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.
|Journal||Frontiers in Physics|
|Publication status||Published - 24 Jan 2022|
Bibliographical note© 2022 Ivanov, Dremin, Genova, Bykov, Novikova, Ossikovski and Meglinski. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Funding: The experimental investigations were supported by the Bulgarian National Science Fund under grant #KP06-N38/13/2019. The current research was supported by the Academy of Finland (Grants 314639 and 325097) and INFOTECH strategic funding. Prof. Igor Meglinski also acknowledges the support from the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of world-class research centres “Digital Biodesign and Personalized Healthcare” No. 075-15-2020-926. VD kindly acknowledges for personal support from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant, agreement No. 839888.
- tissue polarimetry
- Mueller matrices
- physical realizability
- symmetric decomposition
- depolarization spaces
- Ex vivo colon samples
- machine learning