Abstract

Early detection of a tumor makes it more probable that the patient will, finally, beat cancer and recover. The main goal of broadly defined cancer diagnostics is to determine whether a patient has a tumor, where it is located, and its histological type and severity. The major characteristic of the cancer affected tissue is the presence of the glioma cells in the sample. The current approach in diagnosis focuses mainly on microbiological, immunological, and pathological aspects rather than on the “metamaterial geometry” of the diseases. The determination of the effective properties of the biological tissue samples and treating them as disordered metamaterial media has become possible with the development of effective medium approximation techniques. Their advantage lies in their capability to treat the biological tissue samples as metamaterial structures, possessing the well-studied properties. Here, we present, for the first time to our knowledge, the studies on metamaterial properties of biological tissues to identify healthy and cancerous areas in the brain tissue. The results show that the metamaterial properties strongly differ depending on the tissue type, if it is healthy or unhealthy. The obtained effective permittivity values were dependent on various factors, like the amount of different cell types in the sample and their distribution. Based on these findings, the identification of the cancer affected areas based on their effective medium properties was performed. These results prove the metamaterial model capability in recognition of the cancer affected areas. The presented approach can have a significant impact on the development of methodological approaches toward precise identification of pathological tissues and would allow for more effective detection of cancer-related changes.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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2019 (4)

L. Shen, L. R. Margolies, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep Learning to Improve Breast Cancer Detection on Screening Mammography,” Sci. Rep. 9(1), 12495 (2019).
[Crossref]

S. Goldenberg, G. Nir, and S. E. Salcudean, “A new era: artificial intelligence and machine learning in prostate cancer,” Nat. Rev. Urol. 16(7), 391–403 (2019).
[Crossref]

M. Zhu, L. Zhang, S. Ma, J. Wang, J. Su, and A. Liu, “Terahertz metamaterial designs for capturing and detecting circulating tumor cells,” Mater. Res. Express 6(4), 045805 (2019).
[Crossref]

A. Khokhlova, I. Zolotovskii, S. Sokolovski, Y. Saenko, E. Rafailov, D. Stoliarov, E. Pogodina, V. Svetukhin, V. Sibirny, and A. Fotiadi, “The light-oxygen effect in biological cells enhanced by highly localized surface plasmon-polaritons,” Sci. Rep. 9(1), 18435 (2019).
[Crossref]

2018 (4)

A. E. Fetit, J. Novak, D. Rodriguez, D. P. Auer, C. A. Clark, R. G. Grundy, A. C. Peet, and T. N. Arvanitis, “Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis,” NMR Biomed. 31(1), e3781 (2018).
[Crossref]

Z. Zhang, H. Ding, X. Yan, L. Liang, D. Wei, M. Wang, Q. Yang, and J. Yao, “Sensitive detection of cancer cell apoptosis based on the non-bianisotropic metamaterials biosensors in terahertz frequency,” Opt. Mater. Express 8(3), 659–667 (2018).
[Crossref]

I. Banerjee, A. Crawley, M. Bhethanabotla, H. E. Daldrup-Link, and D. L. Rubin, “Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma,” Comput. Med. Imaging Graph. 65, 167–175 (2018).
[Crossref]

S. Bhakdi and P. Thaicharoen, “Easy employment and crosstalk-free detection of seven fluorophores in a widefield fluorescence microscope,” Methods Protoc. 1(2), 20 (2018).
[Crossref]

2017 (4)

N. A. Navolokin, D. A. Mudrak, A. B. Bucharskaya, O. V. Matveeva, S. A. Tychina, N. V. Polukonova, and G. N. Maslyakova, “Effect of flavonoid-containing extracts on the growth of transplanted sarcoma 45, peripheral blood and bone marrow condition after oral and intramuscular administration in rats,” Russ. Open Med. J. 6(3), e0304 (2017).
[Crossref]

E. Callaway, D. Castelvecchi, D. Cyranoski, E. Gibney, H. Ledford, J. J. Lee, L. Morello, N. Phillips, Q. Schiermeier, J. Tollefson, and A. Witze, “2017 in news: the science events that shaped the year,” Nature 552(7685), 304–307 (2017).
[Crossref]

T. Gric and O. Hess, “Controlling hybrid-polarization surface plasmon polaritons in dielectric-transparent conducting oxides metamaterials via their effective properties,” J. Appl. Phys. 122(19), 193105 (2017).
[Crossref]

T. Gric and O. Hess, “Tunable surface waves at the interface separating different graphene-dielectric composite hyperbolic metamaterials,” Opt. Express 25(10), 11466–11476 (2017).
[Crossref]

2016 (1)

E. Meijering, A. E. Carpenter, H. Peng, F. A. Hamprecht, and J.-C. Olivo-Marin, “Imagining the future of bioimage analysis,” Nat. Biotechnol. 34(12), 1250–1255 (2016).
[Crossref]

2015 (2)

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer Statistics, 2015,” CA: Cancer J. Clin. 65(1), 5–29 (2015).
[Crossref]

2014 (2)

A. Kamal, S. Faazil, and M. S. Malik, “Apoptosis-inducing agents: A patent review (2010 - 2013),” Expert Opin. Ther. Pat. 24(3), 339–354 (2014).
[Crossref]

T. Gric and M. Cada, “Analytic solution to field distribution in one-dimensional inhomogeneous media,” Opt. Commun. 322, 183–187 (2014).
[Crossref]

2013 (1)

A. Kaczmarek, P. Vandenabeele, and D. V. Krysko, “Necroptosis: The Release of Damage-Associated Molecular Patterns and Its Physiological Relevance,” Immunity 38(2), 209–223 (2013).
[Crossref]

2012 (1)

O. Kidwai, S. V. Zhukovsky, and J. E. Sipe, “Effective-medium approach to planar multilayer hyperbolic metamaterials: Strengths and limitations,” Phys. Rev. A 85(5), 053842 (2012).
[Crossref]

2011 (3)

P. Zakharov, F. Dewarrat, A. Caduff, and M. S. Talary, “The effect of blood content on the optical and dielectric skin properties,” Physiol. Meas. 32(1), 131–149 (2011).
[Crossref]

G. Li and X.-F. Pang, “Effects of electromagnetic field exposure on electromagnetic properties of biological tissues,” Prog. Biochem. Biophys. 38(7), 604–610 (2011).
[Crossref]

C. Helgert, C. Rockstuhl, C. Etrich, E.-B. Kley, A. Tünnermann, F. Lederer, and T. Pertsch, “Effects of anisotropic disorder in an optical metamaterial,” Appl. Phys. A 103(3), 591–595 (2011).
[Crossref]

2007 (1)

J. Gollub, “Characterizing the effects of disorder in metamaterial structures,” Appl. Phys. Lett. 91(16), 162907 (2007).
[Crossref]

2006 (1)

M. V. Gorkunov, S. A. Grdeskul, I. V. Shadrivov, and Y. S. Kivshar, “Effect of microscopic disorder on magnetic properties of metamaterials,” Phys. Rev. E 73(5 Pt 2), 056605 (2006).
[Crossref]

2005 (2)

B. Levine and J. Yuan, “Autophagy in cell death: An innocent convict?” J. Clin. Invest. 115(10), 2679–2688 (2005).
[Crossref]

H. Sun, J. Yang, and M. Ren, “A fast watershed algorithm based on chain code and its application in image segmentation,” Pattern Recogn. Lett. 26(9), 1266–1274 (2005).
[Crossref]

2004 (1)

M. Bruggerman, W. Kalkner, A. Campus, and A. Smedberg, “Electrochemical effects at the conductor/dielectric interface-a description of the mechanism,” IEEE Intl. Conf. Solid Dielectrics 1, 383–386 (2004).
[Crossref]

2002 (1)

M. Schaefer, W. Gross, J. Ackemann, and M. M. Gebhard, “The complex dielectric spectrum of heart tissue during ischemia,” Bioelectrochemistry 58(2), 171–180 (2002).
[Crossref]

1999 (1)

S. E. Skipetrov, “Effective dielectric function of a random medium,” Phys. Rev. B 60(18), 12705–12709 (1999).
[Crossref]

1995 (1)

P. Salembier and J. Serra, “Flat zones filtering, connected operators, and filters by reconstruction,” IEEE Trans. on Image Process. 4(8), 1153–1160 (1995).
[Crossref]

1979 (1)

N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Trans. Syst., Man, Cybern. 9(1), 62–66 (1979).
[Crossref]

1974 (2)

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

1961 (1)

T. Hanai, “A remark on the ?Theory of the dielectric dispersion due to the interfacial polarization?” Colloid Polym. Sci. 175(1), 61–62 (1961).
[Crossref]

1904 (1)

J. C. Maxwell-Garnett, “Colours in metal glasses and films,” Philos. Trans. R. Soc. A 203, 385–420 (1904).

Ackemann, J.

M. Schaefer, W. Gross, J. Ackemann, and M. M. Gebhard, “The complex dielectric spectrum of heart tissue during ischemia,” Bioelectrochemistry 58(2), 171–180 (2002).
[Crossref]

Arvanitis, T. N.

A. E. Fetit, J. Novak, D. Rodriguez, D. P. Auer, C. A. Clark, R. G. Grundy, A. C. Peet, and T. N. Arvanitis, “Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis,” NMR Biomed. 31(1), e3781 (2018).
[Crossref]

Auer, D. P.

A. E. Fetit, J. Novak, D. Rodriguez, D. P. Auer, C. A. Clark, R. G. Grundy, A. C. Peet, and T. N. Arvanitis, “Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis,” NMR Biomed. 31(1), e3781 (2018).
[Crossref]

Banerjee, I.

I. Banerjee, A. Crawley, M. Bhethanabotla, H. E. Daldrup-Link, and D. L. Rubin, “Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma,” Comput. Med. Imaging Graph. 65, 167–175 (2018).
[Crossref]

Battu, S.

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

Bessaudou, A.

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

Bessette, B.

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

Bhakdi, S.

S. Bhakdi and P. Thaicharoen, “Easy employment and crosstalk-free detection of seven fluorophores in a widefield fluorescence microscope,” Methods Protoc. 1(2), 20 (2018).
[Crossref]

Bhethanabotla, M.

I. Banerjee, A. Crawley, M. Bhethanabotla, H. E. Daldrup-Link, and D. L. Rubin, “Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma,” Comput. Med. Imaging Graph. 65, 167–175 (2018).
[Crossref]

Blondy, P.

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

Bordeweijk, P.

C. J. F. Böttcher and P. Bordeweijk, Theory of Electric Polarisation (Elsevier, 1978).

Bordewijk, P.

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

Böttcher, C. J. F.

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

T. H. Tjia, P. Bordewijk, and C. J. F. Böttcher, “On the notion of dielectric friction in the theory of dielectric relaxation,” Adv. Mol. Relax. Processes 6(1), 19–28 (1974).
[Crossref]

C. J. F. Böttcher and P. Bordeweijk, Theory of Electric Polarisation (Elsevier, 1978).

Bounaix Morand du Puch, C.

J. Leroy, C. Dalmay, A. Landoulsi, F. Hjeij, C. Mélin, B. Bessette, C. Bounaix Morand du Puch, S. Giraud, C. Lautrette, S. Battu, F. Lalloué, M. O. Jauberteau, A. Bessaudou, P. Blondy, and A. Pothier, “Microfluidic biosensors for microwave dielectric spectroscopy,” Sens. Actuators, A 229, 172–181 (2015).
[Crossref]

Bradski, G.

G. Bradski, The OpenCV Library. Dr. Dobb’s J. Softw. Tools (2000).

Bruggerman, M.

M. Bruggerman, W. Kalkner, A. Campus, and A. Smedberg, “Electrochemical effects at the conductor/dielectric interface-a description of the mechanism,” IEEE Intl. Conf. Solid Dielectrics 1, 383–386 (2004).
[Crossref]

Bucharskaya, A. B.

N. A. Navolokin, D. A. Mudrak, A. B. Bucharskaya, O. V. Matveeva, S. A. Tychina, N. V. Polukonova, and G. N. Maslyakova, “Effect of flavonoid-containing extracts on the growth of transplanted sarcoma 45, peripheral blood and bone marrow condition after oral and intramuscular administration in rats,” Russ. Open Med. J. 6(3), e0304 (2017).
[Crossref]

Cada, M.

T. Gric and M. Cada, “Analytic solution to field distribution in one-dimensional inhomogeneous media,” Opt. Commun. 322, 183–187 (2014).
[Crossref]

Caduff, A.

P. Zakharov, F. Dewarrat, A. Caduff, and M. S. Talary, “The effect of blood content on the optical and dielectric skin properties,” Physiol. Meas. 32(1), 131–149 (2011).
[Crossref]

Callaway, E.

E. Callaway, D. Castelvecchi, D. Cyranoski, E. Gibney, H. Ledford, J. J. Lee, L. Morello, N. Phillips, Q. Schiermeier, J. Tollefson, and A. Witze, “2017 in news: the science events that shaped the year,” Nature 552(7685), 304–307 (2017).
[Crossref]

Campus, A.

M. Bruggerman, W. Kalkner, A. Campus, and A. Smedberg, “Electrochemical effects at the conductor/dielectric interface-a description of the mechanism,” IEEE Intl. Conf. Solid Dielectrics 1, 383–386 (2004).
[Crossref]

Carpenter, A. E.

E. Meijering, A. E. Carpenter, H. Peng, F. A. Hamprecht, and J.-C. Olivo-Marin, “Imagining the future of bioimage analysis,” Nat. Biotechnol. 34(12), 1250–1255 (2016).
[Crossref]

Castelvecchi, D.

E. Callaway, D. Castelvecchi, D. Cyranoski, E. Gibney, H. Ledford, J. J. Lee, L. Morello, N. Phillips, Q. Schiermeier, J. Tollefson, and A. Witze, “2017 in news: the science events that shaped the year,” Nature 552(7685), 304–307 (2017).
[Crossref]

Clark, C. A.

A. E. Fetit, J. Novak, D. Rodriguez, D. P. Auer, C. A. Clark, R. G. Grundy, A. C. Peet, and T. N. Arvanitis, “Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis,” NMR Biomed. 31(1), e3781 (2018).
[Crossref]

Crawley, A.

I. Banerjee, A. Crawley, M. Bhethanabotla, H. E. Daldrup-Link, and D. L. Rubin, “Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma,” Comput. Med. Imaging Graph. 65, 167–175 (2018).
[Crossref]

Cyranoski, D.

E. Callaway, D. Castelvecchi, D. Cyranoski, E. Gibney, H. Ledford, J. J. Lee, L. Morello, N. Phillips, Q. Schiermeier, J. Tollefson, and A. Witze, “2017 in news: the science events that shaped the year,” Nature 552(7685), 304–307 (2017).
[Crossref]

Daldrup-Link, H. E.

I. Banerjee, A. Crawley, M. Bhethanabotla, H. E. Daldrup-Link, and D. L. Rubin, “Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma,” Comput. Med. Imaging Graph. 65, 167–175 (2018).
[Crossref]

Dalmay, C.

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Figures (4)

Fig. 1.
Fig. 1. Images of cancerous and non-cancerous mice brain biopsies (A, B – 39M, C, D – 138M, E, F – 153M, G, H – 154M). Glioma cells are marked with green hexagons, neuron cells – with blue asterisks, glia cells – with red circles. Tissue biopsies are presented in A, C, E, G Figures, digitized images made with “Digitizeit” software (www.digitizeit.de/)– in B, D, F, H.
Fig. 2.
Fig. 2. Dependencies of the effective permittivities upon frequency for healthy (a) and unhealthy (b) samples.
Fig. 3.
Fig. 3. Images of cancerous and non-cancerous mice brain tissue biopsies (A, B – 39M, C, D – 138M, E, F – 153M, G, H – 154M). Division of the tissue samples by the building blocks of different conditions is demonstrated, i. e. red – cancerous; green – healthy; yellow – intermediately populated. Tissue photos are presented in A, C, E, G Figures, Analysed images – in B, D, F, H.
Fig. 4.
Fig. 4. Diagram of the methodology.

Equations (3)

Equations on this page are rendered with MathJax. Learn more.

3 ε 1 2 ε e f f + ε 1 f 1 + 3 ε 2 2 ε e f f + ε 2 f 2 = 1
ε e f f = 3 ε 1 f 1 4 ε 2 4 ε 1 4 + 3 ε 2 f 2 4 9 ε 1 2 f 1 2 6 ε 1 2 f 1 + ε 1 2 + 18 ε 1 ε 2 f 1 f 2 + 6 ε 1 ε 2 f 1 + 6 ε 1 ε 2 f 2 2 ε 1 ε 2 + 9 ε 2 2 f 2 2 6 ε 2 2 f 2 + ε 2 2 4 ,
ε k = ε k ( ) + ε k ( 0 ) ε k ( ) 1 + ( i ω τ k ) 1 α k + σ k i ε 0 ω