University of Oulu

Ushenko, V.A., Hogan, B.T., Dubolazov, A. et al. 3D Mueller matrix mapping of layered distributions of depolarisation degree for analysis of prostate adenoma and carcinoma diffuse tissues. Sci Rep 11, 5162 (2021). https://doi.org/10.1038/s41598-021-83986-4

3D Mueller matrix mapping of layered distributions of depolarisation degree for analysis of prostate adenoma and carcinoma diffuse tissues

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Author: Ushenko, Volodymyr A.1; Hogan, Benjamin T.2; Dubolazov, Alexander1;
Organizations: 1Optics and Publishing Department, Chernivtsi National University, 2 Kotsiubynskyi Str., Chernivtsi 58012, Ukraine
2Optoelectronics and Measurement Techniques Laboratory, University of Oulu, 90014 Oulu, Finland
3Institute of Clinical Medicine N.V. Sklifosovsky, I.M. Sechenov First Moscow State Medical University, Moscow, Russia 129090
4Institute of Engineering Physics for Biomedicine (PhysBio), National Research Nuclear University (MEPhI), Moscow, Russia 115409
5College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK.
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021050628984
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-05-06
Description:

Abstract

Prostate cancer is the second most common cancer globally in men, and in some countries is now the most diagnosed form of cancer. It is necessary to differentiate between benign and malignant prostate conditions to give accurate diagnoses. We aim to demonstrate the use of a 3D Mueller matrix method to allow quick and easy clinical differentiation between prostate adenoma and carcinoma tissues with different grades and Gleason scores. Histological sections of benign and malignant prostate tumours, obtained by radical prostatectomy, were investigated. We map the degree of depolarisation in the different prostate tumour tissues using a Mueller matrix polarimeter set-up, based on the superposition of a reference laser beam with the interference pattern of the sample in the image plane. The depolarisation distributions can be directly related to the morphology of the biological tissues. The dependences of the magnitude of the 1st to 4th order statistical moments of the depolarisation distribution are determined, which characterise the distributions of the depolarisation values. To determine the diagnostic potential of the method three groups of histological sections of prostate tumour biopsies were formed. The first group contained 36 adenoma tissue samples, while the second contained 36 carcinoma tissue samples of a high grade (grade 4: poorly differentiated—4 + 4 Gleason score), and the third group contained 36 carcinoma tissue samples of a low grade (grade 1: moderately differentiated—3 + 3 Gleason score). Using the calculated values of the statistical moments, tumour tissues are categorised as either adenoma or carcinoma. A high level (> 90%) accuracy of differentiation between adenoma and carcinoma samples was achieved for each group. Differentiation between the high-grade and low-grade carcinoma samples was achieved with an accuracy of 87.5%. The results demonstrate that Mueller matrix mapping of the depolarisation distribution of prostate tumour tissues can accurately differentiate between adenoma and carcinoma, and between different grades of carcinoma. This represents a first step towards the implementation of 3D Mueller matrix mapping for clinical analysis and diagnosis of prostate tumours.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 11
Issue: 1
Article number: 5162
DOI: 10.1038/s41598-021-83986-4
OADOI: https://oadoi.org/10.1038/s41598-021-83986-4
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work received funding from: the ATTRACT project funded by the EC under Grant Agreement 777222; Academy of Finland (Grants 314639 and 325097); National Research Foundation of Ukraine, Project 2020.02/0061; and INFOTECH strategic funding. I.M. also acknowledges partial support from MEPhI Academic Excellence Project (Contract No. 02.a03.21.0005), and the National Research Tomsk State University Academic D.I. Mendeleev Fund Program.
Copyright information: © The Authors 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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