Breast cancer analysis by confocal energy dispersive micro-XRD

Escudero, Rodrigo Oscar; Cabral, María C.; Valladares, Mariana; Franco, Maria Amélia Santoro; Pérez, Roberto D.

doi:10.1039/c9ay02183c

Cited by 2 publications

(4 citation statements)

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“…[11][12][13][14][15] These imaging methods, including energy-dispersive and angular-dispersive raster scanning measurements, coherent scattering computed tomography, and coded aperture systems, seek to measure the XRD signatures of materials throughout a large field of view within reasonable scan times while maintaining adequate spatial resolutions for imaging applications. While simulation studies are often conducted for the design and optimization of these novel imaging architectures, [16][17][18][19] experimental validations on biologically relevant phantoms [20][21][22][23] and real biospecimens 3,6,8,13,15,24 have also occurred. These results have shown that tissues can be differentiated by spatially resolved XRD measurements; however, the resulting rich spatio-spectral datasets necessitate automatic data processing and classification techniques to parse the large data volume.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…XRD imaging approaches have been developed that seek to circumvent sample preparation, which is often destructive to samples and their spatial correlations, while enabling faster measurements with larger fields of view. [11][12][13][14][15] These imaging methods, including energy-dispersive and angular-dispersive raster scanning measurements, coherent scattering computed tomography, and coded aperture systems, seek to measure the XRD signatures of materials throughout a large field of view within reasonable scan times while maintaining adequate spatial resolutions for imaging applications. While simulation studies are often conducted for the design and optimization of these novel imaging architectures, [16][17][18][19] experimental validations on biologically relevant phantoms [20][21][22][23] and real biospecimens 3,6,8,13,15,24 have also occurred.…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

2021

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Recent studies have demonstrated the ability to rapidly produce large field of view X-ray diffraction (XRD) images, which provide rich new data relevant to the understanding and analysis of disease. However, work has only just begun on developing algorithms that maximize the performance toward decision-making and diagnostic tasks. In this study, we present the implementation of and comparison between rules-based and machine learning (ML) classifiers on XRD images of medically relevant phantoms to explore the potential for increased classification performance. Methods: Medically relevant phantoms were utilized to provide wellcharacterized ground-truths for comparing classifier performance. Water and polylactic acid (PLA) plastic were used as surrogates for cancerous and healthy tissue, respectively, and phantoms were created with varying levels of spatial complexity and biologically relevant features for quantitative testing of classifier performance. Our previously developed X-ray scanner was used to acquire co-registered X-ray transmission and diffraction images of the phantoms. For classification algorithms, we explored and compared two rules-based classifiers (cross-correlation, or matched-filter, and linear least-squares unmixing) and two ML classifiers (support vector machines and shallow neural networks). Reference XRD spectra (measured by a commercial diffractometer) were provided to the rules-based algorithms, while 60% of the measured XRD pixels were used for training of the ML algorithms. The area under the receiver operating characteristic curve (AUC) was used as a comparative metric between the classification algorithms, along with the accuracy performance at the midpoint threshold for each classifier. Results:The AUC values for material classification were 0.994 (crosscorrelation [CC]), 0.994 (least-squares [LS]), 0.995 (support vector machine [SVM]), and 0.999 (shallow neural network [SNN]). Setting the classification threshold to the midpoint for each classifier resulted in accuracy values of CC = 96.48%, LS = 96.48%, SVM = 97.36%, and SNN = 98.94%. If only considering pixels ±3 mm from water-PLA boundaries (where partial volume effects could occur due to imaging resolution limits), the classification accuracies were CC = 89.32%, LS = 89.32%, SVM = 92.03%, and SNN = 96.79%, demonstrating an even larger improvement produced by the machine-learned algorithms in spatial regions critical for imaging tasks. Classification by transmission data alone produced an AUC of 0.773 and accuracy of 85.45%, well below the performance levels of any of the classifiers applied to XRD image data. Conclusions: We demonstrated that ML-based classifiers outperformed rulesbased approaches in terms of overall classification accuracy and improved the spatially resolved classification performance on XRD images of medical phantoms. In particular, the ML algorithms demonstrated considerably improved 532

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

2021

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“…However, the real texture or ultratexture of HA in bone remains unclear, especially the quantitative texture analysis in three-dimensions, which offers important texture information on biominerals in bones (e.g., no orientation distribution function (ODF) analysis data). As a nondestruction technology, X-ray micro diffractometry (XRMD) has been successfully applied to biomineralization researches, − which provides new insight into the crystallographic characteristics of biominerals.…”

Section: Introductionmentioning

confidence: 99%

Interweave Texture of Hydroxyapatite in Yak Bone

Wang

Yan

Wang

2020

Crystal Growth & Design

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A new “interweave” texture of bioapatite was found in the bone of a yak, which greatly strengthens the bone’s mechanical performance to support yaks in high-altitude and oxygen-lacking environments of the world. To unravel this texture, we employed X-ray micro diffractometry, environmental scanning electron microscopy, energy dispersive spectrometry, transmission electron microscopy, and Fourier transform infrared spectrometry to investigate the bioapatite in hard tissues of yaks. Other mineralogical and crystallographic characteristics of bioapatite in yak bone are: (i) hydroxyapatite (HA) is the biomineral phase; (ii) compositionally HA is deficient in Ca and rich in P, with a little Mg, K, and Na substituting for Ca in the M site and a little Si and Al substituting for P in the Z site; (iii) lattice parameters of a = 0.9359–0.9400 nm and c = 0.6861–0.6893 nm; (iv) particle sizes of HA are 13.0–29.0 nm along the c-axis and about 9.5 nm thick perpendicular to c-axis; (v) HA is in poor crystallinity. Based on these characteristics, a model of HA grown in yak bone was proposed, which consists of a series of hexagonal prisms enclosed with collagens and adjusted in zero or several to 10 degrees to the c-axis to make an interweave complex.

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Breast cancer analysis by confocal energy dispersive micro-XRD

Abstract: In this work, the confocal energy dispersive micro-XRD technique has been employed to efficiently study differences between normal and malignant carcinomas in breast tissues.

Cited by 2 publications

References 16 publications

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

Application of machine learning classifiers to X‐ray diffraction imaging with medically relevant phantoms

Interweave Texture of Hydroxyapatite in Yak Bone

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