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.

Highlights

  • Cancer is one of the leading causes of death worldwide, and its diagnosis is critical to initiate therapies [1]

  • The crucial result is the ability of the effective permittivity to behave in the extraordinary way characterized by the peak of effective permittivity curve in the certain case at the frequency range (0-10 THz) (Fig. 2) if the total amount of the glioma cells in the sample is greater than 5%

  • The described metamaterial approach allows for a precise recognition of the healthy and cancerous tissues

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Summary

Introduction

Cancer is one of the leading causes of death worldwide, and its diagnosis is critical to initiate therapies [1]. Machine learning algorithms can analyse large amounts of data and solve complex tasks in a very short time [6,7]. The former can provide the fertile ground for helping physician to improve the accuracy and efficiency of making cancer diagnoses, selecting personalized therapies and predicting long-term outcomes. THz metamaterials are made of periodically arranged sub-wavelength metal structures It is worthwhile pointing out the similarity between the disordered metamaterials [16,17,18] and biological tissues and the applicability of the metamaterial formalism to treat the biological processes. The determination of the effective permittivity of the tissue samples allows for recognition of cancerous tissues

Effective permittivity determination based on metamaterial formalism
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