Abstract
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 vivocolon 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.
Highlights
Ellipsometry and polarimetry have established their duly and justified realm for material characterization [1,2,3,4,5,6]
Δ, S, PΔ, and PI were removed from the main model since they are derived from di, λi, and Pi, and according to the Mann–Whitney test, their data for both health conditions are drawn from the same distribution
To [25, 26, 46], where a single, human colon specimen and tumor grade were considered for binary classification of all measured sites, in the current study the same experimental approach was extended for multiple colon samples and tumor grades, respectively
Summary
Ellipsometry and polarimetry have established their duly and justified realm for material characterization [1,2,3,4,5,6]. Unlike skin cancer, whose origins could be detected at an earlier stage of development due to its presence predominantly in the areas of the human body available for direct visual inspection, colon cancer is localized and diagnosed out of straight sight of notice often at a later stage of development [14]. Such an inevitable obstacle could be overcome by adopting various multimodal optical techniques for providing adequate support to clinicians [15,16,17,18,19]. The performance of each ML algorithm with each of the submodels was evaluated by means of computing the corresponding confusion matrix, areas under the curves (AUC), and loss and relative risk calculations related to misclassifications
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