Metamaterial formalism approach for advancing the recognition of glioma areas in brain tissue biopsies

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.

The Discrete Analysis of the Tissue Biopsy Images With Metamaterial Formalization: Identifying Tumor Locus

“…Figure 2. Example of the dependence of the effective permittivity upon frequency for the case of mouse brain cancer (blue) and healthy tissue (red) [35] As it was discovered in [35] (Fig. 2) effective permittivity values are negative.…”

“…The outstanding feature of the described model is the possibility of the extraordinary nature of the effective permittivity. This is illustrated by the peak of effective permittivity curve in the certain case at the frequency range (0-10 THz) [35] if the total amount of the glioma cells in the sample exceeds 5%. Figure 2.…”

“…It is worthwhile mentioning, that in this case human involvement is not needed. The techniques created allow to calculate the critical limit of the glioma cells which allow to delimitate the tumour boundaries that in turn give opportunity to identify cancer at the early stages [35]. As saying whether the biological tissue is non-cancerous, biopsy sample image is to be analysed using the effective medium approximation [34].…”

“…Doing so, image digitizer techniques are applied aiming to have a deep insight in to the exact coordinates of the cell sets combining the sample. It should be mentioned, that the coordinates of the glioma, glia and neuronal cells were clearly demonstrated in [35]. Aiming to apply the MMF approach, the optical properties of the cells should be characterized by Debye and Cole-Cole models [36].…”

“…To our best knowledge, there is no study applying MMF for recognising the cancerous tissues. In the research we will deploy a theoretical analysis of the effective permittivity of materials aiming to explore the acceptability of the models [35] to treat biological tissues by providing a significant enhancement at the tissue digitization stage.…”