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. 2019 Dec 24;9(1):19795.
doi: 10.1038/s41598-019-56308-y.

Raman spectroscopy as a non-invasive diagnostic technique for endometriosis

Ugur Parlatan  1 , Medine Tuna Inanc  2 , Bahar Yuksel Ozgor  3 , Engin Oral  4 , Ercan Bastu  5 , Mehmet Burcin Unlu  2 , Gunay Basar  6
Affiliations

Raman spectroscopy as a non-invasive diagnostic technique for endometriosis

Ugur Parlatan et al. Sci Rep. .

Abstract

Endometriosis is a condition in which the endometrium, the layer of tissue that usually covers the inside of the uterus, grows outside the uterus. One of its severe effects is sub-fertility. The exact reason for endometriosis is still unknown and under investigation. Tracking the symptoms is not sufficient for diagnosing the disease. A successful diagnosis can only be made using laparoscopy. During the disease, the amount of some molecules (i.e., proteins, antigens) changes in the blood. Raman spectroscopy provides information about biochemicals without using dyes or external labels. In this study, Raman spectroscopy is used as a non-invasive diagnostic method for endometriosis. The Raman spectra of 94 serum samples acquired from 49 patients and 45 healthy individuals were compared for this study. Principal Component Analysis (PCA), k- Nearest Neighbors (kNN), and Support Vector Machines (SVM) were used in the analysis. According to the results (using 80 measurements for training and 14 measurements for the test set), it was found that kNN-weighted gave the best classification model with sensitivity and specificity values of 80.5% and 89.7%, respectively. Testing the model with unseen data yielded a sensitivity value of 100% and a specificity value of 100%. To the best of our knowledge, this is the first study in which Raman spectroscopy was used in combination with PCA and classification algorithms as a non-invasive method applied on blood sera for the diagnosis of endometriosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
(a) Background (BG) and baseline-corrected (BC) Raman spectra of a serum sample. (b) Normalized BC mean Raman spectra of the control and patient groups. Standard deviations of each group were plotted and overlaid as shaded curves.
Figure 2
Figure 2
PCA performance on the training data set, which includes normalized BC data from 41 patients and 39 healthy individuals. (a) PCA score plot (PC1 vs. PC3) (b) Loading 1 and Loading 3 spectra.
Figure 3
Figure 3
The experimental arrangement for Raman spectroscopy.
Figure 4
Figure 4
Infographic for pre-processing and the data analysis.
See this image and copyright information in PMC

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