1. Multi-mode fiber spectroscopy for cancer diagnostics
Anastasiia Melenteva1, Valeria Belikova1, Olga Bibikova2, Urszula Zabarylo2, Viacheslav Artyushenko2, Andrey Bogomolov1,, Thaddäus Hocotz3
1 Samara State Technical University, Samara, Russia
2 art photonics GmbH, Berlin, Germany
3 Charité - Universitätsmedizin Berlin, Germany
This research is a one part of the big German project of “art photonics” company, which called “Fomos”. All data, presented here, were received in Berlin. Our laboratory was engaged in data analysis.

2. Cancer statistics
Estimated age-standardized rates (World) of incident cases, both sexes, all cancers excluding non-melanoma skin cancer, worldwide in 2012*
One of the most common cancers for several decades is tumors of the gastrointestinal tract.
According to the data reported by the Globocan in 2012, the mortality from stomach cancer ..
It is very important to detect cancer in an early stage to improve survival rates for cancer patients.
Detection of the tumor border zone is also an essential task to lower the mortality rate after surgical treatment.

3. Cancer diagnostics Fluoroscopy Gastroscopy Colonoscopy Ultrasound
Cancer is diagnosed through a variety of techniques. In the clinical praxis there are also a few blood tests, which detect cancer-related antigens, but these are far from being definitive.
these methods are time-consuming, very expensive and their success depends on the correct diagnostics.
Novel, time-saving, non-invasive and simple methods is a pressing need in the modern cancer diagnostics.
Computed tomography

4. Multi-mode fiber spectroscopy
Advantages of multi-mode fiber spectroscopy
flexible
powerful
high speed of analysis
non-invasive
in vivo
removes subjectivity in determining the diagnosis
Using fiber optic probes is a very powerful and flexible method for non-invasive in vivo applications, which is also delete the subjectivity valuation of doctors.
Methods using optical spectroscopy are well equipped to enable fast and reliable measurements of a high number of patients.
A. Bogomolov, V. Belikova, U. J. Zabarylo, O. Bibikova, and etc.. Synergy Effect of Combining Fluorescence and Mid Infrared Fiber Spectroscopy for Kidney Tumor Diagnostics. Sensors 17 (2017) 2548.

5. Spectroscopy methods Method\Factor FT-IR ATR Vis/NIR Fluorescence
Raman
Selectivity
high
low
Sale price
 high
small-medium
  small-medium
Penetration depth
small
medium
 small
The main issues
measurement slowness by FT-IR
measurement reproducibility problems
low information content
too weak Raman signals
Four key spectroscopy methods can be compared to select the best one or their best combination for the most sensitive, specific and accurate way to detect tumor margins.
MIR spectroscopy is based on the fundamental vibration frequencies. However, it also requires a very high level of automation, which makes IR-analyzers and their maintenance extremely expensive.
The penetration depth is also different for each methods. Due to the nonuniform of tissues different spectroscopy methods may give different information about tissue. Therefore, the combination of these method also could be tested and it results could be better compared with single method results due to the synergy effect.

6. Fiber Optic Molecular Sensor
Collaboration: OAP-laser team of Technical University Berlin & art photonics
Basic concept: simultaneous application of MIR, NIR, Raman and Fluorescence - for a precise and reliable cancer recognition
Main goal: to develop universal fiber probe to detect tumor tissues focused on minimal-invasive diagnostics
Each techniques is a potential source of information about tissue chemistry and morphology. Methods are chosen in such a way that they can potentially complement each other.
The spectroscopic methods used in the project are also suited for applications in bioanalytics, food analytics and environmental analysis.

7. Experimental The main issues: Tissue 3 Fluids -
Object of study
Number of patients
MIR
NIR
Fluorescence
Tissue
Colon 4 3
Fluids
Plasma 22 -
The main issues:
Difficult to receive samples
Different type of tissues
Difference between patients(age, male/female and etc)
The main experimental goal is to compare different spectroscopic methods ...
Two type of objects were investigated: … by …. The Raman spectra in the high wavenumber region were excited with a 690 nm laser.
Plasma has only MIR and NIR. Both plasma datasets consist of 22 patients.
As you can see from the table colon datasets have different number of patients, therefore, it complicate a comparison between middle and near IR spectroscopy and Fluorescence with Raman. Since the tissue is nonuniform in itself, for each sample spectra were taken in different positions.
This kind of analysis has some problems, which connected with difficulty to collect samples, different type of tissues and difference between patients..
Coordinate with doctors
Plasma MIR – 110, NIR -140
MIR – 65, NIR -137 F – 67 R - 48

8. Multivariate data analysis
Preliminary analysis
PCA
Preprocessing
Raw
SNV
Filter Savitzky-Golay
Discriminant analysis
PLS-DA
Cross validation:
Cross-class by patient
Cross-class by position
Model statistics:
Accuracy
Sensitivity
Specificity
PLS-DA have been built for discrimination on two classes – non-tumor and tumor.
Classical pretreatments, such as SNV, filter Savitzky-Golay and others have used for the analysis.
Segmented cross-validation has been used for the validation models. The segments were formed by “the position class” or by “the patient class. A standard set of statistics was used to estimate model quality, such as accuracy; Sensitivity and specificity.
In the "strict" version, two (or more) models can be compared with each other if they were built on the same set of samples and positions.
Multivariate data analysis was performed on: (SamGTU, Samara, Russia);
Interval selection Toolbox (SamGTU) working under MATLAB R2008b (The MathWorks™ Inc., Natick, MA, USA).

9. Multivariate data analysis
Condition positive
(Tumor)
Condition negative
(Normal)
Predicted condition positive
True Positive (TP)
False Positive (FP)
Predicted condition negative
False Negative (FN)
True Negative (TN)
Accuracy = (TP+TN)/(TP+FP+TN+FN)
Sensitivity (Sns) =
TP/(TP+FP)
Specificity (Spc) =
TN/(TN+FN)
Here you can see the formulae for accuracy, Sns, spc and how the true positive and true negative are defined. Cancer was considered as “positive” and health as “negative” test results, coded as 1 and 0, respectively. Sns reflects the share of false positives and is therefore important for reliability of negative diagnosis.
Accuracy - The most common statistics, the maximum and the best value - 100%. Shows how "in general" the model correctly classifies measurements into two classes.
Sensitivity - The second most important statistics, the maximum and the best value - 100%. Shows the proportion of correctly classified measurements of “the tumor class”.
Specificity - The third most important statistics, the maximum and the best value - 100%. Shows the proportion of correctly classified measurements of “the normal class”.
DQ2 - Used in the case of the same values of the remaining statistics, the maximum and the best value Shows the correlation of the predicted value (before referring to one of the classes) with the original “the normal class”.

10. MIR vs NIR: colon ex-vivo
Fluorescence
Spectra (SNV)
PLS-DA
Statistics (CV)
Sns
Spc
Acc
MIR
0.83
0.74
0.78
NIR
0.82
0.84
In preprocessed spectra is minimal difference therefore separation is based on minor features. Nevertheless, PLS-DA statistics shows relatively good results. In the presented case MIR works a little bit better for higher sensitivity
CV: «cross-class unique position»
PCA and PLS-DA preprocessing - 2D2.25 (Filter Savitzky-Golay)

11. Fluorescence vs Raman: colon ex-vivo
Spectra
PLS-DA
Statistics (CV)
Sns
Spc
Acc
Fluorescence
0.91
0.97
0.94 1
0.85
0.92
SNV
Raw
Here we can see that PLS-DA model for raman is better in accordance with sensitivity. Also we can see the clearly separation on the spectra
CV: «cross-class unique position»
PCA and PLS-DA preprocessing - 2D2.25 (Filter Savitzky-Golay)

12. MIR vs NIR: Plasma Spectra PLS-DA Statistics (CV) Sns Spc Acc MIR 1
0.57
0.79
Both datasets have 3 healthy group – patients before and after operation and non-tumor patients
the carbohydrate metabolism
CV: «cross-class patient», dataset: Normal & before operation data
PLS-DA preprocessing for NIR dataset: 0D2.25 (Filter Savitzky-Golay)

13. Further development Software interface
Developed spectral fiber sensors for tumor margins detection in vivo
Software interface
inactivated
normal samples
tumor samples
Spectral fiber sensors should be developed to use them for tumor margins detection in vivo.
The ultimate goal is to develop a tool for the doctor which will determine the tumor.
The system does not make decisions for the doctor, but helps him make decisions, providing additional information. Accordingly, the software is already being developed. Expect, in addition to the answer yes or not cancer, has a answer“margin" , which assumes a high probability of error.
The model is supplied to the input as a parameter.
margin
margin
A. Bogomolov, U. Zabarylo, D. Kirsanov, V. Belikova, V. Ageev, I. Usenov, V. Galyanin, O. Minet, T. Sakharova, G. Danielyan, E. Feliksberger, V. Artyushenko Sensors, 17(8), 2017: 1914.
Helps the doctor to make a decision!

14. Conclusions
Each of the studied spectroscopic methods exhibits a potential usefulness for cancer recognition
The investigated methods could be complementary to each other
Results provide a starting point towards the development of a multimodal instrument for the detection of cancer
Collection of a representative database is needed for reliable modelling

15. Thank you for your attention!
Acknowledgements
Thank you for your attention!