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Journal of
Clinical Medicine
Article
Improved Non-Invasive Diagnosis of Bladder Cancer with an
Electronic Nose: A Large Pilot Study
PierFrancesco Bassi 1, Luca Di Gianfrancesco 1, * , Luigi Salmaso 2, Mauro Ragonese 1, Giuseppe Palermo 1,
Emilio Sacco 1, Rosa Arboretti Giancristofaro 2, Riccardo Ceccato 2and Marco Racioppi 1
Citation: Bassi, P.; Di Gianfrancesco,
L.; Salmaso, L.; Ragonese, M.;
Palermo, G.; Sacco, E.; Giancristofaro,
R.A.; Ceccato, R.; Racioppi, M.
Improved Non-Invasive Diagnosis of
Bladder Cancer with an Electronic
Nose: A Large Pilot Study. J. Clin.
Med. 2021,10, 4984. https://doi.org/
10.3390/jcm10214984
Academic Editor: Giacomo Novara
Received: 27 August 2021
Accepted: 23 October 2021
Published: 27 October 2021
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Attribution (CC BY) license (https://
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4.0/).
1Department of Urology, Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS di Roma,
UniversitàCattolica del Sacro Cuore di Roma, Largo Agostino Gemelli, 8, 00168 Rome, Italy;
pierfrancesco.bassi@policlinicogemelli.it (P.B.); mauro.ragonese@guest.policlinicogemelli.it (M.R.);
giuseppe.palermo@policlinicogemelli.it (G.P.); emilio.sacco@policlinicogemelli.it (E.S.);
marco.racioppi@policlinicogemelli.it (M.R.)
2Department of Management and Engineering, Universitàdi Padova, 35122 Padova, Italy;
luigisalmaso.unipd@gmail.com (L.S.); rosa.arboretti@unipd.it (R.A.G.); riccardo.ceccato91@gmail.com (R.C.)
*Correspondence: luca.digian@libero.it
Abstract:
Background: Bladder cancer (BCa) emits specific volatile organic compounds (VOCs) in
the urine headspace that can be detected by an electronic nose. The diagnostic performance of an
electronic nose in detecting BCa was investigated in a pilot study. Methods: A prospective, single-
center, controlled, non-randomized, phase 2 study was carried out on 198 consecutive subjects (102
with proven BCa, 96 controls). Urine samples were evaluated with an electronic nose provided with
32 volatile gas analyzer sensors. The tests were repeated at least two times per sample. Accuracy,
sensitivity, specificity, and variability were evaluated using mainly the non-parametric combination
method, permutation tests, and discriminant analysis classification. Results: Statistically significant
differences between BCa patients and controls were reported by 28 (87.5%) of the 32 sensors. The
overall discriminatory power, sensitivity, and specificity were 78.8%, 74.1%, and 76%, respectively;
13/96 (13.5%) controls and 29/102 (28.4%) BCa patients were misclassified as false positive and false
negative, respectively. Where the most efficient sensors were selected, the sensitivity and specificity
increased up to 91.1% (72.5–100) and 89.1% (81–95.8), respectively. None of the tumor characteristics
represented independent predictors of device responsiveness. Conclusions: The electronic nose might
represent a potentially reliable, quick, accurate, and cost-effective tool for non-invasive BCa diagnosis.
Keywords: electronic nose; non-invasive diagnosis; volatile organic compounds; bladder cancer
1. Introduction
To date, cystoscopy remains the gold standard in bladder cancer (BCa) diagnosis, but
it is invasive and uncomfortable. Urinary cytology and radiology play an important role
in follow-up but have some limitations in the diagnostic setting and still remain exam
operator sensitive [
1
–
4
]. Hence, many efforts are focusing on the development of accurate
non-invasive diagnostic tests for BCa.
Volatile organic compounds (VOCs) represent natural markers of pathophysiological
mechanisms in the human body [
5
]. They are normally generated by biochemical processes,
including oxidative stress and lipid metabolism, or can be absorbed from the external
world through ingestion, inhalation, or skin contact [
6
]. An electronic nose is an electronic
sensing device intended to detect odors or flavors. The term “electronic sensing” refers
to the ability to reproduce human senses using sensor arrays and pattern recognition
systems. Once the VOCs are bounded to the detector, it is possible to obtain and analyze
the associated electronic signal with specific software tools.
BCa is associated with the presence of specific VOCs in gas emitted from urine
samples, due to the fact that during tumor growth, protein changes in malignant cells lead
J. Clin. Med. 2021,10, 4984. https://doi.org/10.3390/jcm10214984 https://www.mdpi.com/journal/jcm
J. Clin. Med. 2021,10, 4984 2 of 14
to peroxidation of cell membrane components, producing VOCs [
7
,
8
]. Therefore, VOCs in
the urine headspace (the gas or empty space above the contents of a sealed container) from
BCa patients might play a role as a diagnostic tool. These compounds have been revealed
by pilot studies on the use of the olfactory capacity of dogs [
9
,
10
] and the use of mass
spectrometry [
11
]. They were detected even with an electronic nose with sensitivity and
specificity rates of up to 70% for BCa [12], thereby opening new perspectives in this field.
In this pilot study, we evaluated the potentially reliable and feasible diagnostic per-
formances of a VOC’s sensor device in BCa diagnosis, validated by updated statistical
methods, intended as validated and current methods of managing data and results. The
hypothesis was to evaluate the ability of the electronic nose to detect BCa by analyzing the
VOCs; in this setting (pilot study), any stage of BCa patients was investigated.
2. Materials and Methods
2.1. Patients’ Characteristics
A prospective, single-center, controlled, non-randomized phase 2 study was carried
out. A total of 198 consecutive subjects were recruited: 102 patients with proven BCa and
96 controls (without BCa) (Figure 1).
J. Clin. Med. 2021, 10, x FOR PEER REVIEW 2 of 14
BCa is associated with the presence of specific VOCs in gas emitted from urine sam-
ples, due to the fact that during tumor growth, protein changes in malignant cells lead to
peroxidation of cell membrane components, producing VOCs [7,8]. Therefore, VOCs in
the urine headspace (the gas or empty space above the contents of a sealed container)
from BCa patients might play a role as a diagnostic tool. These compounds have been re-
vealed by pilot studies on the use of the olfactory capacity of dogs [9,10] and the use of
mass spectrometry [11]. They were detected even with an electronic nose with sensitiv-
ity and specificity rates of up to 70% for BCa [12], thereby opening new perspectives
in this field.
In this pilot study, we evaluated the potentially reliable and feasible diagnostic per-
formances of a VOC’s sensor device in BCa diagnosis, validated by updated statistical
methods, intended as validated and current methods of managing data and results. The
hypothesis was to evaluate the ability of the electronic nose to detect BCa by analyzing
the VOCs; in this setting (pilot study), any stage of BCa patients was investigated.
2. Materials and Methods
2.1. Patients’ Characteristics
A prospective, single-center, controlled, non-randomized phase 2 study was carried
out. A total of 198 consecutive subjects were recruited: 102 patients with proven BCa and
96 controls (without BCa) (Figure 1).
Figure 1. Flowchart.
The patients with confirmed BCa were enrolled before undergoing trans-urethral re-
section of bladder tumor (TURB-T). The controls were enrolled in the outpatient setting,
from among patients in follow-up for non-complicated benign prostatic hyperplasia or
urinary incontinence or for urinary lithiasis with long disease-free status. The controls did
not report macrohematuria or urinary symptoms consistent with a bladder lesion. They
underwent imaging of the urinary tract, with negative evidence of Bca. Neither urinary
cytology nor cystoscopy evaluations were done, since no suspicions of BCa were detected.
Subjects with proven active urinary infections, urinary lithiasis, indwelling catheters, and
other active concurrent urological and non-urological malignancies were excluded.
In the case of misclassification by the electronic nose as false-positive, and after ob-
taining informed consent, the controls underwent serial assessments with further uri-
nary cytology and radiological evaluations of t h e urinary tract in order to rule out
BCa. We performed radiological imaging of the urinary tract (ultrasound and UroCT
or UroMR in the event of a suspicious or doubtful finding at the first-level exam)
according to EAU guideline recommendations: “Use renal and bladder ultrasound
and/or computed tomography-intravenous urography (CT-IVU) during the initial
workup in patients with hematuria” (strength rating: strong) [1]. We performed voided
and washing cytology in order to complete a hypothetical initial workup for suspected
bladder cancer. None of the false-positive controls required an endoscopic evaluation,
and none of them received a bladder cancer diagnosis.
We did not perform a prior sample size calculation but based the sample size on
the recommendations of Teare et al. [9]: “If the primary outcome is binary, a total of at
Figure 1. Flowchart.
The patients with confirmed BCa were enrolled before undergoing trans-urethral
resection of bladder tumor (TURB-T). The controls were enrolled in the outpatient setting,
from among patients in follow-up for non-complicated benign prostatic hyperplasia or
urinary incontinence or for urinary lithiasis with long disease-free status. The controls did
not report macrohematuria or urinary symptoms consistent with a bladder lesion. They
underwent imaging of the urinary tract, with negative evidence of Bca. Neither urinary
cytology nor cystoscopy evaluations were done, since no suspicions of BCa were detected.
Subjects with proven active urinary infections, urinary lithiasis, indwelling catheters, and
other active concurrent urological and non-urological malignancies were excluded.
In the case of misclassification by the electronic nose as false-positive, and after
obtaining informed consent, the controls underwent serial assessments with further urinary
cytology and radiological evaluations of the urinary tract in order to rule out BCa. We
performed radiological imaging of the urinary tract (ultrasound and UroCT or UroMR
in the event of a suspicious or doubtful finding at the first-level exam) according to
EAU guideline recommendations: “Use renal and bladder ultrasound and/or computed
tomography-intravenous urography (CT-IVU) during the initial workup in patients with
hematuria” (strength rating: strong) [
1
]. We performed voided and washing cytology in
order to complete a hypothetical initial workup for suspected bladder cancer. None of the
false-positive controls required an endoscopic evaluation, and none of them received a
bladder cancer diagnosis.
We did not perform a prior sample size calculation but based the sample size on
the recommendations of Teare et al. [
9
]: “If the primary outcome is binary, a total of at
J. Clin. Med. 2021,10, 4984 3 of 14
least 120 subjects (60 in each group) are required in the pilot trial”. We therefore enrolled
at least 60 subjects per group. We joined the STARD guidelines for reporting diagnostic
studies (flowchart).
2.2. Sample Management
Urine headspace measurements were performed using the Cyranose 320
®
device
(Sensigent), a volatile gas analyzer equipped with 32 sensors. The urine headspace is the
gas or empty space above the contents of a sealed container.
We collected 10–25 mL of urine. Urine samples were taken in the morning after the
evaluation of inclusion and exclusion criteria and acquisition of signed informed consent.
Samples were collected in the morning since the urine is generally more concentrated
(due to the length of time, the urine is allowed to remain in the bladder) and, therefore,
contains relatively higher levels of cellular elements and analytes that could emit a higher
level of VOCs. The urine samples from BCa patients were collected before TURBT and
from controls in outpatient settings. Sterile urine samples were collected after at least 12 h
of fasting from solids (in order to minimize possible carcinogens that are digested, e.g.,
nitrosamines in meat products) and liquids. The purposes of using morning void urine
were standardization of the sample collection (such as in its timing) and the attempt to
empirically minimize the potential lack of activation of the sensors due to less concentrated
urine (the inability to identify individual molecules that compose the odor might be
overcome by a “richer” headspace); the latest aspect surely deserves to be further explored.
The urine samples were then immediately placed in a sealed container at 37–38.5
◦
C for
stabilization (at least for 1 h to obtain a steady headspace). The stabilized samples were
analyzed within 2 h of collection after device calibration. The calibration consisted of 10 s
analysis of the headspace of a container with 10 mL of sterile saline (the container had the
same characteristics as the ones used for the evaluation of the headspace of urine in the
exam). The collection, stabilization, and analysis were standardized to avoid bias regarding
these steps.
This tool has not yet been independently validated; one of the purposes of the pilot
study was to pave the way for the standardization of the proposed system in order to
precisely validate it independently.
2.3. Electronic Nose
Electronic noses include three major parts: a sample delivery system, a detection sys-
tem, and a computing system. Essentially the instrument consists of headspace sampling, a
chemical sensor array, and pattern recognition modules to generate signal patterns that are
used for characterizing odors [
13
]. Cyranose 320
®
has a matrix of 32 sensors of carbon black
polymer. When an odor (chemical input) is presented to the electronic nose, it causes a
physical change in the sensors, which is detected by the transducers and converted into an
electrical signal, creating a specific signature or smellprint. The readings of the 32 sensors
were taken and recorded in the software. All readings of the 32 sensors were extracted and
further analyzed. Each sensor is not specific for a singular and different VOC; indeed, each
different VOC stimulates the activation of one or more than one sensor. The 32 sensors
constitute a matrix of carbon black polymers; the use of different types allows avoiding or
at least minimizing the disadvantages presented by each one separately and maximizing
their advantages. The 32 sensors are not identical, and each sensor provides a response
related to the variation in a physical quantity that characterizes the sensor itself, such as
the variation in conductivity, the resonance frequency, or the mass. Like the human nose,
the electronic nose does not perform a chemical speciation of the analyzed odor, so it is not
able to identify the individual molecules that compose it, but the set of sensors produces
a sort of “olfactory imprint,” which can be classified on the basis of a reference database
acquired by the instrument in a preliminary training phase. On the basis of this concept,
the electronic nose, in this setting and specific (exploratory) phase of study/evaluation, is
not able to distinguish a single VOC from a cluster.
J. Clin. Med. 2021,10, 4984 4 of 14
2.4. Results Management and Statistics
The analyses were repeated at least 2 times per sample to validate the VOCs’ nature
by controlling the Euclidean distance between the values. We considered all measurements
in the analyses. The results obtained with the electronic nose were directly compared to
the final histopathological reports after TURB-T or RC (cytology was performed only in
the case of recurrent HG tumor or in the case of suspected Cis).
A non-parametric combination (NPC) of the dependent permutation test methodol-
ogy [
14
,
15
] was performed on a total of 198 sample subjects in order to predict whether
the subject belonged or did not belong to the BCa patients’ group. We computed partial
permutation tests for the two independent samples on subsamples determined by one
of the recorded characteristics of the patients (i.e., gender, smoking, and comorbidity),
evaluating the ability of each sensor in discerning people from the two different health
status groups. We then provided p-values adjusted for multiplicity using a closed testing
procedure, controlling the FWE rate. The NPC let us consider a test as positive or negative
in terms of detection. We applied the Tippet combination function [
16
] to combine the
partial tests obtained from the analysis of subsamples determined by levels of the same
factor (such as male/female or smoker/non-smoker/former smoker). This allowed us to
evaluate the global performances of each sensor.
We performed permutation tests on BCa patients to test the sensors’ ability to detect
the potential effect of different tumor types or comorbidities on the VOCs’ composition in
the urine headspace. The cutoff for sensor activation was generated by the nonparametric
statistics, revealing the activation of the sensor by the presence of VOCs but not the concen-
tration of VOCs. We performed a discriminant analysis classification of multiple repeated
detections to classify observations into groups and to describe the relative importance of
variables for distinguishing between groups. This analysis allowed for the identification of
the level of sensor activation: taking into account all measurements, all sensors activated in
each measurement in the BCa group were considered as the most effective. We assumed
misclassification as a false-negative or a false-positive result.
Categorical variables were evaluated as counts and percentages, and x
2
-distribution
was used; continuous variables were evaluated by using mean
±
standard deviation;
parametric and nonparametric variables were evaluated with the t-test and the chi-square
test; logistic regression was performed; and Pearson’s test was used for correlation analysis.
Statistical significance was set at p< 0.05.
3. Results
We reported baseline and tumor characteristics in Tables 1and 2, respectively. Consid-
ering baseline characteristics, there was no statistically significant difference between the
two groups.
The reached sensitivity to detect BCa by using the electronic nose was 74.1% (10.8–100%)
and the specificity 76% (3–95.8%) (AUC 0.563 (0.392–0.752)) when considering the analysis
of all sensors. When we considered sensors for which the null hypothesis of equality
was rejected (by selecting the most efficient sensors, which amounted to 9 of the 32), the
sensitivity increased to 91.1% (72.5–100) and the specificity to 89.1% (81–95.8) (AUC 0.601
(0.578–0.626)).
The overall discriminating power rate by the discriminant analysis classification
was 78.8%; consequently, 21.2% of all subjects (42/198) were not correctly classified (or
misclassified): 13/96 (13.5%) controls classified as false positive and 29/102 (28.4%) BCa
patients as false negative. In the N-MIBC group, the rate of false negatives (misclassification
rate) was 22.2% in the case of low-grade pTa, 11.1% for high-grade pTa, 18.2% for high-
grade pT1, 6.1% for low-grade pT1, and 1.6% for solitary/concomitant pCis. In the MIBC
group, the rate of false negatives (misclassification rate) was 23.8%. The misclassified
tumors had a mean size of 2.69
±
1.08 cm (range 1.5–5): 2 cm (range 1–4) for low-grade
N-MIBC patients, 3.2 cm (range 1–6) for high-grade N-MIBC patients, and 3.4
±
1.14 cm in
J. Clin. Med. 2021,10, 4984 5 of 14
MIBC patients. Other investigations (cystoscopy, cytology, and/or radiology) succeeded in
diagnosing cancer in all these patients.
Table 1. Baseline characteristics.
Group 1
(BCa Patients)
Group 2
(Controls) p-Value
No. of patients 102 96
Age, mean years ±SD (range) 70.8 ±12.4 (33–93) 68.3 ±10.7 (42–86) 0.29
Gender, no. of patients (%) 0.56
•Men 87 (85.3%) 79 (82.3%)
•Women 15 (14.7%) 17 (17.7%)
Smoking, no. of patients (%)
•Non-smoker 10 (9.8%) 16 (16.7%) 0.15
•Former smoker 45 (44.1%) 31 (32.3%) 0.16
•Smoker 47 (46.1%) 49 (51%) 0.49
Comorbidities, no. of patients (%)
•Arterial hypertension 56 (54.9%) 41 (42.7%) 0.08
•Diabetes mellitus 22 (21.6%) 16 (16.7%) 0.38
•History of AMI 21 (20.6%) 14 (14.6%) 0.27
•Kidney failure 9 (8.8%) 4 (4.2%) 0.18
•COPD 16 (15.7%) 9 (9.4%) 0.18
•Liver disorder 4 (3.9%) 1 (1%) 0.20
•Dyslipidemic condition 21 (20.6%) 12 (12.5%) 0.13
•Other neoplastic disease 11 (10.8%) 6 (6.2%) 0.25
SD: standard deviation; AMI: acute myocardial infarction; COPD: chronic obstructive pulmonary disease.
Table 2. Tumor characteristics.
Total NMIBC MIBC p-Value
Histology, no. of patients (%)
0.00
•Pure urothelial 95 (93.1%) 80 (98.8%) 15 (71.4%)
•With differentiation 7 (6.9%) 1 (1.2%) 6 (28.6%)
Grade, no. of patients (%)
0.00
•Low 31 (30.4%) 31 (38.3%) -
•High 71 (69.6%) 50 (61.7%) 21 (100%)
Stage, no. of patients (%)
•pTa 36 (35.3%) 36 (44.4%) -
•pT1 33 (32.3%) 33 (40.7%) -
•Solitary pCis 9 (8.8%) 9 (11.1%) -
•Concurrent pCis 3 (2.9%) 3 (3.7%) -
•pT2 16 (15.7%) -16 (76.2%)
• ≥pT3 5 (4.9%) -5 (23.8%)
Focality, no. of patients (%)
0.11
•Monofocality 43 (42.2%) 31 (38.3%) 12 (57.1%)
•Multifocality 59 (57.8%) 50 (61.7%) 9 (42.9%)
Mean size, cm ±SD (range) 2.7 ±1.31 (1–6) 2.4 ±1.2 3.7 ±1.3 0.00
First diagnosis, no. of patients (%) 43 (42.2%) 33 (40.7%) 10 (47.6%) 0.12
Median time from 1st diagnosis, year
±
SD (range)
1.8 ±1.9 (0–5) 1.9 ±2.0 (0–5) 1.2 ±1.7 (0–5) 0.43
NMIBC: non-muscle invasive bladder cancer; MIBC: muscle invasive bladder cancer; Cis: carcinoma in situ; SD: standard deviation.
J. Clin. Med. 2021,10, 4984 6 of 14
By comparing the mean values detected by each sensor, the difference was not statistically
significant (p= 0.20) in the comparison between BCa patients and controls. By comparing
the absolute values detected by each sensor, 18 of 32 sensors revealed statistically significant
differences between the two groups (they were defined as the most responsive).
Age, history of acute myocardial infarction (AMI), presence of BCa, and the mean
sensor activation value represented the variables determining the positivity (i.e., activation
level) of sensors (p< 0.05) in the multivariate analysis of all subjects corrected for the sensors’
response. In the multivariate analysis of BCa patients, none of the tumor characteristics
(histology, grade, stage, focality, mean size, first diagnosis, median time from first diagnosis)
emerged as an independent predictor of the sensors’ response.
Interestingly, we reported a linear correlation between the level of the sensors’ activa-
tion and the presence of BCa (r = 0.08, p= 0.01).
Moreover, by considering all subjects, we reported statistically significant differences
in VOC measurement-identified patterns between controls and BCa patients and regarding
gender (for 28/32 sensors), smoking status (for 30/32 sensors), and some comorbidities
(for up to 27/32 sensors, such as in the case of diabetes mellitus and COPD) (
Appendix A
).
Furthermore, we reported statistically significant differences (mean p< 0.05) in the dis-
criminating power rate in BCa patients by comparing N-MIBC to MIBC for 17/32 (53.1%)
sensors (Figure 2a); high- to low-grade tumors for 8/32 (25%) sensors (Figure 2b); pure
urothelial to other histopathological differentiation for 14/32 (43.8%) sensors (Figure 2c);
and small to large tumors for 7/32 (21.9%) sensors (Figure 2e).
J. Clin. Med. 2021, 10, x FOR PEER REVIEW 6 of 14
the 32), the sensitivity increased to 91.1% (72.5–100) and the specificity to 89.1%
(81–95.8) (AUC 0.601 (0.578–0.626)).
The overall discriminating power rate by the discriminant analysis classification was
78.8%; consequently, 21.2% of all subjects (42/198) were not correctly classified (or mis-
classified): 13/96 (13.5%) controls classified as false positive and 29/102 (28.4%) BCa
patients as false negative. In the N-MIBC group, the rate of false negatives (misclassifi-
cation rate) was 22.2% in the case of low-grade pTa, 11.1% for high-grade pTa, 18.2% for
high-grade pT1, 6.1% for low-grade pT1, and 1.6% for solitary/concomitant pCis. In the
MIBC group, t h e rate of false negatives (misclassification rate) was 23.8%. The mis-
classified tumors had a mean size of 2.69 ± 1.08 cm (range 1.5–5): 2 cm (range 1–4) for low-
grade N-MIBC patients, 3.2 cm (range 1–6) for high-grade N-MIBC patients, and 3.4 ± 1.14
cm in MIBC patients. Other investigations (cystoscopy, cytology, and/or radiology) suc-
ceeded in diagnosing cancer in all these patients.
By comparing the mean values detected by each sensor, the difference was not sta-
tistically significant (p = 0.20) in the comparison between BCa patients and controls. By
comparing the absolute values detected by each sensor, 18 of 32 sensors revealed
statistically significant differences between the two groups (they were defined as the
most responsive).
Age, history of acute myocardial infarction (AMI), presence of BCa, and the mean
sensor activation value represented the variables determining the positivity (i.e., acti-
vation level) of sensors (p < 0.05) in the multivariate analysis of all subjects corrected
for the sensors’ response. In the multivariate analysis of BCa patients, none of the tumor
characteristics (histology, grade, stage, focality, mean size, first diagnosis, median time
from first diagnosis) emerged as an independent predictor of the sensors’ response.
Interestingly, we reported a linear correlation between the level of th e sensors’
activation and the presence of BCa (r = 0.08, p = 0.01).
Moreover, by considering all subjects, we reported statistically significant differ-
ences in VOC measurement-identified patterns between controls and BCa patients and
regarding gender (for 28/32 sensors), smoking status (for 30/32 sensors), and some
comorbidities (for up to 27/32 sensors, such as in the case of diabetes mellitus and COPD)
(Appendix A). Furthermore, we reported statistically significant differences (mean p <
0.05) in the discriminating power rate in BCa patients by comparing N-MIBC to MIBC for
17/32 (53.1%) sensors (Figure 2a); high- to low-grade tumors for 8/32 (25%) sensors
(Figure 2b); pure urothelial to other histopathological differentiation for 14/32 (43.8%)
sensors (Figure 2c); and small to large tumors for 7/32 (21.9%) sensors (Figure 2e).
(a) (b)
J. Clin. Med. 2021, 10, x FOR PEER REVIEW 7 of 14
(c) (d)
(e)
Figure 2. Measurement patterns identified (tumor characteristics): (a) NMIBC vs. MIBC; (b) high vs.
low grade; (c) urothelial vs. other; (d) monofocal vs. multifocal; and (e) small vs. large.
4. Discussion
The electronic nose is extensively used in several type of malignancies, such as
respiratory diseases (including lung cancer), colorectal cancer, and prostate cancer [17].
The research on VOCs was initially developed by using the olfactory capacity of dogs;
Willis et al. demonstrated the presence of specific BCa VOCs with the aid of trained dogs,
achieving interesting sensitivity and specificity rates up to 86% and 92%, respectively
[10]. Successively, with gas chromatography (GC) and mass spectrometry (MS), the elec-
tronic nose has been introduced in the field with the potential to discriminate between
urine samples of BCa patients and controls.
Undoubtedly, gas sensor arrays offer practical advantages in VOCs’ detection, but
they are not able to identify the chemical nature of VOCs [18]. Despite this limit, a GC
device combined with a VOC recognition pattern achieved accuracy rates of 93–100% in a
pilot study on BCa [19]. Gas chromatography with mass spectrometry (GC-MS) is actually
recognized as an important analytical technique in the field of metabolomics due to high
sensitivity, reproducibility, and peak resolution [20]. Jobu et al. showed that the score
plots differed between BCa patients and controls on principal components analysis
(PCA) mapping. The authors identified five substances as BCa biomarkers but only with
a potential role: ethylbenzene, nonanoyl chloride, dodecanal, (Z)-2-nonenal, and 5-dime-
thyl-3(2H)-isoxazolone. Interestingly, the authors reported that medication could only
slightly influence urine odor. The authors concluded that a urine-odor-based BCa diag-
nosis might prove more sensitive than urinary cytology [21].
In the study by Khalid et al., by using GC-MS, the two-group linear discriminant
analysis (LDA) correctly identified 24/24 (100%) cancer cases and 70/74 (94.6%) controls.
For partial least- squares discriminant analysis (PLS-DA), the correct leave-one-out
Figure 2. Cont.
J. Clin. Med. 2021,10, 4984 7 of 14
J. Clin. Med. 2021, 10, x FOR PEER REVIEW 7 of 14
(c) (d)
(e)
Figure 2. Measurement patterns identified (tumor characteristics): (a) NMIBC vs. MIBC; (b) high vs.
low grade; (c) urothelial vs. other; (d) monofocal vs. multifocal; and (e) small vs. large.
4. Discussion
The electronic nose is extensively used in several type of malignancies, such as
respiratory diseases (including lung cancer), colorectal cancer, and prostate cancer [17].
The research on VOCs was initially developed by using the olfactory capacity of dogs;
Willis et al. demonstrated the presence of specific BCa VOCs with the aid of trained dogs,
achieving interesting sensitivity and specificity rates up to 86% and 92%, respectively
[10]. Successively, with gas chromatography (GC) and mass spectrometry (MS), the elec-
tronic nose has been introduced in the field with the potential to discriminate between
urine samples of BCa patients and controls.
Undoubtedly, gas sensor arrays offer practical advantages in VOCs’ detection, but
they are not able to identify the chemical nature of VOCs [18]. Despite this limit, a GC
device combined with a VOC recognition pattern achieved accuracy rates of 93–100% in a
pilot study on BCa [19]. Gas chromatography with mass spectrometry (GC-MS) is actually
recognized as an important analytical technique in the field of metabolomics due to high
sensitivity, reproducibility, and peak resolution [20]. Jobu et al. showed that the score
plots differed between BCa patients and controls on principal components analysis
(PCA) mapping. The authors identified five substances as BCa biomarkers but only with
a potential role: ethylbenzene, nonanoyl chloride, dodecanal, (Z)-2-nonenal, and 5-dime-
thyl-3(2H)-isoxazolone. Interestingly, the authors reported that medication could only
slightly influence urine odor. The authors concluded that a urine-odor-based BCa diag-
nosis might prove more sensitive than urinary cytology [21].
In the study by Khalid et al., by using GC-MS, the two-group linear discriminant
analysis (LDA) correctly identified 24/24 (100%) cancer cases and 70/74 (94.6%) controls.
For partial least- squares discriminant analysis (PLS-DA), the correct leave-one-out
Figure 2.
Measurement patterns identified (tumor characteristics): (
a
) NMIBC vs. MIBC; (
b
) high vs. low grade; (
c
) urothelial
vs. other; (d) monofocal vs. multifocal; and (e) small vs. large.
4. Discussion
The electronic nose is extensively used in several type of malignancies, such as respi-
ratory diseases (including lung cancer), colorectal cancer, and prostate cancer [
17
]. The re-
search on VOCs was initially developed by using the olfactory capacity of dogs;
Willis et al
.
demonstrated the presence of specific BCa VOCs with the aid of trained dogs, achieving
interesting sensitivity and specificity rates up to 86% and 92%, respectively [
10
]. Succes-
sively, with gas chromatography (GC) and mass spectrometry (MS), the electronic nose has
been introduced in the field with the potential to discriminate between urine samples of
BCa patients and controls.
Undoubtedly, gas sensor arrays offer practical advantages in VOCs’ detection, but
they are not able to identify the chemical nature of VOCs [
18
]. Despite this limit, a GC
device combined with a VOC recognition pattern achieved accuracy rates of 93–100%
in a pilot study on BCa [
19
]. Gas chromatography with mass spectrometry (GC-MS) is
actually recognized as an important analytical technique in the field of metabolomics due
to high sensitivity, reproducibility, and peak resolution [
20
]. Jobu et al. showed that the
score plots differed between BCa patients and controls on principal components analysis
(PCA) mapping. The authors identified five substances as BCa biomarkers but only with a
potential role: ethylbenzene, nonanoyl chloride, dodecanal, (Z)-2-nonenal, and 5-dimethyl-
3(2H)-isoxazolone. Interestingly, the authors reported that medication could only slightly
influence urine odor. The authors concluded that a urine-odor-based BCa diagnosis might
prove more sensitive than urinary cytology [21].
In the study by Khalid et al., by using GC-MS, the two-group linear discriminant
analysis (LDA) correctly identified 24/24 (100%) cancer cases and 70/74 (94.6%) con-
trols. For partial least-squares discriminant analysis (PLS-DA), the correct leave-one-out
cross-validation (LOOCV) prediction values were 95.8% (23/24 cancer cases) and 94.6%
(69/74 controls) [19].
It is critical to note that in the case of more advanced tumors (such as muscle invasive),
patients might be misclassified as negative regardless of the method applied in VOCs’
detection. This is possibly due to tumor by-products overwhelming the VOCs. Previous
canine olfactory studies support this hypothesis: high-grade advanced cancers were missed
more frequently than low-grade ones [
10
]. Surely, the electronic nose is not intended as a
diagnostic tool for advanced disease, which deserve to be comprehensively detected and
studied by standardized and consolidated tests.
An electronic nose is “an instrument which comprises an array of electronic chemical
sensors with partial specificity and an appropriate patter-recognition system, capable of
recognizing simple or complex odours” [
22
]. This device mimics the mammalian olfactory
J. Clin. Med. 2021,10, 4984 8 of 14
system and can identify different complex odors, comparing the incoming odor with pat-
terns previously learned [
23
]. When an odor (chemical input) is presented to the electronic
nose, it causes a physical change in the sensors, which is detected by the transducers and
converted into an electrical signal, creating a specific signature or smellprint [
22
]. The
rise and decline in the signal depend on some parameters: nature of the odor (type and
concentration of the compounds), reaction and diffusion between odor and sensors, type
of sensor, and ambient conditions [22].
Methods based on mass spectrometry analysis can detect and identify which com-
pounds present in air samples are useful for pathophysiologic research [
24
]. Yet, these
methods are time consuming and expensive and depend on a skilled operator; this makes
them difficult to be used in real clinical settings. Electronic noses have the potential to
overcome these disadvantages because they are relatively inexpensive and easy to use and
provide rapid analysis [
22
]. To achieve this goal, it is necessary to create a prediction model
with a training set of samples and external validation of the model for further application.
Few studies have examined the ability of electronic nose technology to assess the VOCs’
role in BCa diagnosis. Bernabei et al. confirmed a close correlation between VOCs in the
urine headspace and urological cancers by processing data with both PCA and PLS-DA [
25
].
The latest experience with an electronic nose was reported by Heers et al. calculating the
Mahalanobis distance and LDA. After storage at
−
20
◦
C, the system correctly detected
28/30 BCa samples and 26/30 controls (p< 0.01), achieving sensitivity and specificity rates
of 93.3% and 86.7%, respectively. Similar results were obtained after storage at
−
80
◦
C
(sensitivity and specificity both 93.3%). However, the authors stressed the need for further
research to test for possible confounders [
26
]. One of the differences with the research of
Heers et al. was the different test methodology. Moreover, the authors stabilized the speci-
men at
−
20
◦
C and
−
80
◦
C, and this surely added costs to the entire process. Our research
did not include the lowering of the sample temperature, and we stabilized the collected
specimen in a sealed container at 37–38.5
◦
C; anyway, this surely represents another point
of discussion and further improvement of the technique. Furthermore, unlike in the study
of Heers et al., our study cohort was larger and more complex (different cases at different
stages), and the patients were consecutive.
Matsumoto et al. explored the electronic nose ability to distinguish urological diseases
by the urinary odor feature: they compared 36 untreated patients with BCa, 29 with
urolithiasis, 10 with urinary tract infection, and 27 healthy volunteers. The authors used
a device with only two sensors, and they established the quantity of odor with the value
of
θ
detected during a measurement. They reported that the angle of the two sensors (
θ
)
depended on the kinds of chemical substances, thus defining
θ
as the feature of odor. The
resulting diagnostic sensitivity for bladder cancer was 61.4%, and specificity was 52.8%: the
authors concluded that this non-invasive instrument is useful for distinguishing bladder
cancer from other benign conditions [27].
The device showed high discriminatory power, with promising accuracy, sensitivity,
and specificity rates. However, our data was not fully comparable with other experiences
(Table 3) because of the differences in sampling and analytical methods. The lower rates
achieved in our study could be related to the larger and more varied sample of patients.
Table 3. Comparison of accuracy, sensitivity, and specificity.
Accuracy, % Sensitivity, % Specificity, %
GC-MS [8,15,16] 70–100% 70–100% 42–97%
Sniffer dogs [5,6] 70–90.1% 55–86% 56–92%
Electronic nose [19] 86.7–93.3% 93.3% 86.7%
Our experience 78.8% (71.6–87.5) 91.1% (72.5–100) 89.1% (81–95.8)
GC-MS: gas chromatography mass spectrometry.
In this pilot study, we excluded BCa patients with hematuria or bacteriuria to minimize
possible bias related to conditions assumed as confounders: inflammatory cells and/or red
J. Clin. Med. 2021,10, 4984 9 of 14
blood cells might affect (even if with controversial results) the detection accuracy of the
electronic nose [
25
]. After selecting NMIBC patients and controls, the overall discriminatory
power was 81.2%. This meant that 11.6% of controls were misclassified as cases of cancer
and 37.3% of cancer patients were overlooked. The highest misclassification rate (22.2%)
was reported in low-grade pTa patients, the least aggressive NMIBC (which does not
warrant such intensive surveillance as high-risk disease). In the case of false-positive results,
we decided not to perform cystoscopy in controls due to the exploratory nature of this pilot
study, whose results, even if promising, could not justify an uncomfortable, costly, and
not-free-from-complications procedure in patients without suspicions of bladder cancer.
The misclassification rate surely deserves to be fully explored, as urologists might not
be comfortable with these data. However, we believe that selecting the most responsive
sensors might overcome this limitation. The variations in the misclassification rates could
be justified by many events potentially occurring in any phase of the management of
samples or results. One of the aims of this study was precisely to lay the groundwork
to standardize the entire procedure in order to minimize variations in the management
and then in the results. Effectively, some sensors revealed a sensitivity near 100% and a
specificity of up to 95.8%, assuming that some cancers emit specific VOCs.
Data from the multivariate analysis might be further explained by the characteristic
heterogeneity of BCa and BCa patients, difficult to unequivocally classify. The overall
detection ability of the electronic nose was not influenced by smoking habits, despite the fact
that in the subsequent analysis, we reported that an active smoker status affects the urinary
headspace composition. Moreover, 27 sensors (84.4%) were able to distinguish subjects
with or without comorbidities; other VOC studies have provided no data on the issue of
comorbidities; therefore, our findings could surely represent a basis for future research.
The Cyranose 320
®
device could logistically be widely available at some point, con-
sidering cost (that could be quickly amortized in the light of the high number of patients
who could avoid more costly tests), size (4
×
8.8
×
2 in (10
×
22
×
5 cm
3
), 30 oz (0.9 kg)),
upkeep (1 year warranty), ease of use (with a short learning curve), and transportability.
Several urine-based biomarkers (Table 4) have been previously validated and partially
accepted for clinical use, since current commercially available urinary biomarker-based
tests are not sufficiently validated to be widely used in clinical practice yet. These tests
were mainly compared to the diagnostic accuracy of the standard urinary cytology, and
most of the proposed molecular markers were able to improve the sensitivity with similar
or lower specificity when compared to urinary cytology. However, the variability in results
among the different studies was strong [28,29].
The main limitations of our study were small sample sizes, strict inclusion/exclusion
criteria, heterogeneity between patients and controls, unbalanced misclassification rates.
the differences in sensor reading seeming patient specific; a lack of comparison with cy-
tology, the inability of the electronic nose to identify a specific VOC, the lack of further
analysis of patients’ subcategories, and the conception of the study on the general perfor-
mance of the electronic nose and not on the best sensors. Another significant limitation of
the study was represented by the low AUC; the basis of this pilot study might pave the
way to improve the diagnostic performances of this device. The high false-negative rates
could certainly limit the electronic nose as a screening tool; further device development
and larger sample sizes could reduce this limitation. Another significant limitation of the
study was represented by the selection of control patients and the fact that they never
underwent cystoscopy to rule out cancer; the controls had no suspicions of bladder cancer
(hematuria or symptoms consistent with a bladder lesion) that could justify a complete
workup for bladder cancer and even a cystoscopy. We did not repeat the analysis of the
samples when voided at different time points from the same patients; it represented a
significant limitation but surely might represent a further line of research deserving full
consideration. We did not present the data on tandem analysis from the same specimen,
and it was one of the most important limitations of the study; surely, the comparison of the
J. Clin. Med. 2021,10, 4984 10 of 14
results with tandem analysis might represent one of the further lines of research for the
optimal development of the device.
Table 4. Comparison of the e-nose to other urine-based biomarkers regarding sensitivity and specificity.
Test Target of Measurement/
Mechanism of Detection
Sensitivity,
% (Range)
Specificity
% (Range)
NMP22 BladderChek * [28,29]Measurement of nuclear matrix proteins
(quantitative ELISA) (11–85.7%) (77–100%)
NMP22 * [28,29]Measurement of nuclear matrix proteins
(qualitative point-of-care test) (24–81%) (49–100%)
BTA STAT * [28,29]
Measurement of human complement
factor-H-related protein
(point-of-care test)
(40–72%) (29–96%)
BTA track * [28,29]
Measurement of human complement
factor-H-related protein
(quantitative ELISA)
(50–62%) (68–87%)
Immunocyt * [28,29]
Fluorescent test combining 3 monoclonal
antibodies (M344, LDQ10, 19A211) (50–85%) (62–86%)
UroVysion * [28,29]
Measurement of aneuploidy for
chromosomes 3, 7, and 17 and loss of the
9p21 locus via fluorescence in situ
hybridization (FISH)
(13–100%) (63–100%)
Cxbladder monitor [28,29]Measurement of 5 urine mRNA
biomarkers and 2 clinical variables (91–93%) -
Bladder cancer (UBC) test [28,29] Measurement of cytokeratins 8 and 18 (12–80%) (77.3–97%)
EpiCheck [28–30]DNA methylation
(15 biomarkers) changes 86% (excluding Ta-LG) 86%
ADXBLADDER [28,29,31] Detection of MCM5 antibodies 73.5% (62.7–82.6) 33.3% (18.6–51)
Our experience VOCs’ detection 91.1% (72.5–100%) 89.1% (81–95.8%)
* FDA-approved urinary assays to use alongside cystoscopy for diagnosis and surveillance.
It is clear that we are at the beginning, but we think that the electronic nose has several
potential advantages: it is quick, it is not dependent on the operator, and it could be useful
in cases of inconclusive findings on cystoscopy/cytology/FISH. The measurements might
allow the realization of a database of digitized patterns in order to follow up the same
patient over time. Surely, an established sensitivity/specificity parameter selection might
be universalized in order to make this technique more broadly applicable. Furthermore,
the establishment of a sort of correlation with cytology might lead to improved diagnostic
accuracy. Looking at the bigger picture, it would be more helpful to follow patients who
may have initial results when a tumor is present and different results after it has been
resected or treated.
Since NMIBC encompasses a really broad clinical scenario, the exploratory nature of
this research deserves to be considered first as an initial step toward the future directions
of the development of this tool. One of the possible scenario in which the tool could be
mostly useful is in the follow-up of NMIBC patients (including BCG-treated patients): the
diagnostic accuracy of the electronic nose might delay the interval between endoscopic
controls, and it might confirm (or not) the indication of further exams such as in the
case of negative cytology and synchronous positive e-nose results (indication to perform
endoscopy) or in the case of negative cytology and synchronous negative e-nose results
(indication to not perform endoscopy). As for hematuria, it is still widely regarded as a
confounding factor: when the electronic nose reaches an optimal level of standardization,
it may play a role in this scenario.
J. Clin. Med. 2021,10, 4984 11 of 14
Moreover, the standardization of the method might be relevant in terms of health
economics; a new effective and non-invasive diagnostic test could be useful as an additional
tool or as a replacement for standard diagnostic procedures, with considerable potential
cost savings.
The promising results of the study might make this tool worthy to be prospectively
studied in a randomized trial; in this way, it might allow reaching the optimal level of
standardization and the test can be considered safe and reliable to be integrated into
clinical practice.
5. Conclusions
The aim of the study was the clinical evaluation of the diagnostic performance of a
VOC sensor device in BCa. Once validated by an updated statistical method and after
further necessary refining of the test (according to recent advances in all fields, including
statistical techniques and methods for data analysis), electronic nose technology might be
used in the future for screening, diagnosis, assessment of the treatment response, or even
staging. Once the device is standardized, especially regarding possible confounders, it
might be even used in the initial evaluation in the case of gross hematuria.
This pilot study might pave the way for further trials designed to detect the best sensor,
and then to select a panel of sensor tests to use for detection of BCa, by narrowing down to
those defined as more responsive and appropriate. The development and optimization of
a non-invasive, repeatable, accurate, potentially cost-effective method in BCa diagnosis,
such as the use of an electronic nose, might improve patient care.
Author Contributions:
Conceptualization, P.B.; methodology, P.B., L.D.G. and M.R. (Marco Racioppi);
software, L.D.G., L.S., R.A.G. and R.C.; validation, P.B., E.S. and G.P.; formal analysis, L.D.G. and
M.R. (Mauro Ragonese); investigation, L.D.G.; resources, P.B.; data curation, L.D.G., L.S., R.A.G. and
R.C.; writing—original draft preparation, L.D.G.; writing—review and editing, P.B. and M.R. (Marco
Racioppi); visualization, P.B.; supervision, P.B. and M.R. (Marco Racioppi). All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study was conducted according to the guidelines of
the Declaration of Helsinki and approved by the institutional review board of the Fondazione Poli-
clinico Universitario “Agostino Gemelli” IRCCS—UniversitàCattolica del Sacro Cuore di Roma
(protocol code e-nose/v.1/2019). This research involving human subjects was registered in the
publicly accessible ICMJE accepted registry: https://eudract.ema.europa.eu/ with EudraCT num-
ber: 2020-000145-14 (retrospectively registered on 10 January 2020).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study. Written informed consent has been obtained from the patients to publish this paper.
Data Availability Statement:
The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
We reported statistically significant differences in VOC measurement-identified pat-
terns (by considering all subjects) between controls and BCa patients, both male and female
(8/32 and 28/32 sensors, respectively); 28/32 (87.5%) sensors showed significant differ-
ences between controls and BCa patients by combining results for both sexes (Figure A1).
J. Clin. Med. 2021,10, 4984 12 of 14
J. Clin. Med. 2021, 10, x FOR PEER REVIEW 12 of 14
Appendix A
We reported statistically significant differences in VOC measurement-identified patterns
(by considering all subjects) between controls and BCa patients, both male and female (8/32
and 28/32 sensors, respectively); 28/32 (87.5%) sensors showed significant differences between
controls and BCa patients by combining results for both sexes (Figure A1).
Figure A1. Measurement patterns identified (baseline characteristics).
We reported statistically significant differences in VOC measurement- identified
patterns (by considering all subjects) between controls and BCa patients, both smokers
and non-smokers (30/32 and 19/32 sensors, respectively); 30/32 (93.75%) sensors showed
significant differences by combining the results for the three smoking categories
(non-smokers, formers, and smokers). None of the sensors revealed a potential predictor
role of previous exposure to tobacco smoke.
We reported statistically significant differences in VOC measurement- identified
patterns (by considering all subjects) between controls and BCa patients, both without
and with comorbidities: arterial hypertension (25/32 sensors, 78.1%), diabetes mellitus
(27/32 sensors, 84.4%), a history of AMI or kidney failure (in both cases 19/32 sensors,
59.4%), chronic obstructive pulmonary disease (COPD) (27/32 sensors, 84.4%), or other
neoplastic diseases (14/32 sensors, 43.8%).
We did not report statistically significant differences by analyzing the presence of
liver disorders or dislipidemic conditions due to the small number of controls.
Statistically significant differences were also associated with the presence of diabetes
mellitus for 12/32 (37.5%) sensors, a history of AMI for 1/32 (3.1%) sensors, kidney failure
for 3/32 (9.4%) sensors, and COPD for 4/32 (12.5%) sensors. We did not report statistically
significant differences between patients with monofocal and multifocal tumors (Figure
2d) or in cases of arterial hypertension, liver disorders, or dyslipidemic conditions (mean
p > 0.05)
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Figure A1. Measurement patterns identified (baseline characteristics).
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