, studies are small and highly heterogeneous. 
Larger spectroscopy studies with robust reporting 
methods and a multimodal approach are needed to 
assess not only the overall diagnostic accuracies but 
also the optimal utilization of this emerging technology. 
Ongoing research into modalities such as SERS and 
NIRS holds great promise, making spectroscopy an 
exciting and dynamic field in urological diagnostics 
with the potential to enhance intraoperative decision-
making.

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23. Jin D, Wang X, Fu B, Li T, Chen N, Chen Z, et al. Raman spectroscopy 
of luminal subtype and basal subtype muscle invasive bladder cancer. 
Int J Clin Exp Med.2019;12(5):5447–5453.

24. 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.200 4;93(9):1232–1236. doi: 10.1111/j.14 6 4 -
410X.2004.04852.x. PMID: 15180613.

25. Barman I, Dingari NC, Singh GP, Kumar R, Lang S, Nabi G. Selective 
sampling using confocal Raman spectroscopy provides enhanced 
specificit y for urinar y bladder cancer diagnosis. Anal Bioanal 
Chem.2012;404(10):3091–3099. doi: 10.1007/s00216-012-6424-6. 
PMID: 23052868.

26. Chen H, Li X, Broderick N, Liu Y, Zhou Y, Han J, et al. Identification and 
characterization of bladder cancer by low-resolution fiber-optic Raman 
spectroscopy. J Biophotonics.2018;11(9):e201800016. doi: 10.1002/
jbio.201800016. PMID: 29797794.

27. Placzek F, Cordero Bautista E, Kretschmer S, Wurster LM, Knorr 
F, González-Cerdas G, et al. Morpho-molecular ex vivo detection 
and grading of non-muscle-invasive bladder cancer using forward 
imaging probe based multimodal optical coherence tomography and 
Raman spectroscopy. Analyst.2020;145(4):1445–1456. doi: 10.1039/
c9an01911a. PMID: 31867582.

28. Morselli S, Baria E, Cicchi R, Liaci A, Sebastianelli A, Nesi G, et al. The 
feasibility of multimodal fiber optic spectroscopy analysis in bladder 
cancer detection, grading, and staging. Urologia.2021;88(4):306–314. 
doi: 10.1177/03915603211007018. PMID: 33789562.

29. Jin H, Lin T, Han P, Yao Y, Zheng D, Hao J, et al. Efficacy of Raman 
spectroscopy in the diagnosis of bladder cancer: a systematic 
review and meta-analysis. Medicine (Baltimore).2019;98(47):e18066. 
doi: 10.1097/MD.0000000000018066. PMID: 31764837; PMCID: 
PMC6882629.

30. Huttanus HM, Vu T, Guruli G, Tracey A, Carswell W, Said N, et al. Raman 
chemometric urinalysis (Rametrix) as a screen for bladder cancer. PLoS 
One.2020;15(8):e0237070. doi: 10.1371/journal.pone.0237070. PMID: 
32822394; PMCID: PMC7446794.

31. Liu Z, Zhang P, Wang H, Zheng B, Sun L, Zhang D, et al. Raman spectrum-
based diagnosis strategy for bladder tumor. Urol Int.2022;106(2):109–
115. doi: 10.1159/000518877. PMID: 34515249.

32. Hu D, Xu X, Zhao Z, Li C, Tian Y, Liu Q, et al. Detecting urine metabolites 
of bladder cancer by sur face-enhanced Raman spectroscopy. 
Spectrochim Acta A Mol Biomol Spectrosc.2021;247:119108. doi: 
10.1016/j.saa.2020.119108. PMID: 33161263.

33. Gok S, Aydin OZ, Sural YS, Zorlu F, Bayol U, Severcan F. Bladder 
cancer diagnosis from bladder wash by Fourier transform 
infrared spectroscopy as a novel test for tumor recurrence. J 
Biophotonics.2016;9(9):967–975. doi: 10.1002/jbio.201500322. PMID: 
27041149.

34. Kuepper C, Kallenbach-Thieltges A, Juet te H, Tannapfel A, 
Großerueschkamp F, Gerwert K. Quantum cascade laser-based 
infrared microscopy for label-free and automated cancer classification 
in tissue sections. Sci Rep.2018;8(1):7717. doi: 10.1038/s41598-018-
26098-w. PMID: 29769696; PMCID: PMC5955970.

35. Cordero E, Rüger J, Marti D, Mondol AS, Hasselager T, Mogensen 
K, et al. Bladder tissue characterization using probe-based Raman 
spectroscopy: evaluation of tissue heterogeneity and influence on 
the model prediction. J Biophotonics.2020;13(2):e201960025. doi: 
10.1002/jbio.201960025. PMID: 31617683; PMCID: PMC7065650.

36. Ali JH, Wang WB, Zevallos M, Alfano RR. Near infrared spectroscopy 
and imaging to probe differences in water content in normal and cancer 
human prostate tissues. Technol Cancer Res Treat.2004;3(5):491–497. 
doi: 10.1177/153303460400300510. PMID: 15453814.

37. Gu Y, Chen WR, Xia M, Jeong SW, Liu H. Effect of photothermal therapy 
on breast tumor vascular contents: noninvasive monitoring by near-
infrared spectroscopy. Photochem Photobiol.2005;81(4):1002–1009. 
doi: 10.1562/2004-09-05-RA-305. PMID: 15807632.

38. Adegoke JA, Gassner C, Sharma VJ, Patel SK, Jackett L, Afara IO, et 
al. Near-infrared spectroscopic characterization of cardiac and renal 
fibrosis in fixed and fresh rat tissue. Anal Sens.2023;3(1):e202200030. 
doi: 10.1002/anse.202200030.

39. Wu S, But t H-J. Near-infrared-sensitive materials based on 
upconverting nanoparticles. Adv Mater.2016;28(6):1208–1226. doi: 
10.1002/adma.201502843. PMID: 26389516.

40. Crow P, Molckovsky A, Stone N, Uff J, Wilson B, WongKeeSong 
L M .  A s s e s s m e n t  o f  f i b e r o p t i c  n e a r- i n f r a r e d  r a m a n 
spectroscopy for diagnosis of bladder and prostate cancer. 
Urology.2005;65(6):1126–1130. doi: 10.1016/j.urology.2004.12.058. 
PMID: 15913721.

41. de Jong BWD, Bakker 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(22):7761–7769. 
doi: 10.1021/ac061417b. PMID: 17105169.

42. Al-Muslet NA, Ali EE. Spectroscopic analysis of bladder cancer 
tissues using Fourier transform infrared spectroscopy. J Appl 
Spectrosc.2012;79(1):139–142.

43. Pavlov V, Bilyalov A, Gilmanova R, Yakupov RR, et al. The use of 
intelligent data processing techniques of Raman spectroscopy for 
the diagnosis of malignant tumors. Bashkortostan Med J.2016;13.

44. Yousif ES, Abdulkareem DT, Enad alboaisa Ns, Mohammad EJ. 
Detection of urinary bladder cancer by (ATR-F TIR) spectroscopy. 
System Rev Pharm.2020;11(12):1932–1937.

45. Taieb A, Berkovic G, Haifler M, Cheshnovsk y O, Shaked NT. 
Classification of tissue biopsies by Raman spectroscopy guided by 
quantitative phase imaging and its application to bladder cancer. J 
Biophotonics.2022;15(8):e202200009. doi: 10.1002/jbio.202200009. 
PMID: 354887

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