Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination

Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel us...

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Main Authors: José Luis Ganoza-Quintana, Félix Fanjul-Vélez, José Luis Arce-Diego
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/11/13/6142
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spelling doaj-c57119b98e39438faf2d78e4395bbdbf2021-07-15T15:30:48ZengMDPI AGApplied Sciences2076-34172021-07-01116142614210.3390/app11136142Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues DiscriminationJosé Luis Ganoza-Quintana0Félix Fanjul-Vélez1José Luis Arce-Diego2Biomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainBiomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainBiomedical Engineering Group, TEISA Department, University of Cantabria, Av de los Castros 46, 39005 Santander, SpainHistology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10×, 20×, and 40× magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (<i>p</i>-value < 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The <i>p</i>-value of the most significant parameters is below 10<sup>−5</sup>. Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10× fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis.https://www.mdpi.com/2076-3417/11/13/6142digital histologyphase contrast imagingbiomarkersbiomedical opticsfractal analysis
collection DOAJ
language English
format Article
sources DOAJ
author José Luis Ganoza-Quintana
Félix Fanjul-Vélez
José Luis Arce-Diego
spellingShingle José Luis Ganoza-Quintana
Félix Fanjul-Vélez
José Luis Arce-Diego
Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
Applied Sciences
digital histology
phase contrast imaging
biomarkers
biomedical optics
fractal analysis
author_facet José Luis Ganoza-Quintana
Félix Fanjul-Vélez
José Luis Arce-Diego
author_sort José Luis Ganoza-Quintana
title Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
title_short Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
title_full Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
title_fullStr Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
title_full_unstemmed Digital Histology by Phase Imaging Specific Biomarkers for Human Tumoral Tissues Discrimination
title_sort digital histology by phase imaging specific biomarkers for human tumoral tissues discrimination
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2021-07-01
description Histology is the diagnosis gold standard. Conventional biopsy presents artifacts, delays, or human bias. Digital histology includes automation and improved diagnosis. It digitalizes microscopic images of histological samples and analyzes similar parameters. The present approach proposes the novel use of phase contrast in clinical digital histology to improve diagnosis. The use of label-free fresh tissue slices prevents processing artifacts and reduces processing time. Phase contrast parameters are implemented and calculated: the external scale, the fractal dimension, the anisotropy factor, the scattering coefficient, and the refractive index variance. Images of healthy and tumoral samples of liver, colon, and kidney are employed. A total of 252 images with 10×, 20×, and 40× magnifications are measured. Discrimination significance between healthy and tumoral tissues is assessed statistically with ANOVA (<i>p</i>-value < 0.005). The analysis is made for each tissue type and for different magnifications. It shows a dependence on tissue type and image magnification. The <i>p</i>-value of the most significant parameters is below 10<sup>−5</sup>. Liver and colon tissues present a great overlap in significant phase contrast parameters. The 10× fractal dimension is significant for all tissue types under analysis. These results are promising for the use of phase contrast in digital histology clinical praxis.
topic digital histology
phase contrast imaging
biomarkers
biomedical optics
fractal analysis
url https://www.mdpi.com/2076-3417/11/13/6142
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