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Citation: Švecová, M.; Dubayová, K.;
Birková, A.; Urdzík, P.; Mareková, M.
Non-Invasive Endometrial Cancer
Screening through Urinary
Fluorescent Metabolome Profile
Monitoring and Machine Learning
Algorithms. Cancers 2024,16, 3155.
https://doi.org/10.3390/
cancers16183155
Academic Editors: Arianna
Mencattini and Helder C. R. De
Oliveira
Received: 30 July 2024
Revised: 8 September 2024
Accepted: 12 September 2024
Published: 14 September 2024
Copyright: © 2024 by the authors.
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4.0/).
cancers
Article
Non-Invasive Endometrial Cancer Screening through Urinary
Fluorescent Metabolome Profile Monitoring and Machine
Learning Algorithms
Monika Švecová1, Katarína Dubayová1, Anna Birková1, Peter Urdzík2and Mária Mareková1,*
1Department of Medical and Clinical Biochemistry, Faculty of Medicine, Pavol Jozef Šafárik University in
Košice, Tr. SNP, 104001 Košice, Slovakia; monika.svecova@upjs.sk (M.Š.); katarina.dubayova@upjs.sk (K.D.);
anna.birkova@upjs.sk (A.B.)
2Department of Gynaecology and Obstetrics, Faculty of Medicine, Pavol Jozef Šafárik University in Košice,
Tr. SNP, 104001 Košice, Slovakia; peter.urdzik@upjs.sk
*Correspondence: maria.marekova@upjs.sk
Simple Summary: The incidence of endometrial cancer is increasing, creating a need for fast and
efficient diagnostic methods. This study explores a new, non-invasive approach using urinary fluores-
cence spectroscopy to detect endometrial cancer. By analyzing morning urine samples and utilizing
advanced machine learning techniques, we identified prospective spectral markers that differentiate
between control, benign, and malignant gynecological patients. Our findings indicate good sensi-
tivity and specificity, with high AUC from machine learning models, suggesting this method could
significantly improve early cancer detection. This approach is easier and more affordable, especially
in resource-limited settings. It has the potential to change the way endometrial cancer is diagnosed,
offering a simpler and more accessible option for patients.
Abstract: Endometrial cancer is becoming increasingly common, highlighting the need for improved
diagnostic methods that are both effective and non-invasive. This study investigates the use of
urinary fluorescence spectroscopy as a potential diagnostic tool for endometrial cancer. Urine
samples were collected from endometrial cancer patients (n= 77), patients with benign uterine
tumors (n= 23), and control gynecological patients attending regular checkups or follow-ups (
n= 96
).
These samples were analyzed using synchronous fluorescence spectroscopy to measure the total
fluorescent metabolome profile, and specific fluorescence ratios were created to differentiate between
control, benign, and malignant samples. These spectral markers demonstrated potential clinical
applicability with AUC as high as 80%. Partial Least Squares Discriminant Analysis (PLS-DA)
was employed to reduce data dimensionality and enhance class separation. Additionally, machine
learning models, including Random Forest (RF), Logistic Regression (LR), Support Vector Machine
(SVM), and Stochastic Gradient Descent (SGD), were utilized to distinguish between controls and
endometrial cancer patients. PLS-DA achieved an overall accuracy of 79% and an AUC of 90%. These
promising results indicate that urinary fluorescence spectroscopy, combined with advanced machine
learning models, has the potential to revolutionize endometrial cancer diagnostics, offering a rapid,
accurate, and non-invasive alternative to current methods.
Keywords: endometrial cancer; autofluorescence; urine; cancer detection; machine learning
1. Introduction
Endometrial cancer (EC) is the sixth most common cancer in women worldwide,
significantly impacting women’s health [
1
]. The incidence of EC is increasing due to factors
such as advanced age and rising obesity [
2
]. Early-stage EC is highly treatable, with a
five-year survival rate around 80–90% [
3
]. However, advanced or recurrent EC cases have
a poor prognosis, emphasizing the need for improved early detection methods [
4
]. Current
Cancers 2024,16, 3155. https://doi.org/10.3390/cancers16183155 https://www.mdpi.com/journal/cancers
Cancers 2024,16, 3155 2 of 20
diagnostic methods for EC, including transvaginal ultrasound (TVS), hysteroscopy, and
endometrial biopsy, are effective but often very uncomfortable, sometimes painful, and not
suitable for routine screenings [5,6].
TVS is commonly used for initial assessments but may lead to further invasive proce-
dures if the results are inconclusive. Hysteroscopy, though providing direct visualization of
the endometrial cavity, requires anesthesia and can have many complications. Endometrial
biopsy, the gold standard for diagnosis, involves tissue sampling that can be painful and
carries several risks, including vasovagal episodes and possible uterine perforation [7].
Given these limitations, there is increasing interest in non-invasive diagnostic techniques
for EC [
8
]. One promising area of research is blood-based biomarkers, which offer a non-
invasive way to detect cancer early. Furthermore, circulating miRNAs and circular RNAs in
body fluids, such as serum and plasma, have gained attention due to their stability and diag-
nostic potential. These molecules not only provide molecular insights into cancer progression
but also support fertility-sparing treatments in EC patients [
9
,
10
]. Researchers are actively
working on identifying and validating new panels of serum metabolites using advanced tech-
niques such as mass spectrometry, enabling faster profiling and showing significant potential
to enhance diagnostic accuracy over traditional markers such as CA-125. [11].
In addition to blood-based diagnostics, urine-based diagnostics have emerged as an
attractive, non-invasive alternative due to the ease of sample collection and the ability of
urine to reflect systemic metabolic changes. Urine contains various biomolecules, including
metabolites, proteins, and DNA, which can serve as biomarkers for EC [
12
]. Recent studies
have explored urine-derived miRNAs for their potential in non-invasive screening, evaluating
them as diagnostic biomarkers for EC and ovarian cancer, with promising results for early
detection [
13
,
14
]. Metabolomic profiling of urine using techniques such as ultra-performance
liquid chromatography mass spectrometry (UPLC-MS) has successfully identified metabolic
patterns that distinguish between EC patients and healthy individuals. These metabolic
profiles reveal changes in amino acid metabolism and help develop diagnostic models. [
15
,
16
].
In addition to metabolomic profiling, techniques such as attenuated total reflection-
Fourier transform infrared (ATR-FTIR) spectroscopy have demonstrated high sensitivity
and specificity in distinguishing EC patients from healthy individuals, making it a valuable
option for screening high-risk populations [
17
]. Studies utilizing infrared spectroscopy of
urine, combined with machine learning, have accurately detected EC by analyzing molecular
‘fingerprints’ present in urine samples [
18
]. Other vibrational spectroscopic methods, such as
mid-infrared absorption and Raman spectroscopy, also offer rapid and cost-effective ways to
enhance diagnostic accuracy through the analysis of these molecular signatures [
19
]. These
non-invasive approaches, particularly when combined with machine learning algorithms,
present a promising and patient-friendly method for EC detection, complementing traditional
invasive diagnostic techniques and improving early detection strategies.
Many metabolites possess native fluorescent properties and are present in biological
samples such as tissue, blood, and urine [
20
]. The metabolism of cancerous tissues dif-
fers from that of healthy ones, affecting the composition of natural fluorophores in body
fluids [
21
]. Fluorescence spectroscopy, especially in 3D form, is gaining attention as a
non-invasive diagnostic tool for early cancer detection due to its sensitivity and specificity.
This technique leverages the autofluorescence properties of endogenous fluorophores in
urine, allowing for the detection of cancer-related alterations in a non-destructive, rapid,
and cost-effective manner [
22
]. Compared to traditional diagnostic methods such as mass
spectrometry (MS) and nuclear magnetic resonance (NMR), 3D fluorescence spectroscopy
is simpler and more affordable, making it well-suited for routine clinical use [
23
]. It en-
ables the simultaneous detection of multiple metabolites and the monitoring of various
metabolic changes without destroying samples. Comparative fluorescence analysis identi-
fies changes in the composition of the analyzed sample even without defining the exact
metabolome makeup. Recent advancements in ML have further enhanced the capabilities
of 3D fluorescence spectroscopy, improving data analysis and interpretation [24–26].
Cancers 2024,16, 3155 3 of 20
Normal urine contains many native fluorophores, including metabolites of tryptophan,
riboflavin, catecholamines, and porphyrins, resulting in strong endogenous fluorescence
predominantly in the UV part of the spectrum (due to indole derivatives) [
27
]. Changes in the
expression of these endogenous fluorophores can indicate metabolic disorders related to dis-
eases, including cancer. The use of urinary fluorescence analysis offers the potential to detect
urinary metabolites associated with neoplastic processes, potentially providing new directions
in the search for predictive and prognostic markers. Previous studies have confirmed the
high efficiency of fluorescence spectroscopy as a diagnostic tool for several different cancers,
including ovarian, bladder, liver, and malignant melanoma, among others [28–31].
To the best of our knowledge, the application of urine fluorescence spectroscopy
for screening patients with EC and distinguishing them from benign uterine tumors and
healthy controls has not been reported. This study pioneers the combination of urinary
fluorescence metabolite profiling with machine learning algorithms for early cancer detec-
tion, offering a novel approach in cancer diagnostics. By analyzing the unique fluorescence
profiles of biofluorophores, this method seeks to provide a reliable, rapid, and patient-
friendly diagnostic screening approach that could significantly enhance early detection and
improve patient outcomes.
By utilizing the autofluorescence properties of urinary biofluorophores, this technique
allows for the detection of cancer-related alterations without the need for invasive proce-
dures. Additionally, the integration of machine learning algorithms improves diagnostic
accuracy and opens possibilities for automation, especially in resource-limited settings.
The key contributions of this study are as follows:
•Development of a novel non-invasive diagnostic method
•Improvement in detection sensitivity
•Cost-effectiveness and practicality
•Potential for automation
•Foundation for further research
2. Materials and Methods
2.1. Study Design
The study group consisted of patients diagnosed with EC (n= 77) and benign tumors
of the uterus (n= 23), along with a control group of gynecological patients (n= 96). The
control group included 36 women that underwent preventive gynecological examinations
and 60 patients who attended follow-up appointments after treatment for gynecological
inflammatory diseases. The most frequent diagnoses were inflammatory disease of the
cervix uteri (ICD10 code N72, n= 24) and other inflammation of the vagina and vulva (N76,
n= 23). Other diagnoses in this group included dysplasia of the cervix uteri (N87, n= 6),
female infertility (N97, n= 3), salpingitis and oophoritis (N70, n= 2), and inflammatory
disease of the uterus excluding the cervix (N71, n= 2).
The patients with diagnosed EC included 72 with endometrioid carcinoma, 3 with
serous endometrial intraepithelial carcinoma, and 2 with mixed EC. Most patients were in
the early stages of the disease (Stages I and II, n= 53) and had low-grade tumors (n= 44).
The staging and grading of all gynecological patients (patients with EC and benign patients)
was defined based on histopathological examination after the surgery, in accordance with
the International Federation of Gynaecology and Obstetrics (FIGO) staging system for
endometrial carcinomas and uterine sarcomas [
32
,
33
]. This system uses key pathological
information such as the extent of myometrial invasion, involvement of the cervical stroma,
spread to regional lymph nodes, and the presence of distant metastases to assign specific
stages, thereby guiding prognosis and treatment planning.
Endometrial polyps (n= 13) and ovarian endometrial cysts (n= 7) were the most
prevalent types of endometrial lesions in the benign group. Other benign diseases included
adenomysosis, leiomyoma, and hematocolpos.
Patients were recruited during their hospitalization or examination at the Gynaecology
and Obstetrics Clinic of Louis Pasteur University Hospital in Košice. Written informed consent
Cancers 2024,16, 3155 4 of 20
was obtained from all participants prior to sample collection. The clinical investigations were
conducted in accordance with the Declaration of Helsinki, and this study was approved by
the Ethics Committee of the Pavol Jozef Šafárik University in Košice, Faculty of Medicine
(2024/EK/01011). Detailed age summaries of all groups can be found in Table 1.
Table 1. Patient age distribution summary.
NAge in Years
Minimum Maximum Mean ±SD
Controls 96 18 65 36.4 ±11.66
Benign patients 23 21 46 32.7 ±6.25
EC patients 77 30 80 60.9 ±11.5
EC Stage I 35 30 77 55.5 ±12.3
EC Stage II 19 46 72 60.1 ±9.5
EC Stage III 10 50 77 70.1 ±8.6
EC Stage IV 9 51 80 64.0 ±10.1
EC Stage V 4 58 65 61.5 ±4.9
2.2. Urine Sample Collection, Processing, and Analysis
Urine samples were collected from gynecological patients immediately following
their admission or arrival to the hospital before taking any medication or undergoing any
procedures. Control group participants provided first morning urine samples after fasting
for at least 8 h before sample collection. All urine samples were collected under standard-
ized conditions to ensure consistency and minimize variability. Key urine parameters,
including leukocytes, nitrite, pH, specific gravity, protein, glucose, ketones, urobilinogen,
bilirubin, and blood, were evaluated semi-quantitatively using a 10-parameter urine strip
test (Dekaphan Leuco, Erba Lachema, Brno, Czech Republic).
To ensure sample integrity, samples were immediately centrifuged at 5000 rpm for
10 min
at room temperature (Centrifuge Boeco S8, Boeco Germany, Hamburg, Germany)
after collection to remove cellular debris. The supernatant was aliquoted into microtubes
and stored at
−
80
◦
C until further analysis. No additional pre-treatment was applied before
the dilution and subsequent fluorescence measurements. Before measurement, the urine
samples were thawed, and 1 mL of each sample was diluted with deionized water in a
1:3 ratio in a geometric series from the undiluted state to a 1000-fold dilution, ensuring a
consistent dilution process for all samples [34].
2.3. Instrumentation and Spectral Acquisition
The autofluorescence of urine samples was measured at room temperature using a
luminescence spectrophotometer PerkinElmer LS55 (PerkinElmer, Waltham, MA, USA) in a
1 cm quartz cuvette (Helmut Fischer, Sindelfingen, Germany). Each sample was measured
using synchronous spectra with a
∆λ
of 30 nm across a range of 250–550 nm, with a step size
of 0.5 nm, excitation/emission slits set to 5/5 nm, and a scan speed of 1200 nm/min. The
measurements were performed, and urine total fluorescent metabolome profiles (uTFMP)
were constructed as previously described [
35
]. Birkováet al. previously classified seven
spectral zones of the uTFMP based on related fluorophores [
36
]. To ensure consistency in
data processing, uTFMPs were standardized using the same parameters for all samples to
facilitate comparison between control, benign, and malignant cases.
2.4. Statistical and Computational Analysis
For statistical and data analysis, GraphPad Prism 8.0.1 (Boston, MA, USA) and
Python (version 3.12) were used. Initially, the normality of the data were assessed us-
ing the
Shapiro–Wilk
test. Given that the data exhibited a non-normal distribution, the
Cancers 2024,16, 3155 5 of 20
Kruskal–Wallis
test was used for multiple comparisons, specifically applying Dunn’s mul-
tiple comparisons test with Bonferroni correction to control the family-wise error rate [
37
].
For zone comparisons, Tukey’s multiple comparisons test was applied following one-way
ANOVA, with adjustments made for Type I error using the family-wise error rate during
pairwise comparisons [
38
]. Statistical significance was assigned for p-values < 0.05, and
values of fluorescence intensity were expressed as median
±
interquartile range to account
for the non-parametric nature of the data.
Following preprocessing, features such as peaks and fluorescence ratios were extracted
from the spectra. The Z4a/Z5 and Z6/Z7 ratios were selected based on the progression of
the spectra observed in the mean profiles of the uTFMP. To assess the diagnostic potential
of the fluorescence ratios, ROC curves were generated using the Wilson/Brown method.
This method is particularly effective for calculating confidence intervals in classification
models, especially with smaller sample sizes. By applying the Wilson/Brown method, we
ensured that the ROC curves more accurately represented the diagnostic performance of
the fluorescence data. This approach uses a binomial distribution to calculate confidence
intervals, providing more reliable results than traditional methods [39].
The raw fluorescence data were first subjected to smoothing using the Savitzky-Golay
filter with a window length of 11 and a polynomial order of 3. This filtering technique was
applied to reduce noise while preserving the overall shape and features of the fluorescence
spectra, ensuring that any minor fluctuations were smoothed out without distorting the
significant peaks. The smoothed data were then used for all subsequent analyses.
To further process the data, uTFMP was transposed by each zone, and the presence
of peaks was classified based on the increase in fluorescence units. Specifically, a value of
2 (peak)
was assigned if there was an increase of at least 15 units of fluorescence between
consecutive measurements within the zone, a value of 1 (slight inclination) for an increase
of 5–10 units, and a value of 0 if the increase was less than 5 units.
The dataset was divided into training and testing sets using a 70:30 split ratio. The
split was stratified based on the target variable to maintain the proportion of control and
malignant samples in both the training and test sets, ensuring that the model was trained
and evaluated on representative samples.
Partial Least Squares Discriminant Analysis (PLS-DA) was used to reduce data dimen-
sionality while maximizing the separation between control, benign, and malignant urine
samples. [
40
]. Four machine learning models: Random Forest (RF), Logistic Regression (LR),
Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) were employed to
differentiate between controls and patients with EC. These models were implemented and
validated using the Scikit-learn library [
41
]. Each model was built in accordance with the rec-
ommendations of the IFCC Working Group on Machine Learning in Laboratory Medicine [
42
].
To further account for class imbalance and avoid bias, we employed stratified 10-fold
cross-validation, ensuring that each fold contained the same class distribution. Each model
was also run for 100 repetitions to assess robustness and generalizability. Hyperparameter
tuning was performed to improve model performance. For RF, we evaluated the number
of trees (100, 150, 200) and tree depths (5, 10, 15), with the optimal configuration being
set at 150 trees with a maximum depth of 10. For SVM, the regularization parameter (C)
was tested with values of 0.01, 0.1, and 1 and performed best at 1. LR was run with a
maximum of 10.00 iterations. For SGD, the loss function was set to logistic regression, and
we tested alpha values of 0.000001, 0.0001, and 0.01, with learning rates set to optimal and
adaptive. SGD achieved the best performance with an alpha of 0.0001 and the optimal
learning rate. These optimal hyperparameters were selected based on model performance
during cross-validation to ensure the best predictive accuracy and robustness.
The performance of each model was evaluated based on metrics such as accuracy,
sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV),
positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the curve
(AUC), providing a comprehensive assessment of their diagnostic potential. In addition,
confusion matrices were generated for each model to visualize and compare the true
Cancers 2024,16, 3155 6 of 20
positive, true negative, false positive, and false negative rates, offering further insights into
classification performance across different data representations.
3. Results
3.1. Semiquantitative Strip Analysis of Key Urine Parameters
The average pH in the gynecological control group was 6.06. In this group, 5 patients
were positive for ketones with a low concentration of 1.5 mmol/L. Among the urine
samples, 4 tested positive for leukocytes and 8 for blood, which can indicate inflammation
or mild infection, common in gynecological conditions. Additionally, there were occasional
positive results for proteins (36 samples) and bilirubin (7 samples). All protein-positive
samples were still within the upper range of a physiological state at 300 mg/L. These
results correspond to these patients having inflammatory gynecological conditions.
In the benign group, the average pH was 6.43, slightly higher, but still within the
normal range. Eight patients were positive for ketones with varying concentrations between
1.5–5 mmol/L, possibly related to metabolic changes due to benign conditions such as
fibroids or cysts. Among the urine samples in this group, 8 tested positive for leukocytes, 7
for urobilinogen, 1 for blood, and 3 for hemoglobin. Additionally, there were occasional
positive results for proteins (11 samples) and bilirubin (12 samples). Positive bilirubin
results might indicate some degree of hemolysis or anemia, which can occasionally be
associated with benign gynecological conditions [43].
In the EC group with malignant cases, the average pH was 5.69, indicating a more
acidic environment, possibly associated with carcinogenesis and cellular breakdown. Sev-
eral parameters showed positive results in the urinalysis: 61 samples positive for leukocytes,
28 for proteins, 3 for glucose, 26 for ketones, 26 for urobilinogen, 38 for bilirubin, and 32
for blood components, including 16 specifically positive for hemoglobin. High leukocyte
counts (500 mg/dL) indicate significant inflammation or infection. Elevated proteins and
the presence of blood components suggest kidney involvement or significant tissue break-
down. The presence of glucose and elevated ketones are more alarming and could indicate
metabolic stress or cancer-related cachexia. Figure 1illustrates the number of patients
positive for each urine parameter across the control, benign, and EC groups.
Cancers 2024, 16, x FOR PEER REVIEW 7 of 22
Figure 1. Semiquantitive strip analysis comparison of positive urine parameters.
3.2. Fluorescent Profiles and Zones
The mean uTFMP curves showed several spectral characteristics distinguishing the
different groups (Figure 2). Zones 1a and 1b, characteristic of indole derivatives and cate-
cholamine metabolites (250–300 nm), exhibited the highest fluorescence intensity in the
control group and the lowest in the malignant group. This indicates a higher presence or
more active metabolism of these compounds in the control group compared to the malig-
nant group. Zone 2 (300–325 nm), containing mainly 5-HIAA (5-hydroxyindoleacetic
acid), clearly differentiated the control group from the benign and malignant patients.
Zone 3 (325–345 nm) showed a distinct peak for 3-HAA (3-hydroxyanthranilic acid) fluo-
rescence, which was present only in malignant samples. This indicates elevated levels of
3-HAA in malignant samples, confirming the increased catabolism of tryptophan. Zone 4
exhibits fluorescence for various metabolites with similar fluorescent characteristics. Since
individual fluorophores have not been identified in this study, we will not specify them.
In zones 4a and 4b (345–380 nm), a red shift was present in malignant patients in compar-
ison with the control group. Zone 5 (380–410 nm), characteristic of xanthurenic acid, did
not show notable differences among the groups. In Zone 6 (410–450 nm), the fluorescence
of xanthopterin and kynurenine was not elevated in benign or malignant samples and had
a lower fluorescence intensity overall. Finally, zone 7 (450–500 nm), containing flavins
such as FAD and vitamin B
2
, was slightly elevated in malignant samples.
Figure 1. Semiquantitive strip analysis comparison of positive urine parameters.
Cancers 2024,16, 3155 7 of 20
3.2. Fluorescent Profiles and Zones
The mean uTFMP curves showed several spectral characteristics distinguishing
the different groups (Figure 2). Zones 1a and 1b, characteristic of indole derivatives
and catecholamine metabolites (250–300 nm), exhibited the highest fluorescence in-
tensity in the control group and the lowest in the malignant group. This indicates a
higher presence or more active metabolism of these compounds in the control group
compared to the malignant group. Zone 2 (300–325 nm), containing mainly 5-HIAA
(5-hydroxyindoleacetic acid), clearly differentiated the control group from the benign
and malignant patients. Zone 3 (325–345 nm) showed a distinct peak for 3-HAA (3-
hydroxyanthranilic acid) fluorescence, which was present only in malignant samples.
This indicates elevated levels of 3-HAA in malignant samples, confirming the increased
catabolism of tryptophan. Zone 4 exhibits fluorescence for various metabolites with
similar fluorescent characteristics. Since individual fluorophores have not been iden-
tified in this study, we will not specify them. In zones 4a and 4b (345–380 nm), a red
shift was present in malignant patients in comparison with the control group. Zone 5
(380–410 nm), characteristic of xanthurenic acid, did not show notable differences among
the groups. In Zone 6 (
410–450 nm
), the fluorescence of xanthopterin and kynurenine
was not elevated in benign or malignant samples and had a lower fluorescence intensity
overall. Finally, zone 7 (450–500 nm), containing flavins such as FAD and vitamin B
2
,
was slightly elevated in malignant samples.
Cancers 2024, 16, x FOR PEER REVIEW 8 of 22
Figure 2. Urinary total fluorescent metabolome profiles (uTFMP) divided into fluorescent zones.
Figure 3 illustrates the fluorescent urinary zones and their median ± interquartile
range (IQR), highlighting statistical significance between the zones and compared groups.
Specifically, zone 1a and zone 1b showed high statistical significance (p < 0.001), indicating
strong statistical differences. Zone 2 exhibited moderate significance (p < 0.05), differenti-
ating the controls from the other groups. Zone 6 demonstrated very strong significance (p
< 0.0001), underscoring the marked difference between the groups in this spectral zone.
Z1a Z1b Z2 Z3 Z4a Z4b Z5 Z6 Z7
0
500
1000
1500
Malignant
Benign
Control
✱✱✱
✱✱✱✱
✱✱✱ ✱
Figure 3. Fluorescent urinary zones. Values are expressed as median ± interquartile range. ****
indicates p < 0.0001, *** indicates p < 0.001, * p < 0.05.
3.3. Fluorescent Spectral Markers and Their Clinical Utility
It was previously established that fluorescence ratios provided greater accuracy than
comparing specific selected wavelengths [34]. The Z4a/Z5 ratio was found to be signifi-
cantly higher in both malignant and benign samples compared to the control group of
patients (Figure 4A). This ratio effectively differentiated both EC and benign samples from
Figure 2. Urinary total fluorescent metabolome profiles (uTFMP) divided into fluorescent zones.
Figure 3illustrates the fluorescent urinary zones and their median
±
interquar-
tile range (IQR), highlighting statistical significance between the zones and compared
groups. Specifically, zone 1a and zone 1b showed high statistical significance (p< 0.001),
indicating strong statistical differences. Zone 2 exhibited moderate significance (
p< 0.05
),
differentiating the controls from the other groups. Zone 6 demonstrated very strong
significance (
p< 0.0001
), underscoring the marked difference between the groups in this
spectral zone.
Cancers 2024,16, 3155 8 of 20
Cancers 2024, 16, x FOR PEER REVIEW 8 of 22
Figure 2. Urinary total fluorescent metabolome profiles (uTFMP) divided into fluorescent zones.
Figure 3 illustrates the fluorescent urinary zones and their median ± interquartile
range (IQR), highlighting statistical significance between the zones and compared groups.
Specifically, zone 1a and zone 1b showed high statistical significance (p < 0.001), indicating
strong statistical differences. Zone 2 exhibited moderate significance (p < 0.05), differenti-
ating the controls from the other groups. Zone 6 demonstrated very strong significance (p
< 0.0001), underscoring the marked difference between the groups in this spectral zone.
Z1a Z1b Z2 Z3 Z4a Z4b Z5 Z6 Z7
0
500
1000
1500
Malignant
Benign
Control
✱✱✱
✱✱✱✱
✱✱✱ ✱
Figure 3. Fluorescent urinary zones. Values are expressed as median ± interquartile range. ****
indicates p < 0.0001, *** indicates p < 0.001, * p < 0.05.
3.3. Fluorescent Spectral Markers and Their Clinical Utility
It was previously established that fluorescence ratios provided greater accuracy than
comparing specific selected wavelengths [34]. The Z4a/Z5 ratio was found to be signifi-
cantly higher in both malignant and benign samples compared to the control group of
patients (Figure 4A). This ratio effectively differentiated both EC and benign samples from
Figure 3. Fluorescent urinary zones. Values are expressed as median
±
interquartile range.
**** indicates p< 0.0001, *** indicates p< 0.001, * p< 0.05.
3.3. Fluorescent Spectral Markers and Their Clinical Utility
It was previously established that fluorescence ratios provided greater accuracy than
comparing specific selected wavelengths [
34
]. The Z4a/Z5 ratio was found to be signif-
icantly higher in both malignant and benign samples compared to the control group of
patients (Figure 4A). This ratio effectively differentiated both EC and benign samples from
controls (p< 0.001). In contrast, the Z6/Z7 ratio was the lowest in malignant samples,
followed by benign samples, and then control samples (Figure 4B). This ratio distinguished
malignant samples from control samples with very strong statistical significance (p< 0.0001)
and also differentiated benign samples from control samples (p< 0.05). The Z4a/Z5 and
Z6/Z7 ratios define prospective fluorescent spectral markers that help discriminate the EC
and benign group of patients from controls.
Cancers 2024, 16, x FOR PEER REVIEW 9 of 22
controls (p < 0.001). In contrast, the Z6/Z7 ratio was the lowest in malignant samples, fol-
lowed by benign samples, and then control samples (Figure 4B). This ratio distinguished
malignant samples from control samples with very strong statistical significance (p <
0.0001) and also differentiated benign samples from control samples (p < 0.05). The Z4a/Z5
and Z6/Z7 ratios define prospective fluorescent spectral markers that help discriminate
the EC and benign group of patients from controls.
Figure 4. Fluorescent ratios (A) Ratio Z4a/Z5. (B) Ratio Z6/Z7. Values are expressed as median ±
interquartile range. **** indicates p < 0.0001, *** indicates p < 0.001, * p < 0.05.
To evaluate the potential clinical utility of these ratios in distinguishing between the
groups, ROC curves were ploed (Figure 5). The area under the ROC curve (AUC) for the
Z4a/Z5 ratio was 74.91% (STD: 0.04474, 95% CI: 66.14–83.68%, p < 0.0001) when comparing
controls to benign samples and 66.44% (STD: 0.04184, 95% CI: 58.24–74.64%, p < 0.0001)
when comparing controls to malignant samples.
For the Z6/Z7 ratio, the AUC was 68.43% (STD: 0.06395, 95% CI: 55.90–80.97%, p <
0.001) for controls versus benign samples and 80.34% (STD: 0.03478, 95% CI: 73.53–87.16%,
p < 0.0001) for controls versus malignant samples. The ROC curves and corresponding
AUC values demonstrate that both fluorescence ratios have potential clinical applicability
in differentiating between malignant, benign, and control samples. The AUC values, par-
ticularly for the Z6/Z7 ratio in malignant samples, indicate a high level of diagnostic ac-
curacy, suggesting these ratios could be useful biomarkers in clinical seings.
Figure 5. Receiver operating characteristic curves (A) Ratio Z4a/Z5 (B) Ratio Z6/Z7.
Figure 4. Fluorescent ratios (A) Ratio Z4a/Z5. (B) Ratio Z6/Z7. Values are expressed as
median ±interquartile range. **** indicates p< 0.0001, *** indicates p< 0.001, * p< 0.05.
To evaluate the potential clinical utility of these ratios in distinguishing between the
groups, ROC curves were plotted (Figure 5). The area under the ROC curve (AUC) for the
Z4a/Z5 ratio was 74.91% (STD: 0.04474, 95% CI: 66.14–83.68%, p< 0.0001) when comparing
controls to benign samples and 66.44% (STD: 0.04184, 95% CI: 58.24–74.64%, p< 0.0001)
when comparing controls to malignant samples.
Cancers 2024,16, 3155 9 of 20
Cancers 2024, 16, x FOR PEER REVIEW 9 of 22
controls (p < 0.001). In contrast, the Z6/Z7 ratio was the lowest in malignant samples, fol-
lowed by benign samples, and then control samples (Figure 4B). This ratio distinguished
malignant samples from control samples with very strong statistical significance (p <
0.0001) and also differentiated benign samples from control samples (p < 0.05). The Z4a/Z5
and Z6/Z7 ratios define prospective fluorescent spectral markers that help discriminate
the EC and benign group of patients from controls.
Figure 4. Fluorescent ratios (A) Ratio Z4a/Z5. (B) Ratio Z6/Z7. Values are expressed as median ±
interquartile range. **** indicates p < 0.0001, *** indicates p < 0.001, * p < 0.05.
To evaluate the potential clinical utility of these ratios in distinguishing between the
groups, ROC curves were ploed (Figure 5). The area under the ROC curve (AUC) for the
Z4a/Z5 ratio was 74.91% (STD: 0.04474, 95% CI: 66.14–83.68%, p < 0.0001) when comparing
controls to benign samples and 66.44% (STD: 0.04184, 95% CI: 58.24–74.64%, p < 0.0001)
when comparing controls to malignant samples.
For the Z6/Z7 ratio, the AUC was 68.43% (STD: 0.06395, 95% CI: 55.90–80.97%, p <
0.001) for controls versus benign samples and 80.34% (STD: 0.03478, 95% CI: 73.53–87.16%,
p < 0.0001) for controls versus malignant samples. The ROC curves and corresponding
AUC values demonstrate that both fluorescence ratios have potential clinical applicability
in differentiating between malignant, benign, and control samples. The AUC values, par-
ticularly for the Z6/Z7 ratio in malignant samples, indicate a high level of diagnostic ac-
curacy, suggesting these ratios could be useful biomarkers in clinical seings.
Figure 5. Receiver operating characteristic curves (A) Ratio Z4a/Z5 (B) Ratio Z6/Z7.
Figure 5. Receiver operating characteristic curves (A) Ratio Z4a/Z5 (B) Ratio Z6/Z7.
For the Z6/Z7 ratio, the AUC was 68.43% (STD: 0.06395, 95% CI: 55.90–80.97%,
p< 0.001
)
for controls versus benign samples and 80.34% (STD: 0.03478, 95%
CI: 73.53–87.16%
,
p< 0.0001)
for controls versus malignant samples. The ROC curves and corresponding AUC values
demonstrate that both fluorescence ratios have potential clinical applicability in differentiat-
ing between malignant, benign, and control samples. The AUC values, particularly for the
Z6/Z7 ratio in malignant samples, indicate a high level of diagnostic accuracy, suggesting
these ratios could be useful biomarkers in clinical settings.
3.4. Classification and Machine Learning Models
The Partial Least Squares Discriminant Analysis (PLS-DA) was performed on the
transposed data of the urinary total fluorescence metabolite profile (uTFMP) and created
fluorescent ratios (Figure 6).
The R
2
value of 0.824 indicates that the model explains about 82.4% of the vari-
ance in the response variable, which signifies a good fit. Additionally, the Q
2
value of
0.823 demonstrates
that the model has strong predictive power and is likely to perform
well on new, unseen data. This indicates that the model generalizes well beyond the
training data.
The analysis of urine spectra for EC versus controls using PLS-DA demonstrated a
high level of accuracy and effectiveness in differentiating these groups. For the PLS-DA
model comparing control and malignant samples, the following performance metrics were
obtained: For control samples, the precision was 0.91, recall was 0.70, and F1-score was
0.79, with support of 30 samples. For malignant samples, the precision was 0.69, recall was
0.91, and F1-score was 0.78, with a support of 22 samples. Overall, the model achieved
an accuracy of 0.79, with a macro average precision of 0.80, macro average recall of 0.80,
macro average F1-score of 0.79, weighted average precision of 0.82, weighted average recall
of 0.79, and weighted average F1-score of 0.79. The AUC for the ROC analysis was 0.90,
indicating excellent discriminatory power between the control and malignant groups.
For the model comparing control and benign samples, the performance metrics were
as follows: precision of 0.79, recall of 0.96, and F1-score of 0.87 for control samples (support:
28). For benign samples, the precision was 0.50, recall was 0.12, and F1-score was 0.20
(support: 8). The overall accuracy was 0.78, with a macro average precision of 0.65, recall
of 0.54, and F1-score of 0.54, as well as a weighted average precision of 0.73, recall of
0.78, and F1-score of 0.72. The AUC for the ROC analysis was 0.68, indicating moderate
discriminatory power. The PLS-DA scatter plots for the training and test datasets showed
some overlap between control (green) and benign (blue) samples, highlighting the challenge
in distinguishing benign samples from controls.
Cancers 2024,16, 3155 10 of 20
Cancers 2024, 16, x FOR PEER REVIEW 11 of 22
Figure 6. Partial Least Squares Discriminant Analysis (PLS-DA) (A) Train set between controls and
malignant samples; (B) Test set between controls and malignant samples; (C) Train set between con-
trols and benign samples; (D) Test set between controls and malignant samples; (E) ROC curve be-
tween controls and malignant samples; (F) ROC curve between controls and benign samples.
To evaluate the diagnostic potential of this method, several classification machine
learning models were constructed to distinguish EC from the control group of gynecolog-
ical patients using two types of data representations: transposed fluorescent zones and
the whole urine profile (uTFMP).
Figure 6. Partial Least Squares Discriminant Analysis (PLS-DA) (A) Train set between controls and
malignant samples; (B) Test set between controls and malignant samples; (C) Train set between
controls and benign samples; (D) Test set between controls and malignant samples; (E) ROC curve
between controls and malignant samples; (F) ROC curve between controls and benign samples.
To evaluate the diagnostic potential of this method, several classification machine
learning models were constructed to distinguish EC from the control group of gynecological
patients using two types of data representations: transposed fluorescent zones and the
whole urine profile (uTFMP).
Cancers 2024,16, 3155 11 of 20
For the transposed fluorescent zones, the highest performance was demonstrated by
the Support Vector Machine (SVM), with an accuracy of 0.77 and an AUC of 0.87. Sensitivity
(0.73) and specificity (0.79) were balanced by this model, yielding a positive predictive
value (PPV) of 0.74 and a negative predictive value (NPV) of 0.79. The positive likelihood
ratio (PLR) was 4.07, and the negative likelihood ratio (NLR) was 0.34. Random Forest (RF)
followed closely, with an accuracy of 0.78 and an AUC of 0.86. Logistic Regression (LR)
also performed well, with an accuracy of 0.79 and an AUC of 0.85. Slightly lower metrics
were observed for Stochastic Gradient Descent (SGD), with an accuracy of 0.78 and an AUC
of 0.81.
When using the uTFMP, LR and SVM outperformed the other models. LR achieved an
accuracy of 0.83 and an AUC of 0.90, while SVM also reached a high performance with an
accuracy of 0.81 and an AUC of 0.90. RF showed strong performance with an accuracy of
0.77 and an AUC of 0.85, and SGD performed well with an accuracy of 0.80 and an AUC
of 0.83.
Overall, LR and SVM were the top-performing algorithms across both data represen-
tations, demonstrating strong and consistent results. LR particularly excelled in terms of
both accuracy and AUC when using the uTFMP data. SVM also provided excellent per-
formance, particularly with the transposed fluorescent zone representation. RF and SGD,
while slightly behind LR and SVM, still offered robust results, indicating their potential
utility in this diagnostic context. Detailed performance metrics of the machine learning
models are summarized in Table 2.
Table 2. Performance of ML algorithms differentiating between EC and controls.
Zones Sensitivity Specificity PPV NPV PLR NLR Accuracy AUC
RF 0.71 0.83 0.77 0.79 4.88 0.35 0.78 0.86
SVM 0.73 0.79 0.74 0.79 4.07 0.34 0.77 0.87
LR 0.81 0.77 0.74 0.84 3.66 0.24 0.79 0.85
SGD 0.77 0.79 0.75 0.82 3.92 0.29 0.78 0.81
uTFMP Sensitivity Specificity PPV NPV PLR NLR Accuracy AUC
RF 0.67 0.86 0.78 0.77 4.17 0.43 0.77 0.85
SVM 0.78 0.83 0.79 0.83 5.30 0.26 0.81 0.90
LR 0.78 0.87 0.83 0.84 4.97 0.23 0.83 0.90
SGD 0.76 0.83 0.80 0.82 5.54 0.29 0.80 0.83
PPV, Positive predictive value; NPV, Negative predictive value; PLR, Positive likelihood ratio; NLR, Negative
likelihood ratio; AUC, Area under the curve; uTFMP, Urinary total fluorescent metabolome profiles.
The ROC curves illustrate the trade-offs between sensitivity and specificity for each
classifier (Figure 7). In the transposed fluorescent zone representation, SVM demon-
strated the highest AUC of 0.87, indicating the best overall performance in distinguishing
EC from the control group. RF followed with an AUC of 0.86, showing strong perfor-
mance as well. LR had an AUC of 0.85, and SGD had the lowest AUC of 0.81 among the
models evaluated. In the uTFMP representation, both LR and SVM achieved the highest
AUC of 0.90, showcasing their superior diagnostic potential. RF and SGD had AUCs of
0.85 and 0.83, respectively. These AUC values further corroborate the effectiveness of LR
and SVM in accurately identifying EC cases, making them the most reliable models in
this study.
Cancers 2024,16, 3155 12 of 20
Cancers 2024, 16, x FOR PEER REVIEW 13 of 22
Figure 7. ROC curves of built machine learning models (A) ML based on fluorescent zones and
spectral ratios (B) ML based overall urinary total fluorescent metabolome profile.
To further evaluate the diagnostic potential of the machine learning models devel-
oped to distinguish between EC patients and the control group, confusion matrices were
created for both the transposed fluorescent zones and spectral ratios as well as the overall
uTFMP, as shown in Figure 8.
In the transposed data models (Figure 8A), SVM slightly outperformed RF, with 16
false negatives and 20 false positives for SVM, compared to 22 false negatives and 21 false
positives for RF. Although both models demonstrated strong classification ability, SVM’s
lower misclassification rates indicated a subtle improvement in performance. Logistic Re-
gression (LR) also performed comparably, with 21 false negatives and 23 false positives.
However, Stochastic Gradient Descent (SGD) showed the poorest performance, with 26
false negatives and 26 false positives, indicating its lower sensitivity and specificity com-
pared to the other models in this data representation.
For the ML models built on the overall uTFMP data (Figure 8B), RF continued to
perform well, with 24 false negatives and 17 false positives, reflecting its ability to distin-
guish EC patients from controls effectively. SVM performed similarly, with 15 false nega-
tives and 17 false positives, maintaining consistent effectiveness across different data rep-
resentations. LR exhibited the best performance, with only 15 false negatives and 12 false
positives. However, SGD, while still demonstrating some diagnostic potential, continued
to underperform relative to the other models, with 22 false negatives and 15 false posi-
tives.
Figure 7. ROC curves of built machine learning models (A) ML based on fluorescent zones and
spectral ratios (B) ML based overall urinary total fluorescent metabolome profile.
To further evaluate the diagnostic potential of the machine learning models developed
to distinguish between EC patients and the control group, confusion matrices were created
for both the transposed fluorescent zones and spectral ratios as well as the overall uTFMP,
as shown in Figure 8.
Cancers 2024, 16, x FOR PEER REVIEW 14 of 22
Figure 8. Confusion matrices for machine learning models: (A) fluorescent zones and spectral ratios.
(B) overall urine total fluorescent metabolome profiles.
The key results:
• LR and SVM showed the best overall performance
• LR particularly excelled with the uTFMP data, while SVM performed well across
both data sets
• RF performed well but had higher misclassification rates
• SGD showed lower performance but still demonstrated diagnostic potential
4. Discussion
Early and accurate detection of endometrial cancer (EC) is crucial for improving pa-
tient outcomes, especially given the rising incidence globally. Traditional diagnostic meth-
ods, while effective, often involve invasive procedures and high costs, which can be a bar-
rier to early-stage screening. Therefore, there is a significant need for fast, reliable, and
affordable screening tools. Comparative 3D fluorescence analysis of urine offers a prom-
ising non-invasive alternative that could facilitate early detection and monitoring of EC
and potentially other cancers.
Urine, as a non-invasively obtained biological material, has great diagnostic poten-
tial. It contains a wealth of diagnostic information but is an analytically challenging bio-
logical system due to its concentration variability and the influence of diet on its compo-
sition. However, the problem of concentration variability was eliminated by the introduc-
tion of the urinary total fluorescent metabolome profile (uTFMP) [35].
This study aimed to observe and study the autofluorescence of urine samples as a
screening tool for differentiating patients with EC, or benign tumors of the uterus, from a
control group of gynecological patients. Collecting controls with the same age distribution
as the EC patient group is challenging because older women, who are more likely to de-
velop EC, often do not aend gynecological checkups. Furthermore, those who do aend
are rarely considered healthy, complicating the recruitment of age-matched controls.
The uTFMP was utilized to distinguish between these groups, revealing several dis-
tinct spectral characteristics. In zones 1a and 1b, indicative of indole derivatives and cate-
cholamine metabolites, the highest fluorescence intensity was observed in the control
group, while the malignant group showed the lowest intensity. This suggests a higher
presence or more active metabolism of these compounds in the control group, potentially
due to inflammatory processes such as bacterial infections in the urogenital tract. These
Figure 8. Confusion matrices for machine learning models: (A) fluorescent zones and spectral ratios.
(B) overall urine total fluorescent metabolome profiles.
In the transposed data models (Figure 8A), SVM slightly outperformed RF, with
16 false
negatives and 20 false positives for SVM, compared to 22 false negatives and 21 false
positives for RF. Although both models demonstrated strong classification ability, SVM’s
lower misclassification rates indicated a subtle improvement in performance. Logistic
Regression (LR) also performed comparably, with 21 false negatives and 23 false positives.
However, Stochastic Gradient Descent (SGD) showed the poorest performance, with 26 false
negatives and 26 false positives, indicating its lower sensitivity and specificity compared to
the other models in this data representation.
Cancers 2024,16, 3155 13 of 20
For the ML models built on the overall uTFMP data (Figure 8B), RF continued to per-
form well, with 24 false negatives and 17 false positives, reflecting its ability to distinguish
EC patients from controls effectively. SVM performed similarly, with 15 false negatives and
17 false positives, maintaining consistent effectiveness across different data representations.
LR exhibited the best performance, with only 15 false negatives and 12 false positives. How-
ever, SGD, while still demonstrating some diagnostic potential, continued to underperform
relative to the other models, with 22 false negatives and 15 false positives.
The key results:
•LR and SVM showed the best overall performance
•
LR particularly excelled with the uTFMP data, while SVM performed well across both
data sets
•RF performed well but had higher misclassification rates
•SGD showed lower performance but still demonstrated diagnostic potential
4. Discussion
Early and accurate detection of endometrial cancer (EC) is crucial for improving
patient outcomes, especially given the rising incidence globally. Traditional diagnostic
methods, while effective, often involve invasive procedures and high costs, which can be
a barrier to early-stage screening. Therefore, there is a significant need for fast, reliable,
and affordable screening tools. Comparative 3D fluorescence analysis of urine offers a
promising non-invasive alternative that could facilitate early detection and monitoring of
EC and potentially other cancers.
Urine, as a non-invasively obtained biological material, has great diagnostic potential.
It contains a wealth of diagnostic information but is an analytically challenging biological
system due to its concentration variability and the influence of diet on its composition.
However, the problem of concentration variability was eliminated by the introduction of
the urinary total fluorescent metabolome profile (uTFMP) [35].
This study aimed to observe and study the autofluorescence of urine samples as a
screening tool for differentiating patients with EC, or benign tumors of the uterus, from a
control group of gynecological patients. Collecting controls with the same age distribution
as the EC patient group is challenging because older women, who are more likely to develop
EC, often do not attend gynecological checkups. Furthermore, those who do attend are
rarely considered healthy, complicating the recruitment of age-matched controls.
The uTFMP was utilized to distinguish between these groups, revealing several dis-
tinct spectral characteristics. In zones 1a and 1b, indicative of indole derivatives and
catecholamine metabolites, the highest fluorescence intensity was observed in the control
group, while the malignant group showed the lowest intensity. This suggests a higher
presence or more active metabolism of these compounds in the control group, potentially
due to inflammatory processes such as bacterial infections in the urogenital tract. These
findings are consistent with previous research that links metabolic changes in inflammatory
conditions to varying fluorescence intensities [
44
]. Zone 2, containing mainly 5-HIAA,
clearly differentiated the control group from the benign and EC patients. This finding aligns
with previous work where a similar trend was observed in this spectral range in the study
of malignant melanoma [31]. In zone 3, which exhibited fluorescence from 3-HAA, malig-
nant samples showed elevated levels, confirming the increased catabolism of tryptophan.
This elevation in tryptophan catabolites aligns with findings from various cancer studies,
highlighting that such metabolic shifts are a common feature in malignancy [
29
,
31
,
45
,
46
].
Our study found a possible NADH red shift and elevated FAD levels in malignant urine
samples, indicating altered metabolic processes and changes in redox state associated with
cancer. These findings align with reports of altered NADH fluorescence in melanoma [
47
]
and cancer detection studies highlighting NADH and FAD as potential spectral biomark-
ers [
21
,
45
,
48
]. Interestingly, zone 6 was not elevated in benign or malignant samples but
had lower fluorescence overall. This contrasts with elevated levels observed in ovarian
Cancers 2024,16, 3155 14 of 20
cancer patients [
46
], suggesting variability based on cancer type or patient population
in EC.
Comparison of the average fluorescence profiles of the different groups is not ideal
due to the inherent variability in fluorescence profiles among individuals. This variability
can obscure significant differences and lead to misleading conclusions when only average
values are considered. Instead, the ratios of selected fluorescence zones provide a more
robust and reliable predictive value. These ratios define so-called fluorescent spectral mark-
ers and can highlight specific metabolic shifts and abnormalities that are more consistent
across different individuals within the same group, offering a clearer distinction between
control, benign, and malignant samples.
The Z4a/Z5 ratio, representing the balance between NADH and xanthurenic acid,
provided good differentiation between control and malignant samples. NADH plays a
crucial role in cellular respiration, and its levels are often altered in cancer cells due to
metabolic reprogramming, including the Warburg effect [
49
]. Xanthurenic acid, a trypto-
phan metabolite, has also been associated with oxidative stress and immune modulation,
processes that are disrupted in malignancy [
50
]. Similarly, the Z6/Z7 ratio, comparing
kynurenine and FAD, showed strong diagnostic potential. Kynurenine, a key metabo-
lite in tryptophan catabolism, is known to be elevated in various cancers, including EC,
contributing to immune suppression and tumor progression [
51
,
52
]. Elevated FAD levels,
which reflect alterations in cellular redox states and energy metabolism, are characteristic
of cancer cells [53].
Both the Z4a/Z5 and Z6/Z7 ratios exhibit potential as prospective spectral biomarkers
for distinguishing between benign gynecological samples or EC samples from a control
group, as evidenced by their respective ROC AUC values. The highest AUC, 80.34% for the
Z6/Z7 ratio in malignant samples, indicates strong diagnostic accuracy, despite the lower
AUC values potentially influenced by unequal data sets. In comparison with published
studies utilizing fluorescence ratios of urine, our results align, demonstrating the clinical
applicability of these ratios as reliable biomarkers [
21
,
48
]. Although these studies focused
on multiple ratios and observed variability in their performance, our findings fall within
the acceptable range, reinforcing the potential of these spectral markers for non-invasive
cancer detection.
Our results compare favorably with commonly used blood biomarkers, such as HE4
(human epididymis protein 4) and CA125, which are often used in clinical settings for
the diagnosis of endometrial and ovarian cancers. While HE4 and CA125 are established
markers, their diagnostic performance in EC detection has limitations. Studies have re-
ported AUC values ranging between 70–85% for HE4 and between 65–80% for CA125,
depending on the cohort and the stage of the disease [
54
]. In comparison, our Z6/Z7 ratio
demonstrates a comparable AUC of 80.34%, suggesting similar diagnostic accuracy but
with a simpler, non-invasive urine-based approach. Unlike blood biomarker tests, urine
diagnostics are pain-free, more convenient for patients, and provide an opportunity for
more frequent testing, thus offering a better patient experience and improved accessibility
in clinical practice.
In comparison to other non-invasive biomarkers such as urinary miRNAs, our Z6/Z7
ratio shows similar diagnostic potential. Studies have demonstrated that, for example,
miR-92a and other miRNAs can achieve AUC values between 75–85% for distinguishing EC
from healthy controls [
55
]. While both approaches offer the benefits of non-invasive testing,
the Z6/Z7 ratio provides a simpler and more accessible method. Unlike miRNA detection,
which requires more complex molecular testing, our technique is easier to implement
in clinical practice, potentially allowing for more frequent, patient-friendly testing and
broader clinical application.
To further validate the diagnostic potential of this method, several machine learn-
ing models were constructed. The Partial Least Squares Discriminant Analysis (PLS-DA)
showed high accuracy in differentiating EC from control samples. However, the model
demonstrated moderate performance in distinguishing benign samples from control sam-
Cancers 2024,16, 3155 15 of 20
ples. The disparity in sample sizes (96 control, 77 malignant, and 23 benign) may contribute
to the model’s limited effectiveness in identifying benign samples. In comparison, Shao
et al. used PLS-DA on UPLC-Q-TOF/MS data for urine metabolomic profiling in endome-
trial cancer detection, achieving high diagnostic performance, albeit with a more expensive
technique [
56
]. Meza Ramirez et al. employed infrared spectroscopy combined with
Orthogonal Projection to Latent Structures (OPLS) as a pre-processing technique before
applying PLS-DA and optimizing their results [
18
]. Despite using different methodologies,
both studies support our findings by demonstrating the effectiveness of PLS-DA in identi-
fying EC from urine samples. These results highlight the potential of the PLS-DA model
for diagnostic applications in non-invasive EC detection, particularly for differentiating
malignant samples from controls.
While previously mentioned studies using spectroscopic methods such as infrared
spectroscopy and ambient mass spectrometry have shown high diagnostic accuracy, our
approach using fluorescence spectroscopy offers a more rapid and cost-effective alternative
for large-scale screening. The SVM model using transposed fluorescent zones demonstrated
the highest performance with an AUC of 0.87. When using the whole uTFMP, both LR and
SVM achieved the highest AUC of 0.90, showcasing their superior diagnostic potential.
These results are comparable to a previous study, which achieved an accuracy of 0.89 using
SVM on metabolic data, although their method is significantly more expensive [
56
]. Overall,
our results demonstrate the efficacy and practicality of fluorescence spectroscopy combined
with SVM and LR for non-invasive EC detection.
To assess the diagnostic potential of our machine learning models, confusion matrices
were generated for both the transposed fluorescent zones and the overall urine profile.
These matrices revealed LR and SVM as the top-performing models, demonstrating strong
accuracy in distinguishing EC patients from controls. Although false positives are less
concerning in a screening context due to follow-up tests such as imaging or biopsy, re-
ducing them is still important to lower patient anxiety and healthcare costs [
57
]. False
negatives, where cases of EC might be missed, pose a greater risk due to potential treatment
delays, though they would likely be identified in later diagnostic stages [
58
]. Mitigation
strategies, such as increasing the sample size, especially for benign and control cases, and
improving the models, will help minimize both false positives and false negatives. Addi-
tionally, integrating this tool into a multi-modal diagnostic framework, commonly seen in
other machine learning studies that combine traditional diagnostic methods such as blood
biomarkers and imaging techniques with ML algorithms, can significantly enhance the
overall accuracy and performance of the model [54,59].
Ultimately, the combination of high diagnostic accuracy, non-invasiveness, and cost-
effectiveness makes our method, particularly with LR and SVM, a promising tool for
early-stage EC detection. Future work should focus on balancing sensitivity and specificity,
ensuring the tool is optimized for clinical use as an efficient and accessible screening
method. As this method is intended to be a quick and affordable screening tool, its primary
goal is to identify patients who require further diagnostic evaluation rather than providing
a definitive diagnosis. With these refinements, the tool is well-positioned to support early
cancer detection, guiding healthcare professionals toward more precise, invasive diagnostic
procedures when necessary.
To provide additional context for the performance of our method, Table 3presents
a comparison with other state-of-the-art diagnostic techniques for EC. Our approach,
leveraging urinary fluorescence spectroscopy combined with ML, achieves a competitive
AUC of 0.90. This method also offers significant advantages, including its non-invasive
nature and ease of implementation, making it a promising option for clinical applications. In
comparison, Meza Ramirez et al. achieved a higher AUC of 0.99 using infrared spectroscopy,
but their method required urine collection via catheter, making it a more invasive sampling
approach, which could limit its broader clinical adoption [
18
]. Bouziyane et al. reported an
AUC of 0.93 using RT-qPCR for miR-21 in tissue samples, but the need for tissue biopsies
makes it more invasive than our urine-based method [
60
]. Njoku et al. (2022) demonstrated
Cancers 2024,16, 3155 16 of 20
a range of AUC values, with urine CA125 alone yielding an AUC of 0.89 and HE4 an AUC
of 0.69. However, when combined with transvaginal ultrasound-measured endometrial
thickness, the diagnostic model achieved an AUC of 0.96, offering a non-invasive yet more
complex and expensive approach [
59
]. Finally, Barr et al. (2022) reported an AUC of 0.77
from blood biomarkers (CA125 and HE4). Their results also varied, with the combined
model including BMI and parity achieving an AUC of 0.91 in premenopausal women [
54
].
Although they demonstrated diagnostic potential, the use of blood biomarkers highlights
the limitations of traditional testing in terms of invasiveness and diagnostic accuracy. While
some methods demonstrate slightly higher diagnostic performance, our study strikes
a balance between accuracy, simplicity, and patient comfort, positioning it as a strong
alternative for widespread, non-invasive screening of EC.
Table 3. Comparison of state-of-the-art non-invasive diagnostic techniques for EC.
Study Method Sample Size Biomarkers
AUC
Reference
Our Study (2024) Fluorescence
spectroscopy + ML (urine)
77 EC, 96 controls,
23 benign Spectral features 0.90 -
Meza Ramirez
et al. (2022)
Infrared spectroscopy + ML
(catheterized urine) 109 EC, 110 controls Spectral features 0.99 [18]
Bouziyane et al. (2021) RT-qPCR (tissue) 71 tumors, 53 adjacent,
54 benign miR-21 0.93 [60]
Njoku et al. (2022) Urine biomarkers + TSV 153 symptomatic
patients
CA125 + endometrial
thickness 0.96 [59]
Barr et al. (2022) Blood biomarkers 755 patients (397 EC) HE4, CA125 0.77 [54]
A key challenge in deploying ML models in healthcare is ensuring that clinicians
understand and trust the models’ predictions. Explainable AI (XAI) methods offer a solution
by providing insights into how models reach their conclusions, thereby improving both
model performance and trustworthiness in clinical settings [
61
,
62
]. XAI allows clinicians
to verify the model’s decisions against established clinical literature, ensuring that the AI
identifies biologically relevant patterns [
62
,
63
]. This capability is particularly important
when using fluorescence profiles, as XAI can help elucidate which spectral features or zones
contribute most to distinguishing between benign, malignant, and control samples [
61
,
63
].
Additionally, XAI supports ethical and legal frameworks by addressing transparency
issues, such as the “right to explanation” under the European Union’s GDPR [
64
], fostering
trust in AI systems. This transparency is crucial for encouraging clinician acceptance and
trust, as they need confidence that AI systems are safe, interpretable, and aligned with
clinical protocols. Furthermore, explainability can help ensure ethical decision-making,
especially in high-stakes applications such as healthcare [65].
As research progresses, integrating XAI with urinary fluorescence profiling can signifi-
cantly enhance diagnostic precision while also adhering to the ethical standards expected in
medical applications [
64
,
65
]. The future development of this study should further explore
the potential of XAI to explain model results and verify them with clinical literature, which
will be crucial for gaining acceptance and trust from healthcare stakeholders [61,63].
Our study’s findings suggest that urinary fluorescence profiles, combined with ML
models, can effectively distinguish between control, benign, and malignant samples, of-
fering prospective diagnostic markers for early-stage EC. The non-invasive nature of this
technique, coupled with its cost-effectiveness, makes it a promising tool for large-scale
screening and early detection.
Integrating this method into current diagnostic workflows would provide a prelim-
inary screening tool that could guide further diagnostic procedures, such as imaging or
biopsy, especially for patients identified as high-risk. This approach could reduce the need
for invasive tests in low-risk patients while ensuring more targeted and timely interven-
tions for high-risk individuals. However, its clinical implementation may face barriers,
Cancers 2024,16, 3155 17 of 20
including the need for larger validation cohorts and the establishment of infrastructure for
fluorescence-based screening in clinical settings.
While the preliminary results are promising, further validation with larger cohorts
is necessary to confirm these findings. Expanding the analysis to include more benign
cases and exploring other cancer types could increase the generalizability and applicability
of this affordable diagnostic approach. Overcoming these barriers will be essential to
successfully integrating this method into clinical workflows, ensuring it provides a cost-
effective, accurate, and scalable solution for early cancer detection.
5. Conclusions
In conclusion, this study provides compelling evidence for using urinary fluorescence
spectroscopy as a screening tool for endometrial cancer. The method’s good sensitivity and
specificity, non-invasive nature, and cost-effectiveness make it a valuable addition to current
diagnostic techniques. Machine learning models further enhance its accuracy, making it
a robust tool for clinical applications, especially in resource-limited settings. As this is a
pilot study, further research is essential to validate these findings. Larger cohorts, including
more patients in the benign group and healthy volunteers, are needed to ensure reliability
and generalizability. This study’s limitations, such as the variability in fluorescence profiles
among individuals, must be acknowledged. Despite these challenges, the advantages of this
approach over traditional diagnostic methods are significant. Traditional diagnostics often
involve invasive procedures and high costs, which can hinder early detection. Compared to
other spectroscopic methods or traditional metabolomics, urinary fluorescence spectroscopy
offers a more practical alternative due to its ease of use and lower cost. Additionally, its
non-invasive nature makes it accessible to a broader population regardless of age. Future
studies should aim to reproduce these results in larger, more diverse populations and
explore the technology’s potential to differentiate various molecular profiles of endometrial
cancer. Expanding research to include other gynecological cancer types could further
enhance the diagnostic approach’s generalizability. In summary, while further validation is
necessary, this study lays the groundwork for a promising new diagnostic tool that could
significantly impact the early detection and management of endometrial cancer, ultimately
improving patient outcomes.
Author Contributions: Conceptualization, K.D. and M.Š.; methodology, K.D. and A.B.; software,
M.Š.; validation, M.Š. and K.D.; formal analysis, M.Š.; investigation, M.Š. and K.D.; resources,
M.M. and P.U.; data curation, M.Š. and A.B.; writing—original draft preparation, M.Š. and K.D.;
writing—review
and editing, K.D.; visualization, M.Š.; supervision, K.D. and M.M.; project adminis-
tration, M.M. and P.U.; funding acquisition, M.M. All authors have read and agreed to the published
version of the manuscript.
Funding: This research was funded by the Scientific Grant Agency of the Ministry of Education,
Science, Research, and Sport of the Slovak Republic and Slovak Academy of Sciences VEGA, grant
number 1/0435/23.
Institutional Review Board Statement: This study was conducted in accordance with the Declaration
of Helsinki and approved by the Ethics Committee of Pavol Jozef Šafárik University in Košice, Faculty
of Medicine (2024/EK/01011).
Informed Consent Statement: Informed consent was obtained from all participants involved in
this study.
Data Availability Statement: The data presented in this study are available upon reasonable re-
quest from the first author. The data are not publicly available due to ethical restrictions and
patient confidentiality.
Conflicts of Interest: The authors declare no conflicts of interest.
Cancers 2024,16, 3155 18 of 20
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