Correlation of X-ray diffraction signatures of breast tissue and their histopathological classification

Robert M. Moss; Amany S. Amin; Chiaki Crews; Colin Purdie; Lee Jordan; Francesco Iacoviello; Andrew Evans; Robert D.  Speller; Sarah Vinnicombe

doi:10.1038/s41598-017-13399-9

Correlation of X-ray diffraction signatures of breast tissue and their histopathological classification

Robert M. Moss (Lead / Corresponding author)
, Amany S. Amin
, Chiaki Crews
, Colin Purdie
, Lee Jordan
, Francesco Iacoviello
, Andrew Evans
, Robert D. Speller
, Sarah Vinnicombe

Research output: Contribution to journal › Article › peer-review

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Abstract

This pilot study examines the correlation of X-ray diffraction (XRD) measurements with the histopathological analysis of breast tissue. Eight breast cancer samples were investigated. Each sample contained a mixture of normal and cancerous tissues. In total, 522 separate XRD measurements were made at different locations across the samples (8 in total). The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine if there were any distinguishing features that could be used to identify different tissue components. 99.0% of the variation between the spectra were described by the first two principal components (PC). Comparing the location of points in PC space with the classification determined by histopathology indicated correlation between the shape/magnitude of the XRD spectra and the tissue type. These results are encouraging and suggest that XRD could be used for the intraoperative or postoperative classification of bulk tissue samples.

Original language	English
Article number	12998
Pages (from-to)	1-9
Number of pages	9
Journal	Scientific Reports
Volume	7
DOIs	https://doi.org/10.1038/s41598-017-13399-9
Publication status	Published - 11 Oct 2017

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SDG 3 Good Health and Well-being

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Cite this

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title = "Correlation of X-ray diffraction signatures of breast tissue and their histopathological classification",

abstract = "This pilot study examines the correlation of X-ray diffraction (XRD) measurements with the histopathological analysis of breast tissue. Eight breast cancer samples were investigated. Each sample contained a mixture of normal and cancerous tissues. In total, 522 separate XRD measurements were made at different locations across the samples (8 in total). The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine if there were any distinguishing features that could be used to identify different tissue components. 99.0\% of the variation between the spectra were described by the first two principal components (PC). Comparing the location of points in PC space with the classification determined by histopathology indicated correlation between the shape/magnitude of the XRD spectra and the tissue type. These results are encouraging and suggest that XRD could be used for the intraoperative or postoperative classification of bulk tissue samples.",

author = "Moss, \{Robert M.\} and Amin, \{Amany S.\} and Chiaki Crews and Colin Purdie and Lee Jordan and Francesco Iacoviello and Andrew Evans and Speller, \{Robert D.\} and Sarah Vinnicombe",

note = "Funding: Breast Cancer Now Pilot Grant (2014NovSP459); Engineering and Physical Sciences Research Council (EPSRC) grant EP/M028100.",

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doi = "10.1038/s41598-017-13399-9",

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T1 - Correlation of X-ray diffraction signatures of breast tissue and their histopathological classification

AU - Moss, Robert M.

AU - Amin, Amany S.

AU - Crews, Chiaki

AU - Purdie, Colin

AU - Jordan, Lee

AU - Iacoviello, Francesco

AU - Evans, Andrew

AU - Speller, Robert D.

AU - Vinnicombe, Sarah

N1 - Funding: Breast Cancer Now Pilot Grant (2014NovSP459); Engineering and Physical Sciences Research Council (EPSRC) grant EP/M028100.

PY - 2017/10/11

Y1 - 2017/10/11

N2 - This pilot study examines the correlation of X-ray diffraction (XRD) measurements with the histopathological analysis of breast tissue. Eight breast cancer samples were investigated. Each sample contained a mixture of normal and cancerous tissues. In total, 522 separate XRD measurements were made at different locations across the samples (8 in total). The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine if there were any distinguishing features that could be used to identify different tissue components. 99.0% of the variation between the spectra were described by the first two principal components (PC). Comparing the location of points in PC space with the classification determined by histopathology indicated correlation between the shape/magnitude of the XRD spectra and the tissue type. These results are encouraging and suggest that XRD could be used for the intraoperative or postoperative classification of bulk tissue samples.

AB - This pilot study examines the correlation of X-ray diffraction (XRD) measurements with the histopathological analysis of breast tissue. Eight breast cancer samples were investigated. Each sample contained a mixture of normal and cancerous tissues. In total, 522 separate XRD measurements were made at different locations across the samples (8 in total). The resulting XRD spectra were subjected to principal component analysis (PCA) in order to determine if there were any distinguishing features that could be used to identify different tissue components. 99.0% of the variation between the spectra were described by the first two principal components (PC). Comparing the location of points in PC space with the classification determined by histopathology indicated correlation between the shape/magnitude of the XRD spectra and the tissue type. These results are encouraging and suggest that XRD could be used for the intraoperative or postoperative classification of bulk tissue samples.

U2 - 10.1038/s41598-017-13399-9

DO - 10.1038/s41598-017-13399-9

M3 - Article

C2 - 29021531

SN - 2045-2322

VL - 7

SP - 1

EP - 9

JO - Scientific Reports

JF - Scientific Reports

M1 - 12998

ER -

Correlation of X-ray diffraction signatures of breast tissue and their histopathological classification

Abstract

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