sensors
Article
Synergy Effect of Combined Near and Mid-Infrared
Fibre Spectroscopy for Diagnostics of
Abdominal Cancer
Thaddäus Hocotz 1, Olga Bibikova 2,*, Valeria Belikova 3, Andrey Bogomolov 3,
Iskander Usenov 2,4, Lukasz Pieszczek 5, Tatiana Sakharova 2, Olaf Minet 6, Elena Feliksberger 2,
Viacheslav Artyushenko 2, Beate Rau 1and Urszula Zabarylo 7
1Department of Surgery, CharitéUniversity of Berlin, 13353 Berlin, Germany; [email protected] (T.H.);
[email protected] (B.R.)
2Art Photonics GmbH, Rudower Chaussee 46, 12489 Berlin, Germany; [email protected] (I.U.);
3Laboratory of Multivariate Analysis and Global Modelling, Samara State Technical University,
[email protected] (A.B.)
4Institute of Optics and Atomic Physics, Technical University of Berlin, 10623 Berlin, Germany
5Institute of Chemistry, University of Silesia in Katowice, 9 Szkolna Street, 40006 Katowice, Poland;
6CC06-CharitéCentrum 6, Center for Diagnostic and Interventional Radiology and Nuclear Medicine,
CharitéUniversitätsmedizin Berlin, CBF Campus Benjamin Franklin, Hindenburgdamm 30, 12203 Berlin,
Germany; [email protected]
7BIH Berlin Institute of Health, BCRT-Center for Regenerative Therapies, Cranach Haus,
Charité- Universitätsmedizin Berlin, CVK Campus Virchow-Klinikum, Augustenburger Platz 1,
*Correspondence: [email protected]; Tel.: +49-30-6779-8870
Received: 27 October 2020; Accepted: 19 November 2020; Published: 23 November 2020
Abstract:
Cancers of the abdominal cavity comprise one of the most prevalent forms of cancers,
with thehighest contributionfrom colonand rectalcancers (12%of thehuman population), followedby
stomach cancers (4%). Surgery, as the preferred choice of treatment, includes the selection of adequate
resection margins to avoid local recurrences due to minimal residual disease. The presence of
functionally vital structures can complicate the choice of resection margins. Spectral analysis of tissue
samples in combination with chemometric models constitutes a promising approach for more efficient
and precise tumour margin identification. Additionally, this technique provides a real-time tumour
identification approach not only for intraoperative application but also during endoscopic diagnosis
of tumours in hollow organs. The combination of near-infrared and mid-infrared spectroscopy has
advantages compared to individual methods for the clinical implementation of this technique as a
diagnostic tool.
Keywords:
mid-infrared spectroscopy; near-infrared spectroscopy; FTIR absorbance; diffuse reflection;
synergy effect; fibre probe; joint data analysis; cancer diagnostics
1. Introduction
Abdominal cancers comprise of several cancer types between the lower chest and the groin,
which include the stomach, small intestines, colon, liver, gallbladder, pancreas, spleen, kidneys,
and adrenal glands. They are one of the most prevalent forms of cancer, with the highest contribution
Sensors 2020,20, 6706; doi:10.3390/s20226706 www.mdpi.com/journal/sensors
Sensors 2020,20, 6706 2 of 18
from colorectal cancers (12% of the human population), followed by stomach cancer (4%) [
1
]. The 5-year
survival varies from more than 50% for colorectoral cancer to less than 8% for pancreatic cancer [2].
Surgery, as one of the preferred treatment modalities, aims to remove cancerous tissue, including
a margin of healthy tissue, making the selection of resection margins as one of the foremost challenges
for a surgeon [
3
]. Inadequate resection margins may lead to the increased risk of local recurrence or the
need for additional resection [
4
], while the severe side effects of radical surgery lead to a considerable
deterioration of patient health and quality of life [
5
]. As a result, new diagnostic methods are being
sought to accurately and rapidly assess the extent of tumour margins and to distinguish malignant
tissues from normal ones more efficiently.
Optical spectroscopy methods offer unique opportunities to investigate the sample properties
at the molecular level. Since every chemical species in a sample has a unique spectrum, the overall
chemical composition can be evaluated through the spectral analysis by using sophisticated methods of
modern data analysis, which are referred to as chemometrics [
6
]. In that context, on/in-line spectroscopy
has proven to be a valuable and efficient analytical tool for medical diagnostics and general tissue
studies in biology and medicine [
7
,
8
]. As a new diagnostic tool, it offers unique opportunities for a
label-free investigation of tissue samples at the molecular level, serving as an ancillary instrument for
classical histopathology, and it is sometimes referred to as “spectral histopathology” [
9
,
10
]. Optical
fibres play a vital role in the translation of spectroscopy methods into clinical and preclinical practice:
the ability to miniaturise fibres, their considerable flexibility, and their ease of use facilitate the
translation of such devices to the clinical setting. This technology would allow in situ analysis in real
time with a minimal requirement of optical alignment and maximum safety [
11
]. Near-infrared (NIR)
fibre-optic spectroscopy is one of the most established spectroscopic methods that is successfully used
in measuring water content in the skin [
12
], brain oxygenation in stroke patients [
13
], and in cancer
research [
14
,
15
]. Near-infrared spectra are formed by combinations and overtones of fundamental
vibrations of C–H, N–H, O-H, and other functional groups [
14
]. NIR spectroscopy provides information
on different biologically important molecules (e.g., proteins, lipids, glucose, collagen, globulins) [
16
]
and is capable of diagnosing several diseases [
16
], including cancer of various organs [
17
,
18
]. However,
the overlap of individual absorptions, resulting in broad, unspecific bands limits the ability to identify
concentrations of specific molecular species contained in the sample [
16
,
19
]. Vibrational spectroscopy
is one of the fundamental physical methods of chemical analysis that avoids the limitation of low
molecular recognition accuracy caused by the broadness of spectral bands in NIR reflection [
20
].
Molecular-specific vibrational absorptions in the mid-infrared (MIR) fingerprint region, which are
characteristic for various anti-symmetric vibrations (e.g., for polar bonds such as O–H or N–H),
enable the unambiguous molecular identification of many major and minor compounds present in
the samples [
21
]. Despite classical MIR transmission spectra of biological tissue being rather complex
to analyse, there have been many successful attempts to apply attenuated total reflection (ATR) MIR
spectroscopy to various types of human cancer [
22
–
24
] to evaluate early-stage cartilage degradation [
25
]
and for the analysis of sediments in various body fluids [
26
]. Moreover, there are proof-of-concept
examples of clinical applications of MIR spectroscopy in Fourier transform mode (FT-IR) that are
used for the enhancement of histopathology and cytology [
27
]. FTIR can be applied not only for
cancer diagnosis but also for the interrogation of different tissues to diagnose diabetic nephropathy
progression [28], systemic sclerosis [29], or systemic amyloidosis [30].
A combination of several spectroscopic modalities enables us to reveal information that was
previously not accessible and eliminates the drawbacks of individual approaches. In the last decade,
it was demonstrated that a combination of fibre-optic approaches with the fusion of spectroscopic
data from several methods increases the accuracy of sample differentiation by tens of percent [
31
,
32
].
Recently, authors demonstrated that a combination of a fibre-optic approach with the fusion of the
spectroscopy data from several methods also increases the accuracy of tumour detection by 28% [33].
However, the majority of reported multispectral analyses present a combination of fluorescence
and NIR spectroscopy methods [
32
,
34
]. Here, we present a fusion of datasets obtained from two
Sensors 2020,20, 6706 3 of 18
complementary techniques enclosing the information about the molecular differences on the cellular
level (ATR MIR absorption) and the changes in tissue properties at a significant penetration depth of
several millimeters (NIR reflection). The main advantage of MIR analysis is the ability to precisely
interpret analytical information provided by spectral signatures of functional groups present in its
molecular structure (direct spectral information). The latter enables the detection of different cancer
types based on the qualitative and quantitative correlation of specific cancer biomarkers. MIR radiation
penetrates the sample by up to 2
µ
m, which allows analysing individual cellular structures [
33
],
while NIR radiation penetrates up to several mm into the tissue, allowing the evaluation of several
tissue layers (e.g., epithelial cells, mucosa, muscular) [
14
]. We assume that this ability could vastly
enhance the information collection process and become an essential part of the clinical translation of a
new cancer diagnostic tool.
For a dataset combining several heterogeneous parts, two strategies of data analysis could be
considered: (i) to apply one common universal model covering the entire dataset and (ii) to divide the
samples into groups and consider the corresponding datasets separately. It is necessary to find the
optimal division of the dataset to determine which approach would provide the most accurate solution.
The current results represent a part of a more comprehensive research project with the primary aim to
develop new optical techniques for tumour margin identification and non-invasive cancer diagnosis
in real time, including the translation of the technique into the clinical environment as a supportive
diagnostic tool. The authors’ recent work from this series was aimed at the development and testing
of a NIR sensor for the diagnosis of kidney tumours based on light-emitting diodes (LEDs) [
15
].
Furthermore, the synergy effect of the concomitant MIR and fluorescence spectroscopy use for analysis
of kidney tumours [
33
] or NIR and fluorescence spectroscopy for analysis of colon tumours was
demonstrated [
35
]. In this study, we applied an equivalent approach. The novelty lies in testing an
entirely different combination of near- and mid-infrared fibre-optic spectroscopy techniques to detect
several abdominal cancers, i.e., stomach, colon, and rectal cancer, and to demonstrate a similar synergy
effect of their combination. Exceeding the scope of previous publications, not only the separation of
normal and cancerous tissue samples of the same organ but also the spectroscopic separation between
the different organ types was intended.
2. Materials and Methods
2.1. Sample Collection
Altogether, 70 unstained tissue samples were collected from 35 patients suffering from colorectal
or stomach cancer. From each patient, two correlating ex vivo samples (one sample of cancerous
tissue and one sample of healthy tissue) were taken. The samples were acquired during the planned
surgery for open or laparoscopic resection of cancerous tissue. Informed patient consent was acquired
at least 24 h before the surgery. The tissue sampling and further investigation of biopsies were
approved by the institutional ethics committee (ethical approval number EA1/159/16). All samples
were collected at two independent departments of general surgery of the Charit
é
—Universitätsmedizin
Berlin, Germany—Campus CharitéMitte (CCM) and Virchow—Klinikum (CVK).
Theresectedtissuewastransportedinasealedcontainertothepathologydepartmentwithin30min
of resection. From each resectate, a trained pathologist took similar samples of cancerous and normal
tissue (5–10 mm thickness) with appropriate distance (at least 5 cm) from each other. The specimens
were mounted on cork tiles, fixed with pins, and immediately quick-frozen in liquid nitrogen. Samples
were stored and transported exclusively at −80 ◦C until the spectroscopic measurement.
2.2. Sample Preparation
A 37
◦
C water bath without direct water contact was used to thaw the samples. The samples
were subsequently fixed in a petri-dish, in the same orientation as on the cork tiles using tissue glue
(TRUGLUE
®
, Trusetal, Germany) to avoid sample displacement during measurement. The Petri dish
Sensors 2020,20, 6706 4 of 18
was equipped with a colour- and position-coded (blue =normal; red =cancerous tissue) 3 mm
×
3 mm
gridformeasurementpointdetermination. Thesurfaceofthetissuewaswashedwithsaline(NaCl0.9%).
For each specimen, at least three distinct points were selected. Mid-infrared absorption (MIR) and
diffuse reflection (NIR) measurements were performed using optical fibre probes placed in slight
contact with the tissue surface. During each measurement, the probe was placed in contact with
the sample manually. Through this process, we intended to cover the entire outer silica crystal tip
with the examined tissue surface. Other specific measurement procedures that ensure a constant
contact pressure between the probe and the tissue samples were not implemented during this study.
The objective of the applied experimental design was to mimic in vivo conditions during endoscopic
examinations of the gastrointestinal tract. The accurate spatial coincidence of measurements with two
independent spectroscopic methods was ensured by performing the measurements in preselected
positions following the position code marked on the grid. Three measurements were performed using
each spectroscopic method in each preselected position. Additional specifications about the sample
preparation can be found in previous publications by the authors [15,33].
2.3. Spectroscopic Measurements
A Matrix MF (Bruker Optik GmbH, Ettlingen, Germany) spectrometer was employed for MIR
spectroscopic measurements. It was equipped with a mercury–cadmium–telluride (MCT) detector
cooled by liquid nitrogen. A polycrystalline infrared (PIR) fibre-based attenuated total reflection
(ATR) probe topped with a silica crystal (art photonics GmbH, Berlin, Germany) was used for the
acquisition of spectra. The probe had been adjusted for measurements of tissue samples in the
spectroscopic fingerprint region (1800 to 900 cm
−1
). In such a range, MIR spectra of studied samples
were acquired with the spectral resolution of 8 cm
−1
. Each collected spectra was the result of 64
averaged scans. A sterile 0.9% sodium chloride physiological solution was used as a reference sample
for spectra calibration.
Spectra in the NIR range were acquired using a portable fibre-optic NIRQuest512 spectrometer
(Ocean Optics, Inc., Orlando, FL, USA) equipped with an indium gallium arsenide detector. The source
of light used during the measurements was a LS-1 tungsten halogen lamp (Ocean Optics, Inc., Orlando,
FL, USA). The spectrometer was supplied with a fibre-optic probe with eight 400 mm fibres—one for
light emission and seven for light collection (art photonics GmbH, Berlin, Germany). The recorded
spectral NIR range was from 900 to 1700 nm, and the spectral resolution was equal to 1.66 nm.
The exposure time of the measurements and number of scans collected for spectra averaging were
150 ms and 5, respectively. Measurements of a white reference material—Spectralon (Labsphere, Inc,
North Sutton, NH, USA) and closed spectrometer slit (dark reference) were conducted to calibrate
the spectral intensity of the sample spectra. The spectral intensity calibration was performed with
collected white and dark references through so called unity-based normalisation (UBN) approach [
36
].
2.4. Histopathological Evaluation
To confirm the presence or absence of cancer cells in the measured samples, the spectroscopically
inspected surface of the specimens underwent histopathological evaluation via a standardised frozen
thin-section technique. Only samples with a confirmed presence or absence of cancer cells were used
for chemometric modelling.
The detailed description of the samples is presented in Table 1that shows the amount of cancer
tissue samples in which cancer tissue could be confirmed. The reduced number of confirmed cancer
samples in the stomach group can be explained by the comprehensive application of neoadjuvant
chemotherapy, completely eradicating the cancerous tissue in over 50% of cases. The reduced number
of confirmed malignant samples in the stomach cancer group was caused by the presence of diffuse
stomach cancer subtype in 4(N)/19(N) patients. Only spectroscopic data of confirmed cancer and
normal samples was utilised for the data analysis and modelling aspects of the study.
Sensors 2020,20, 6706 5 of 18
Table 1. Samples used for data evaluation.
Organ Number of Patients Number of Samples Cancer in Tumour
Sample (Confirmed)
Absence of Cancer in
Normal Sample
(Confirmed)
Stomach
19 38 (19N, 19T) 9 (T)/19 (T) 15 (N)/19 (N)
Colon 10 20 (10N, 10T) 9 (T)/10 (T) 10 (N)/10 (N)
Rectum 6 12 (6N, 6T) 6 (T)/6 (T) 6 (N)/6 (N)
2.5. Data Analysis
Each tissue sample was measured three to five times in three to five positions, depending on
the size of the sample. Repeated measurements were averaged. Each row in a dataset matrix (i.e.,
position) corresponds to a unique position on one of the tissue samples, and several subsequent rows
(i.e., positions) in the matrix correspond to one individual patient (tumour and normal tissue samples).
In some cases, the size and consistency of the biopsy were not suitable for complete measurement
with both spectroscopic methods. Therefore, the original, full MIR dataset, including colon samples of
10 patients resulted in 73 different positions, 15 stomach samples resulted in 93 positions, and 6 rectum
samples resulted in 37 positions. The original, full NIR dataset, including colon samples of 10 patients
resulted in 54 different positions, 13 stomach samples resulted in 61 positions, and 6 rectum samples
resulted in 28 positions. The main difficulty in testing the synergy effect is the necessity of using the
same position measured by both spectroscopic methods. Since this was not always possible, there are
only 52 colon sample positions (27N, 25T) from 10 patients, 52 stomach sample positions (32N, 20T)
from 12 patients, and 21 rectum sample positions (9N, 12T) from 6 patients that were used in this work.
Reduced datasets were used also for the construction of individual models based on one spectroscopic
technique (NIR or MIR) to reliably compare the diagnostic potential of concatenated data. The reason
behind such an idea was to avoid the influence of imbalanced quality and size of different datasets,
which itself could be a strong source of additional variance and may hamper the final interpretation
and comparison of the diagnostic performance of investigated approaches.
Multivariate data analysis and data visualisation were performed with TPT-cloud (www.tptcloud.
com, Global Modelling, Aalen, Germany and Samara State Technical University, Samara, Russia)
and Interval Selection Toolbox for Matlab
TM
(MathWorks, Natick, MA, USA). Before the analysis
was conducted, repeated measurements were averaged. Partial least-squares discriminant analysis
(PLS-DA) [
37
] was used for building classification models. Moreover, we applied Variable Importance
in Projection (VIP) method. The exact methodology of VIP is described elsewhere [
38
]. Leave-one-out
(LOO) [
39
,
40
] and Monte Carlo [
41
,
42
] cross-validation were used to test the model performance.
During Monte Carlo cross-validation, 10% of the complete dataset was removed in an iterative
procedure. The validation cycle was repeated 100 times for each model. Raw datasets and datasets
pretreated by different methods [
43
] were considered, including standard normal variate correction
(SNV), first (1D) and second (2D) derivative (by Savitzky–Golay algorithm with a second-order
polynomial and window width of 25 points for NIR data and 8 points for MIR data), first and second
derivative and SNV (derivative followed by SNV). Additional normalisation (max intensity is 1,
and min intensity is 0) was used for preprocessed NIR and MIR spectra to concatenate both parts
correctly. It is important to note that for each data part (for each spectroscopic method), the whole
series of the spectra were scaled with the same coefficient. In the present paper, we have decided
to use this normalisation strategy to avoid any further increase of complexity during data analysis
and to ensure the same contribution of NIR and MIR data parts in models based on concatenated
spectroscopic data.
NIR data in the figures were also smoothed by Savitzky–Golay filter (with second-order of
polynomial and window width of 25 points).
In PLS-DA modelling, 1 (“positive” test results) indicates designated tumours, and 0 (“negative”
test results) indicates designated normal samples. Sensitivity, specificity, and accuracy statistics
Sensors 2020,20, 6706 6 of 18
were used for a calibration dataset and cross-validation prediction to measure discrimination quality.
Additional specifications can be found in previous publications by the authors [33].
2.6. Models Ranking Using ROC-Curves
A receiver operating characteristic curve (ROC-curve) was used for the model comparison,
which was created by plotting the true positive rate, i.e., sensitivity (Sn) against the false positive rate,
i.e., reversed specificity (1-Sp) at various threshold settings [
44
]. In other words, each PLS-DA model is
represented by a curve in the ROC-space, and each point on the ROC-curve corresponds to a model
statistics for a certain threshold. One point in the ROC-space is considered to be better than another
if it corresponds to a higher sensitivity and specificity at a time, i.e., if it is closer to the point with
coordinates (0, 1), which represented a perfect classification case (100% sensitivity and 100% specificity).
Informally speaking, one model is better than another if most of the corresponding curve is closer to
point (0, 1) than most of the curve corresponding to the second model.
3. Results and Discussion
3.1. Spectral Analysis
Mid-infrared absorption and near-infrared reflection spectra were collected from stomach, colon,
and rectum tissues, including malignant and normal species. In particular, 52 measured positions
of colon samples, 52 measured positions of stomach samples, and 21 measured positions of rectum
samples were selected to be included in data analysis based on the spectral quality of the raw data.
From each study subject, the average spectra of the normal and tumour tissues were calculated. NIR
and MIR spectra of normal tissue samples from colon (green), stomach (blue), and rectum (red) are
presented in Figure 1a,b correspondingly.
Sensors 2020, 20, 6706 6 of 19
used for a calibration dataset and cross-validation prediction to measure discrimination quality.
Additional specifications can be found in previous publications by the authors [33].
2.6. Models Ranking Using ROC-Curves
A receiver operating characteristic curve (ROC-curve) was used for the model comparison,
which was created by plotting the true positive rate, i.e., sensitivity (Sn) against the false positive rate,
i.e., reversed specificity (1-Sp) at various threshold settings [44]. In other words, each PLS-DA model
is represented by a curve in the ROC-space, and each point on the ROC-curve corresponds to a model
statistics for a certain threshold. One point in the ROC-space is considered to be better than another
if it corresponds to a higher sensitivity and specificity at a time, i.e., if it is closer to the point with
coordinates (0, 1), which represented a perfect classification case (100% sensitivity and 100%
specificity). Informally speaking, one model is better than another if most of the corresponding curve
is closer to point (0, 1) than most of the curve corresponding to the second model.
3. Results and Discussion
3.1. Spectral Analysis
Mid-infrared absorption and near-infrared reflection spectra were collected from stomach,
colon, and rectum tissues, including malignant and normal species. In particular, 52 measured
positions of colon samples, 52 measured positions of stomach samples, and 21 measured positions of
rectum samples were selected to be included in data analysis based on the spectral quality of the raw
data. From each study subject, the average spectra of the normal and tumour tissues were calculated.
NIR and MIR spectra of normal tissue samples from colon (green), stomach (blue), and rectum (red)
are presented in Figure 1a,b correspondingly.
Figure 1. The mean spectra and the intervals of standard deviation for normal colon, stomach, and
rectum samples: (a) unpreprocessed mid-infrared (MIR) spectra; (b) smoothed standard normal
variate correction (SNV)-normalized near-infrared (NIR) spectra. The curves and the surrounding
coloured regions represent the mean spectra and the standard deviation intervals of the respective
data variables.
Significant differences between organs were observed in the whole spectral interval for both
methods, MIR and NIR spectra. The overall intensity distribution of MIR absorption was much higher
for rectal tissue in comparison with colon and stomach tissues. In the rectal tissue, spectral differences
were especially pronounced in the region of 1800–1700 cm−1, which can be assigned to the carbonyl
stretching vibration of lipids at 1742 cm−1, and in the correlating interval surrounding 1458 cm−1,
which can be associated with lipid CH2 bending or scissoring vibrations [45–47]. Spectral differences
for rectal tissue were also observed at around 1240 cm−1 caused by the asymmetric stretching
vibrations of PO2 associated with phosphate and a nearby peak at 1155 cm−1 that can be assigned to
the C–OH stretching mode by amino acids (phenylalanine, threonine, tyrosine, serine) from cell
Figure 1.
The mean spectra and the intervals of standard deviation for normal colon, stomach,
and rectum samples: (
a
) unpreprocessed mid-infrared (MIR) spectra; (
b
) smoothed standard normal
variate correction (SNV)-normalized near-infrared (NIR) spectra. The curves and the surrounding
coloured regions represent the mean spectra and the standard deviation intervals of the respective
data variables.
Significant differences between organs were observed in the whole spectral interval for both
methods, MIR and NIR spectra. The overall intensity distribution of MIR absorption was much higher
for rectal tissue in comparison with colon and stomach tissues. In the rectal tissue, spectral differences
were especially pronounced in the region of 1800–1700 cm
−1
, which can be assigned to the carbonyl
stretching vibration of lipids at 1742 cm
−1
, and in the correlating interval surrounding 1458 cm
−1
,
which can be associated with lipid CH2 bending or scissoring vibrations [
45
–
47
]. Spectral differences
for rectal tissue were also observed at around 1240 cm
−1
caused by the asymmetric stretching vibrations
of PO
2
associated with phosphate and a nearby peak at 1155 cm
−1
that can be assigned to the C–OH
Sensors 2020,20, 6706 7 of 18
stretching mode by amino acids (phenylalanine, threonine, tyrosine, serine) from cell proteins [
48
].
An additional pronounced spectral difference of the rectal tissue is related to the presence of a peak at
1083 cm
−1
. This peak is associated with symmetric stretching vibrations of the PO
2
group that mainly
originate from phospholipids and represents an increased concentration of cellular nucleic acids [49].
In the colon tissue, an additional spectral peak was observed at 1043 cm
−1
, which is assigned to the C–O
stretching band coupled with C–O banding of C–OH groups of glycogen [
50
]. Differences in the NIR
reflection spectra cannot be interpreted as straightforward as MIR spectra because of the absorption
peaks broadness (up to 100 nm) and overlapping. However, several intervals can be highlighted,
including the CH stretching second overtone (
≈
1100–1200 nm) and CH combinations first overtone of
OH and NH bonds (
≈
1300–1700 nm) [
51
]. Significant spectral variations related to the specific tissue
types described above cause additional limitations regarding the development of a common model
based on the full datasets of all three organs. In this case, the best division of the dataset should be
considered carefully to find optimal models in the data analysis process. (see Section 3.2).
As the next step, the alterations between the overall intensity distribution and the spectral shape
between malignant and normal tissues were analysed for each organ separately for MIR absorption
(Figure 2a,c,e) and NIR reflection (Figure 2b,d,f).
Sensors 2020, 20, 6706 7 of 19
proteins [48]. An additional pronounced spectral difference of the rectal tissue is related to the
presence of a peak at 1083 cm−1. This peak is associated with symmetric stretching vibrations of the
PO2 group that mainly originate from phospholipids and represents an increased concentration of
cellular nucleic acids [49]. In the colon tissue, an additional spectral peak was observed at 1043 cm−1,
which is assigned to the C–O stretching band coupled with C–O banding of C–OH groups of glycogen
[50]. Differences in the NIR reflection spectra cannot be interpreted as straightforward as MIR spectra
because of the absorption peaks broadness (up to 100 nm) and overlapping. However, several
intervals can be highlighted, including the CH stretching second overtone (≈1100–1200 nm) and CH
combinations first overtone of OH and NH bonds (≈1300–1700 nm) [51]. Significant spectral
variations related to the specific tissue types described above cause additional limitations regarding
the development of a common model based on the full datasets of all three organs. In this case, the
best division of the dataset should be considered carefully to find optimal models in the data analysis
process. (see Section 3.2).
As the next step, the alterations between the overall intensity distribution and the spectral shape
between malignant and normal tissues were analysed for each organ separately for MIR absorption
(Figure 2a,c,e) and NIR reflection (Figure 2b,d,f).
Figure 2. The mean spectra and the standard deviation intervals of tumour (designated as T) and
benign (designated as N) samples: (a) unpreprocessed mid-infrared (MIR) spectra of colon samples;
(b) smoothed SNV-normalized near-infrared (NIR) spectra of colon samples; (c) unpreprocessed mid-
infrared (MIR) spectra of rectum samples; (d) smoothed SNV-normalized near-infrared (NIR) spectra
of rectum samples; (e) unpreprocessed mid-infrared (MIR) spectra of stomach samples; (f) smoothed
SNV-normalized near-infrared (NIR) spectra of stomach samples. The curves and the surrounding
coloured regions represent the mean spectra and the standard deviation intervals of the respective
data variables.
Figure 2.
The mean spectra and the standard deviation intervals of tumour (designated as T) and
benign (designated as N) samples: (
a
) unpreprocessed mid-infrared (MIR) spectra of colon samples;
(
b)
smoothed SNV-normalized near-infrared (NIR) spectra of colon samples; (
c
) unpreprocessed
mid-infrared (MIR) spectra of rectum samples; (
d
) smoothed SNV-normalized near-infrared (NIR)
spectra of rectum samples; (
e
) unpreprocessed mid-infrared (MIR) spectra of stomach samples;
(
f
) smoothed SNV-normalized near-infrared (NIR) spectra of stomach samples. The curves and the
surrounding coloured regions represent the mean spectra and the standard deviation intervals of the
respective data variables.
Sensors 2020,20, 6706 8 of 18
In the colon tissue samples, the presence of a glycogen peak with an average intensity maximum
at 1043 cm
−1
(Figure 2a) caused by symmetric stretching vibrations of the C–O and C–OH groups
represents the most significant MIR spectral difference between normal and cancerous tissue [
33
,
52
].
Even if the average intensity of the glycogen peak is similar in cancer and normal tissues, the shape of
the peak is more distinct in the cancer samples. This finding correlates with other studies that reported
higher glycogen concentrations in cancer samples compared to normal samples in both rectum and
colon tissues. The increase of glycogen correlates with an increased proliferation rate of tumour cells
resulting in a higher degree of glycolysis to meet the cells’ increased energy demand [50,52].
The most significant differences in the NIR spectra of the colon tissue samples were observed
at CH first overtone combinations (1400–1600 nm and 1300–1420 nm), which were slightly more
pronounced for the tumour tissues [53].
In the rectal tissues samples, the malignant tissue when compared to normal tissue samples
showed increased MIR bands at positions of 1640 cm
−1
and 1550cm
−1
associated with amide groups
related to proteins. Confirming the results of recent studies [
47
,
54
], decreased peaks related to lipids
were found at 1742 cm
−1
and 1400 cm
−1
, and those related to carbohydrates were found at 1160cm
−1
.
The increase in the amide I band (1642 cm
−1
) and amide II band (1550cm
−1
) can be related to changes
in the relative amounts of proteins in the cancer cells during cancer progression [
55
] and potentially be
caused by the unregulated production of cell cytoplasm contents. The decreased peak intensity of the
C=O band (1742 cm
−1
) in the cancer sample spectra can be correlated with a higher energy demand
during cell proliferation that results in the metabolisation of fats [55].
Regarding NIR spectra, the most significant differences in the rectal tissue samples were observed
between 1000 and 1330 nm, corresponding to the CH stretching second overtone region associated
with glycoproteins, glycolipids, and carbohydrates [14].
In the gastric tissue samples, the most noticeable distinction of cancer tissue compared to normal
tissue samples was the relatively lower bands intensity (representing, therefore, lower content of
assigned material) in the spectral region from 1200 to 900 cm
−1
, representing mostly the C–O stretching
absorptions from glycogen constituents featured at 1125, 1080, and 1040 cm
−1
bands. The same
trend in stomach tissues was observed by Park et al. and Lee et al. [
56
,
57
]. In this study, the lower
concentration of glycogen in the stomach tissue of both malignant and normal samples could be
ascribed to the application of neoadjuvant chemotherapy to all study participants. Through this
therapeutic approach, not only cancer cells but also normal stomach cells could have been inhibited in
their cell proliferation. It is reasonable to assume that the healthy stomach lining is similarly affected by
chemotherapy, as are the cancer cells due to its naturally rapid proliferation. To confirm this difference,
further investigations comparing patients treated and untreated neoadjuvant chemotherapy should
be performed. Unfortunately, this is difficult to implement because the application of neoadjuvant
chemotherapy is the recommended standard procedure for patients with stomach cancer. Therefore,
access to samples of untreated patients is limited.
Spectral differences in the NIR region are most pronounced at the spectral region of OH and NH
first overtone (1300–1420 nm).
FTIR spectra of water are comprised of three prominent bands: ~3400, 2125, and 1645 cm
−1
.
In our experiment, due to the measurement range, only the third (1645 cm
−1
) H-O-H bending vibration
band was overlapping with the vibrations of other chemical compounds. This band should be removed
because it can interfere with the bands of other chemical components typically found in biological
specimens. The popular way of erasing overlapping water peaks, in studies investigating biological
fluids, is the subtraction of water/aqueous solution spectra from the spectra of the samples [
58
,
59
].
An artefact that can occur during such a procedure is a negative band in the final spectrum; this is only
done when the water band intensity is higher than the intensity of the correlating peak in the sample.
We are sure that no over-subtraction of the water band took place during the experiment because
the overall absorbance of each tissue sample spectrum was much higher than the overall absorbance
of the background (saline solution). The intensity of the reflected light beam in the water solution
Sensors 2020,20, 6706 9 of 18
was lower than in the tissue sample. Nevertheless, an under-subtraction of the water band can occur.
To this date, there is no universal and repeatable way of completely erasing this band experimentally.
However, to ensure that water content was constant, the surface of each sample was washed with
saline (NaCl 0.9%) before the measurement. Even if the subtraction does not completely erase the
water signal from the spectra, the impact of that band is drastically decreased. Therefore, the influence
of the water absorption on the final model performance is significantly reduced.
Due to the strong, broad absorptions of the water band located at 1645 cm
−1
, there is some degree
of overlap, particularly of the amide I peak [
60
]. Thus, the accurate estimation of amides based on a
peak around 1645 cm
−1
is not always possible. Subtraction of the water band is usually not done in
studies investigating tissue samples [
60
]. Therefore, a peak around 1645 cm
−1
is mostly higher than
the amide II band (1555 cm
−1
). In our study, the amide I peak intensity was decreased by the water
spectrum subtraction, and its intensity was lower than the intensity of the amide II band.
3.2. Multivariate Analysis
When considering complex datasets including spectra of multiple different organs, an efficient data
analysis can be based on a full dataset (one common model) or its subsets comprising spectra of selected
organs (separate models). Tissuesfromdifferentorganshavedifferentchemical compositions, which are
reflected in the spectroscopic data. The difference in spectra of different organs was investigated by
Kondepati et al. and Dybas et al. [
51
,
61
], and the difference in the spectra of different tissue types
was studied by Barroso et al. and Ralbovsky and Lednev [
62
,
63
]. Therefore, each approach to the
multispectral analysis (MIR, NIR, combination) was represented by datasets including measurements
of one joint or several individual sample sets (colon, stomach, rectum sample sets as well as the full set
including all of them), which resulted 12 possible datasets in total.
Due to the manual contact of the probe with the sample surface, there are always minor variations
of the contact pressure during a measurement. These kinds of variations can influence the concentration
of water, hemoglobin, and lipids. Therefore, even the same kind of samples can have different overall
absorbance due to variance in the contact pressure. Reduced contact pressure results in higher diffuse
reflectance and scattering [
64
]. However, all these variations should not influence the spectral ratio
intensity between various peaks in the individual spectroscopic signal. To reduce scattering and
differences in the overall absorbance between different samples, scattering correction preprocessing
methods are usually implemented. In our study, a combination of scattering correction preprocessing
was applied to reduce the influence of the contact pressure variations. Every single dataset was tested
using different preprocessing methods before PLS-DA modelling: no preprocessing, SNV, second
derivative (2D), first derivative (1D), 2D +SNV (2D followed by SNV), 1D +SNV (1D followed by
SNV). This combinatorial diversity represents a particular challenge for finding the most efficient
modelling strategy.
Concatenated spectral data representing a combination of two spectroscopic methods were tested
by independently preprocessing both parts of the concatenated dataset with the same methods as for
the individual datasets. Both parts were normalised between 0 and 1, as described in Section 2.5 above.
Models with maximal accuracy were chosen to represent single methods (MIR, NIR) or combinations
of methods for each sample set. Single method models with the same preprocessings as was used for
the individual parts of the concatenated spectral data (combining two spectroscopic methods) are
provided in Table 2(models marked with *). The model loadings are displayed in the Supplementary
Materials accompanying the paper. ROC-curves were used to exclude subjectivity of the threshold
choice method during model comparison (threshold =0.5 was used for all models in Table 2.
Sensors 2020,20, 6706 10 of 18
Table 2. Comparison of spectroscopic and data preprocessing methods for cancer diagnostics.
#Method LV 1Pre-Processing
Calibration 5Cross-Validation
(Leave-One-Out)
Cross-Validation
(Monte Carlo)
%Se 2%Sp 3%Ac 4%Se 2%Sp 3%Ac 4%Se 2%Sp 3%Ac 4
Colon, Stomach, Rectum (CSR) samples set
1 NIR 5 2D, SNV 84 84 84 70 76 74 64 78 72
2 MIR 5 2D, SNV 82 81 82 74 78 76 70 70 70
3
Combination
52D, SNV |2D,
SNV 91 94 93 72 90 82 68 86 78
Colon samples set
4NIR 5 2D 92 89 90 72 81 77 62 74 69
-* 4 2D, SNV 80 81 81 64 74 69 61 75 68
5 MIR 5 2D, SNV 96 96 96 72 78 75 78 83 80
6
Combination
52D, SNV |2D,
SNV 100 96 98 92 93 92 84 96 90
Stomach samples set
7NIR 5 2D 90 94 92 85 94 90 84 92 89
-* 5 SNV 75 94 87 60 81 73 61 82 74
8 MIR 5 2D, SNV 90 97 94 80 94 88 78 92 87
9
Combination
5 SNV |2D, SNV 95 100 98 85 97 92 75 97 88
Rectum samples set
10 NIR 5 2D 100 100 100 92 67 81 87 58 75
11 MIR 5 2D 100 100 100 92 78 86 94 81 89
12
Combination
5 2D |2D 100 100 100 92 89 90 96 83 90
1Number of latent variables, 2Sensitivity, 3Specificity, 4Accuracy, 5Prediction on a dataset used for model calibration, * Additional models.
Sensors 2020,20, 6706 11 of 18
Two types of cross-validation (CV) were used to validate the models—Standard LOO CV and
Monte Carlo CV (see Table 2). These two cross-validation approaches differ in the size of the segment
chosen in each iteration of the testing cycle: one position in the case of LOO and 10% of the randomly
chosennumber of the total number of positions in Monte Carlo. Themorecommonlyusedleave-one-out
design resulted in more optimistic prediction statistics, while the results of Monte Carlo CV are more
reliable because a larger proportion of the data is allocated to the testing set than to the training
set when compared to LOO CV. However, both methods worked very similarly in this case for the
purpose of comparison between different methods and data blocks, as well as the calibration of
data-based prediction.
In general, the validation strategy used in the manuscript is dictated by the necessity to work with
a very limited (for ethical and other reasons) dataset. The limited availability of patient samples is one
of the main challenges in clinical studies such as this. Nevertheless, we consider this approach, using a
two-step validation, sufficient to reach the goals of the work: to investigate the influence of NIR and
MIR data combination on the model accuracy (the comparison of different methods of measurement
and data analysis), to show the presence of differences between healthy and cancerous tissues of
different organs, and to incentivize further efforts aimed at the development of combination methods.
The testing of different preprocessing methods resulted in the selection of the same procedures
(either 2D or SNV or their combination) for all datasets. It can be explained by their cumulative effect
on the spectra: using derivatives removed the linear variations of the baseline, whereas SNV eliminates
the variability of the overall spectral intensity that could be related to the experimental factors, e.g.,
to the difference of the effective measurement spot or volume.
It can be seen from Figures 1and 2that the shapes of MIR and NIR spectra are different for the three
considered organs as well as between normal and tumour samples. As such, a non-homogeneity in the
data is present; it adds extra complexity to the PLS-DA models (Table 2), which leads to a larger number
of latent variables. Kukreti et al. [
65
] proposed applying particular data preprocessing for analysis
that excludes a variation in data associated with the specific sample. In our previous research [
33
,
35
],
the importance of the choice of preprocessing and the importance of correctly combining different
parts of data was also noted.
As shown recently [
33
], the simultaneous use of data obtained by two different spectroscopic
techniques can increase the classification accuracy. A similar effect was observed in all our models
presented here (see models 3, 6, 9, and 12 in Table 2and Figure 3). It was also noted that not any pair of
methods results in a significant increase in accuracy compared to using these methods separately [
33
].
In this work, combining two techniques made it possible to obtain an increase in accuracy by 0–15%
(calibration and LOO cross-validation accuracy are considered) both on the full set and on subsets,
including measurements of individual organs (model #3 was compared with #1,2; #6 with #4,5; #9 with
#7,8 and #12 with #10,11). The synergy effect described above is presented in Figure 3. The curves
corresponding to models built using concatenated data (Figure 3c) are, in a majority of cases, closer to
(0,1) than curves in Figure 3a,b corresponding to models built using single method data. It is important
to note that the presented results were obtained from a limited number of samples, not covering
all possible variations in the data, which are related to both natural and disease-related differences.
Although the obtained research models can be inaccurate for new, undiagnosed clinical samples,
the available samples and datasets are well suited for the purpose of comparing the performances of
the suggested approaches to the joint data analysis.
Different divisions of the dataset were tested as well. In this case, using a separate model for each
respective organ type should be used instead of the joint model. For example, a model built using the
colon dataset was used for the discrimination of colon sample measurements etc. This “local” model
building approach increased the accuracy (see % acc column in Table 2) by 3–16% for NIR (model #1
was compared with #4,7,10), 0–18% for MIR (model #2 was compared with #5,8,11), and 5–10% for
the combination of methods (model #3 was compared with #6,9,12). The same trend is presented in
Figure 3a–c. The models built using single organ samples sets (blue, green, and pink lines) were better
Sensors 2020,20, 6706 12 of 18
than the “global” model obtained using the full set of samples (red line; except for the rectum model in
Figure 3a, whose corresponding dataset is more limited than the others).
Sensors 2020, 20, 6706 12 of 19
Figure 3. Receiver operating characteristic curve (ROC-curve) of prediction of Leave-one-out (LOO)
cross-validation (CV) for models from Table 2. (a) Models #1, 4, 7, 10 (models built using NIR data);
(b) models #2, 5, 8, 11 (models built using MIR data); (c) models #3, 6, 9, 12 (models built using
concatenated data).
Different divisions of the dataset were tested as well. In this case, using a separate model for
each respective organ type should be used instead of the joint model. For example, a model built
using the colon dataset was used for the discrimination of colon sample measurements etc. This
“local” model building approach increased the accuracy (see % acc column in Table 2) by 3–16% for
NIR (model #1 was compared with #4,7,10), 0–18% for MIR (model #2 was compared with #5,8,11),
and 5–10% for the combination of methods (model #3 was compared with #6,9,12). The same trend is
presented in Figure 3a–c. The models built using single organ samples sets (blue, green, and pink
lines) were better than the “global” model obtained using the full set of samples (red line; except for
the rectum model in Figure 3a, whose corresponding dataset is more limited than the others).
There are many peaks in the VIP values (see Figure 4) because derivative preprocessing for the
data was used. Each peak in the original data was recalculated resulting in several less broad peaks
by using the derivative. In Figure 5, we displayed the calibration data for models built using separate
methods with 2D preprocessing (calibration data for other models are displayed in the
Supplementary Materials accompanying the paper). It can be seen from Figure 4 that the intervals
harbouring the most pronounced values are different for all models built using NIR data (Figure 4a).
This includes models built using concatenated data (Figure 4c, left part), MIR data (Figure 4b), as well
as combination models (Figure 4c, right part). Despite differences in VIP values, there are several
common intervals: around 1742 cm−1, 1650 cm−1, and 1043 cm−1 in all models built using MIR data and
900–1000 nm, 1100–1200 nm, and 1300–1500 nm in all models built using NIR data. These findings
are equal to the differences between normal and tumour tissue measurements described in Section
3.1. Regarding models that were built using concatenated data from rectum samples, the VIP values
for NIR spectra (Figure 4c, left part) are less pronounced than the values for MIR spectra (Figure 4c,
right part). There is no significant difference between the calibration statistics (see Table 2) for this
organ built using only MIR or concatenated data. However, there are differences in LOO CV and
Monte Carlo CV, which indicates that using both validation methods results in more accurate
conclusions on the presence of synergies when both spectroscopic techniques are combined.
Figure 3.
Receiver operating characteristic curve (ROC-curve) of prediction of Leave-one-out (LOO)
cross-validation (CV) for models from Table 2. (
a
) Models #1, 4, 7, 10 (models built using NIR data);
(
b
) models #2, 5, 8, 11 (models built using MIR data); (
c
) models #3, 6, 9, 12 (models built using
concatenated data).
There are many peaks in the VIP values (see Figure 4) because derivative preprocessing for the
data was used. Each peak in the original data was recalculated resulting in several less broad peaks by
using the derivative. In Figure 5, we displayed the calibration data for models built using separate
methods with 2D preprocessing (calibration data for other models are displayed in the Supplementary
Materials accompanying the paper). It can be seen from Figure 4that the intervals harbouring the
most pronounced values are different for all models built using NIR data (Figure 4a). This includes
models built using concatenated data (Figure 4c, left part), MIR data (Figure 4b), as well as combination
models (Figure 4c, right part). Despite differences in VIP values, there are several common intervals:
around 1742 cm
−1
, 1650 cm
−1
, and 1043 cm
−1
in all models built using MIR data and 900–1000 nm,
1100–1200 nm, and 1300–1500 nm in all models built using NIR data. These findings are equal to the
differences between normal and tumour tissue measurements described in Section 3.1. Regarding
models that were built using concatenated data from rectum samples, the VIP values for NIR spectra
(Figure 4c, left part) are less pronounced than the values for MIR spectra (Figure 4c, right part).
There is no significant difference between the calibration statistics (see Table 2) for this organ built
using only MIR or concatenated data. However, there are differences in LOO CV and Monte Carlo CV,
which indicates that using both validation methods results in more accurate conclusions on the presence
of synergies when both spectroscopic techniques are combined.
The observed synergy effect is a result of using complementary information brought by two
physically different methods of spectroscopy. Evidently, a combination of the complementary
information about the tissue properties collected from two different methods and combined in a single
modelling approach led to an increase in accuracy of the multivariate discrimination models. In spite
of the gain from merging two optical spectroscopic techniques observed in the presented research,
an attempt to develop a joint discrimination model comprising several abdominal cancers, i.e., stomach,
colon, and rectal cancer, was found to be non-optimal for diagnostics in the presented case. Figure 3
and Table 2demonstrate that the accuracy obtained using three separate models, one for each specific
organ, is generally higher than the accuracy of the joint “global” model for all investigated organs.
This can be explained by the major spectral differences observed for the investigated organs (Figure 1).
Thus, for further medical applications, it is worth considering the strategy of multiple models rather
than using one common model for all abdominal cancer types.
Sensors 2020,20, 6706 13 of 18
Sensors 2020, 20, 6706 13 of 19
Figure 4. Variable importance in Projection (VIP) for models from Table 2. (a) Models #1, 4, 7, 10
(models built using NIR data); (b) models #2, 5, 8, 11 (models built using MIR data); (c) models #3, 6,
9, 12 (models built using concatenated data). Each curve was shifted up by 30% from the previous
curve on the y-axis for better visualization of the VIP values. Models built using full datasets (colon,
stomach, rectum: CSR) are shown in blue, red—colon datasets, yellow—stomach datasets and
violet—rectum datasets.
Figure 5. Calibration data (CD) of models built using single methods (before removing the mean
centre): (a) CD of model #5—MIR dataset pretreated by 2D, SNV including only colon measurements;
Figure 4.
Variable importance in Projection (VIP) for models from Table 2. (
a
) Models #1, 4, 7, 10
(models built using NIR data); (
b
) models #2, 5, 8, 11 (models built using MIR data); (
c
) models
#3, 6, 9, 12 (models built using concatenated data). Each curve was shifted up by 30% from the
previous curve on the y-axis for better visualization of the VIP values. Models built using full datasets
(colon, stomach, rectum: CSR) are shown in blue, red—colon datasets, yellow—stomach datasets and
violet—rectum datasets.
Sensors 2020, 20, 6706 13 of 19
Figure 4. Variable importance in Projection (VIP) for models from Table 2. (a) Models #1, 4, 7, 10
(models built using NIR data); (b) models #2, 5, 8, 11 (models built using MIR data); (c) models #3, 6,
9, 12 (models built using concatenated data). Each curve was shifted up by 30% from the previous
curve on the y-axis for better visualization of the VIP values. Models built using full datasets (colon,
stomach, rectum: CSR) are shown in blue, red—colon datasets, yellow—stomach datasets and
violet—rectum datasets.
Figure 5. Calibration data (CD) of models built using single methods (before removing the mean
centre): (a) CD of model #5—MIR dataset pretreated by 2D, SNV including only colon measurements;
Figure 5.
Calibration data (CD) of models built using single methods (before removing the mean
centre): (
a
) CD of model #5—MIR dataset pretreated by 2D, SNV including only colon measurements;
(
b
) CD of model #4—NIR dataset pretreated by 2D including only colon measurements; (
c
) CD of
model #8—MIR dataset pretreated by 2D, SNV including only stomach measurements; (
d
) CD of model
#7—NIR dataset pretreated by 2D including only stomach measurements; (
e
) CD of model #11—MIR
dataset pretreated by 2D including only rectum measurements; (
f
) CD of model #10—NIR dataset
pretreated by 2D including only rectum measurements.
Sensors 2020,20, 6706 14 of 18
4. Conclusions and Outlook
The main challenge for the development of multivariate models that could be incorporated into
a medical device lies in the investigation of their applicability limits. As a preliminary step of this
development, we compared models built on different sets of samples, including three separate organs:
colon, rectum and stomach, which are physiologically related. We demonstrated that the separate
models, which were trained to work with samples of one specific organ, are more accurate than the
“one common model”, which was trained to work with all considered organs. This statement can be
explained by the strong spectral differences observed for all three organs.
Considering possible future applications, a device including a combination of two selected
spectroscopic techniques will be able to operate in at least three modes. Based on the practical
application, those modes are comprised of (a) a detailed examination of the sample surface (based on
MIR direct spectral information), (b) a general in-depth tissue examination (based on NIR spectra),
and (c) a more advanced and comprehensive examination (based on concatenated MIR and NIR
spectra). The practical benefit of the detailed mode (a) capable of bringing information on the molecular
structures on a single-cell scale is the explicit justification of the classification result, which is an essential
feature of any medical device. This approach will be especially relevant for hollow organs, such as the
ones considered in this study, and it consists of the possibility of non-traumatically analysing tissues
before surgery in greater depth.
In addition to the reported improvements in the methods diagnostic potential by means of
combining MIR and NIR datasets, there are other promising features of the suggested approach that
can produce enormous benefits for the clinical application. We assume that one of the known critical
drawbacks of NIR spectroscopy, the necessity to perform an outlier detection step prior to the precise
spectral analysis [
66
], could be eliminated by using additional data of the MIR fingerprint region.
Empirically validated and accepted models based on MIR measurements will enable the development
of additional models for the surface analysis of specific tissue types, which could be used to choose an
appropriate NIR model for the subsequent deeper analysis of tissue.
Beyond these technical considerations, many additional questions remain to be addressed in future
studies, which would aim to translate a combined fibre-optic cancer detection probe into the clinics.
We would like to emphasize that all validation parameters in this study are calculated from a limited
sample set. Therefore, the specific values of sensitivity, specificity, and accuracy may not be maintained
in a larger sample set. Nonetheless, the approach of combining NIR and MIR should be applied in
further studies based on extended datasets, because it was shown that it yields better results than each
method individually. Although, differences in tissue properties between ex vivo samples used for the
training of chemometric classification models and
in vivo
tissue could potentially require an additional
adjustment of the modelling algorithms in order to obtain sufficient levels of accuracy and diagnostic
reliability. These differences could potentially be caused by the higher oxygenation and perfusion
levels of
in vivo
tissues. Furthermore, the requirement for probe sterility in an intraoperative setting,
the need to take repeated measurements in fixed positions, and cleaning the tip of the probe after each
measurement remain important challenges to clinical integration. Nevertheless, the introduction of
optical spectroscopic tools into clinical cancer diagnostics is a steady trend. Scientific investigations
by up-to-date spectroscopic techniques and modern methods of data analysis provide new in-depth
knowledge for the successful development of this promising approach.
Sensors 2020,20, 6706 15 of 18
Supplementary Materials:
The following are available online at http://www.mdpi.com/1424-8220/20/22/6706/s1,
Figure S1: Calibration data (CD), including data of the three organs, of models built using single methods (before
mean center removing): (a) CD of model# 2 - MIR data set pretreated by 2D, SNV; (b) CD of model# 1 - NIR
data set pretreated by 2D, SNV; Figure S2. Calibration data (CD), including data of the three organs, of model #3
built using concatenated data (before mean center removing): (a) MIR part of data set pretreated by 2D, SNV;
(b) NIR part of data set pretreated by 2D, SNV. Both parts normalized to [0,1]; Figure S3. Calibration data (CD) of
models built using concatenated data (before mean center removing): (a) CD of model# 6 - MIR part of data set
pretreated by 2D, SNV including only colon measurements; (b) CD of model# 6 - NIR part of data set pretreated
by 2D including only colon measurements; (c) CD of model# 9 - MIR part of data set pretreated by 2D, SNV
including only stomach measurements; (d) CD of model# 9 - NIR part of data set pretreated by SNV including
only stomach measurements; (e) CD of model# 12 - MIR part of data set pretreated by 2D including only rectum
measurements; (f) CD of model# 12 - NIR part of data set pretreated by 2D including only rectum measurements;
Figure S4. X-loadings of models from Table 2: (a) model# 1; (b) model# 2; (c) model# 4; (d) model# 5; (e) model# 7;
(f) model# 8; (g) model# 10; (h) mode # 11. Red color corresponding 1st lv, light green - 2nd lv, green - 3rd lv,
blue - 4th lv, violet - 5th lv. Each curve was shifted up by 30% from the previous curve on the y-axis for better
visualization of the loading plots; Figure S5. X-loadings of models from Table 2: (a,b) model# 3; (c,d) model# 6;
(e,f) model# 9; (g,h) model# 12; There are two parts of each plot corresponding to the NIR (a,c,e,g) and MIR
(b,d,f,h) parts of concatenated data. Red color corresponding 1st lv, light green - 2nd lv, green - 3rd lv, blue - 4th lv,
violet - 5th lv. Each curve was shifted up by 30% from the previous curve on the y-axis for better visualization of
the loading plots.
Author Contributions:
Conceptualisation, A.B., E.F. and V.A.; Formal analysis, A.B., V.B. and L.P.; Investigation,
O.B. and U.Z.; Methodology, T.H., U.Z. and O.M.; Project administration, U.Z. and O.B.; Resources, I.U., T.S., B.R.
and V.A.; Supervision, O.M., B.R. and V.A.; Validation, T.H., O.B., U.Z. and I.U.; Writing—original draft, O.B. and
V.B.; Writing—review and editing, T.H., A.B. and L.P. All authors have read and agreed to the published version
of the manuscript.
Funding:
The work of V.B. and A.B. on data evaluation and analysis and multivariate model building was
supported by the Ministry of Education and Science of the Russian Federation within the framework of state task
No. 0778-2020-0005.
Acknowledgments: We thank Beate Rau for leading and supervising the project at the Charité.
Conflicts of Interest: The authors declare no conflict of interest.
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