Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)

Abstract Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it...

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Autores principales:	Hassaan Majeed, Tan Huu Nguyen, Mikhail Eugene Kandel, Andre Kajdacsy-Balla, Gabriel Popescu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1ba3e942909a401280d138ff0f6924eb
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spelling	oai:doaj.org-article:1ba3e942909a401280d138ff0f6924eb2021-12-02T15:07:50ZLabel-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)10.1038/s41598-018-25261-72045-2322https://doaj.org/article/1ba3e942909a401280d138ff0f6924eb2018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25261-7https://doaj.org/toc/2045-2322Abstract Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.Hassaan MajeedTan Huu NguyenMikhail Eugene KandelAndre Kajdacsy-BallaGabriel PopescuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hassaan Majeed Tan Huu Nguyen Mikhail Eugene Kandel Andre Kajdacsy-Balla Gabriel Popescu Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
description	Abstract Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects −34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
format	article
author	Hassaan Majeed Tan Huu Nguyen Mikhail Eugene Kandel Andre Kajdacsy-Balla Gabriel Popescu
author_facet	Hassaan Majeed Tan Huu Nguyen Mikhail Eugene Kandel Andre Kajdacsy-Balla Gabriel Popescu
author_sort	Hassaan Majeed
title	Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
title_short	Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
title_full	Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
title_fullStr	Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
title_full_unstemmed	Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)
title_sort	label-free quantitative evaluation of breast tissue using spatial light interference microscopy (slim)
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/1ba3e942909a401280d138ff0f6924eb
work_keys_str_mv	AT hassaanmajeed labelfreequantitativeevaluationofbreasttissueusingspatiallightinterferencemicroscopyslim AT tanhuunguyen labelfreequantitativeevaluationofbreasttissueusingspatiallightinterferencemicroscopyslim AT mikhaileugenekandel labelfreequantitativeevaluationofbreasttissueusingspatiallightinterferencemicroscopyslim AT andrekajdacsyballa labelfreequantitativeevaluationofbreasttissueusingspatiallightinterferencemicroscopyslim AT gabrielpopescu labelfreequantitativeevaluationofbreasttissueusingspatiallightinterferencemicroscopyslim
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Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM)

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