University of Oulu

Ivanov D, Dremin V, Genova T, Bykov A, Novikova T, Ossikovski R and Meglinski I (2022) Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning. Front. Phys. 9:814787. doi:10.3389/fphy.2021.814787

Polarization-based histopathology classification of ex vivo colon samples supported by machine learning

Saved in:
Author: Ivanov, Deyan1; Dremin, Viktor2,3; Genova, Tsanislava4;
Organizations: 1LPICM, CNRS, École Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
2Research and Development Center of Biomedical Photonics, Orel State University, Orel, Russia
3College of Engineering and Physical Sciences, Aston University, Birmingham, United Kingdom
4Bulgarian Academy of Sciences, Institute of Electronics, Sofia, Bulgaria
5Optoelectronics and Measurement Techniques Unit, University of Oulu, Oulu, Finland
6Department of Biomedical Engineering, Florida International University, Miami, FL, United States
7Institute of Clinical Medicine N.V. Sklifosovsky, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
8Immanuel Kant Baltic Federal University, Kaliningrad, Russia
9V.A. Negovsky Scientific Research Institute of General Reanimatology, Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022060844741
Language: English
Published: Frontiers Media, 2022
Publish Date: 2022-08-17
Description:

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 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.

see all

Series: Frontiers in physics
ISSN: 2296-424X
ISSN-E: 2296-424X
ISSN-L: 2296-424X
Volume: 9
Article number: 814787
DOI: 10.3389/fphy.2021.814787
OADOI: https://oadoi.org/10.3389/fphy.2021.814787
Type of Publication: A1 Journal article – refereed
Field of Science: 114 Physical sciences
Subjects:
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.
Academy of Finland Grant Number: 325097
Detailed Information: 325097 (Academy of Finland Funding decision)
Copyright information: © 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.
  https://creativecommons.org/licenses/by/4.0/