This work is dedicated to the diagnosis and grading of colon cancer by a combined use of Poincaré sphere and 2D Stokes vector polarimetry mapping approaches. The major challenge consists in exploring the applicability of polarized light for non-invasive screening of the histological abnormalities within the samples of biological tissues. Experimental studies were conducted in ex vivo colon sample, excised after surgical procedure for colon tumour removal of G2-adenocarcinoma lesion. Polarimetric measurements in linear and circular regime were carried via personally developed polarimetric, optical set-up, using supercontinuous fibre laser with irradiation fixed at 635 nm. We apply the Poincaré sphere and two-dimensional Stokes vector scanning approach for screening the corresponding tissue samples. A comparison between linear and circular polarization states is made both for quantitative and qualitative evaluations. It is shown that circular polarization has better diagnostic capabilities than linear polarization, with higher dynamic ranges of the polarimetric parameters and better values of the diagnostic quantities. In addition to the standard polarimetry parameters, utilized as essential diagnostic markers, we apply statistical analysis to obtain more detailed information in frame of the applied diagnostic approach.
In biophotonics, novel techniques and approaches are being constantly sought to assist medical doctors and to increase both sensitivity and specificity of the existing diagnostic methods. In such context, tissue polarimetry holds promise to become a valuable optical diagnostic technique as it is sensitive to tissue alterations caused by different benign and malignant formations. In our studies, multiple Mueller matrices were recorded for formalin-fixed, human, ex vivo colon specimens containing healthy and tumor zones. The available data were pre-processed to filter noise and experimental errors, and then all Mueller matrices were decomposed to derive polarimetric quantities sensitive to malignant formations in tissues. In addition, the Poincaré sphere representation of the experimental results was implemented. We also used the canonical and natural indices of polarimetric purity depolarization spaces for plotting our experimental data. A feature selection was used to perform a statistical analysis and normalization procedure on the available data, in order to create a polarimetric model for colon cancer assessment with strong predictors. Both unsupervised (principal component analysis) and supervised (logistic regression, random forest, and support vector machines) machine learning algorithms were used to extract particular features from the model and for classification purposes. The results from logistic regression allowed to evaluate the best polarimetric quantities for tumor detection, while the use of random forest yielded the highest accuracy values. Attention was paid to the correlation between the predictors in the model as well as both losses and relative risk of misclassification. Apart from the mathematical interpretation of the polarimetric quantities, the presented polarimetric model was able to support the physical interpretation of the results from previous studies and relate the latter to the samples’ health condition, respectively.
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