Paraffin-Embedded Prostate Cancer Tissue Grading Using Terahertz Spectroscopy and Machine Learning

Knyazkova, Anastasia I.; Borisov, Alexey V.; Спирина, Л. В.; Kistenev, Yury V.

doi:10.1007/s10762-020-00673-7

Cited by 17 publications

(11 citation statements)

References 41 publications

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“…The analysis of the absorption spectra of the pellets included a reduction of the dimension of the feature space using the principal component analysis (PCA) 36 – 38 and predictive model construction using the support vector machine (SVM). 39 – 41 …”

Section: Experiments and Methodologymentioning

confidence: 99%

“…The analysis of the absorption spectra of the pellets included a reduction of the dimension of the feature space using the principal component analysis (PCA) [36][37][38] and predictive model construction using the support vector machine (SVM). [39][40][41] The basic idea of PCA is to find the reduced number of new variables termed the principal components that are sufficient for the recovery of the initial variables, possibly with insignificant errors. 37 The linear separability of non-diabetic and diabetic pellets was illustrated by an SVM with a linear kernel.…”

Section: Calculation the Absorption Coefficient And Refractive Index mentioning

confidence: 99%

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Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets

et al. 2021

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Significance: The creation of fundamentally new approaches to storing various biomaterial and estimation parameters, without irreversible loss of any biomaterial, is a pressing challenge in clinical practice. We present a technology for studying samples of diabetic and non-diabetic human blood plasma in the terahertz (THz) frequency range. Aim: The main idea of our study is to propose a method for diagnosis and storing the samples of diabetic and non-diabetic human blood plasma and to study these samples in the THz frequency range. Approach: Venous blood from patients with type 2 diabetes mellitus and conditionally healthy participants was collected. To limit the impact of water in the THz spectra, lyophilization of liquid samples and their pressing into a pellet were performed. These pellets were analyzed using THz time-domain spectroscopy. The differentiation between the THz spectral data was conducted using multivariate statistics to classify non-diabetic and diabetic groups' spectra. Results: We present the density-normalized absorption and refractive index for diabetic and nondiabetic pellets in the range 0.2 to 1.4 THz. Over the entire THz frequency range, the normalized index of refraction of diabetes pellets exceeds this indicator of non-diabetic pellet on average by 9% to 12%. The non-diabetic and diabetic groups of the THz spectra are spatially separated in the principal component space. Conclusion: We illustrate the potential ability in clinical medicine to construct a predictive rule by supervised learning algorithms after collecting enough experimental data.

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Section: Experiments and Methodologymentioning

confidence: 99%

Section: Calculation the Absorption Coefficient And Refractive Index mentioning

confidence: 99%

Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets

et al. 2021

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“…The correlation coefficient of component decomposition with the mixture content was 99.14% (Figure 19). In 2020, Knyazkova et al [191] applied principal component analysis, SVM, and "major-ity vote" classification to analyze paraffin-embedded prostate cancer tissue. The model showed a 100% classification rate for the test set.…”

Section: Application Of Machine Learning To Increase the Sensitivity Of Detection Methods For Glioma Molecular Markersmentioning

confidence: 99%

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

et al. 2021

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This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.

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“…For example, applying PPCA to the absorption coefficient or transmission spectra extracts the key attenuation features. Examples can be found in the classification of exhaled air from patients with diabetes mellitus and healthy volunteers, 349 as well as classifying tissues of melanoma and nevus, 346 normal and prostate cancer, 350 and different concentrations of bovine serum albumin (BSA) solutions. 343 Other measured values can also be dimensionally reduced by PPCA.…”

Section: New Advances In Data Analysismentioning

confidence: 99%

Terahertz (THz) biophotonics technology: Instrumentation, techniques, and biomedical applications

Chen

Lindley-Hatcher

Stantchev

et al. 2022

Chemical Physics Reviews

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Terahertz (THz) technology has experienced rapid development in the past two decades. Growing numbers of interdisciplinary applications are emerging, including materials science, physics, communications, and security as well as biomedicine. THz biophotonics involves studies applying THz photonic technology in biomedicine, which has attracted attention due to the unique features of THz waves, such as the high sensitivity to water, resonance with biomolecules, favorable spatial resolution, capacity to probe the water–biomolecule interactions, and nonionizing photon energy. Despite the great potential, THz biophotonics is still at an early stage of development. There is a lack of standards for instrumentation, measurement protocols, and data analysis, which makes it difficult to make comparisons among all the work published. In this article, we give a comprehensive review of the key findings that have underpinned research into biomedical applications of THz technology. In particular, we will focus on the advances made in general THz instrumentation and specific THz-based instruments for biomedical applications. We will also discuss the theories describing the interaction between THz light and biomedical samples. We aim to provide an overview of both basic biomedical research as well as pre-clinical and clinical applications under investigation. The paper aims to provide a clear picture of the achievements, challenges, and future perspectives of THz biophotonics.

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Paraffin-Embedded Prostate Cancer Tissue Grading Using Terahertz Spectroscopy and Machine Learning

Cited by 17 publications

References 41 publications

Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets

Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

Terahertz (THz) biophotonics technology: Instrumentation, techniques, and biomedical applications

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