Abstract

Breast cancer is a well-known health issue that has been a major focus for healthcare professionals for quite some time. Still, the most common noninvasive diagnostic tool - mammography - results in a high false positive rate along with risks of exposure to radiation. These disadvantages are magnified and become more severe when screenings are done repeatedly. To tackle this problem, we introduce a novel framework for uncomplicated diagnosis of breast cancer. Our method utilizes the analytical technique of Mueller matrix decomposition and Stokes vector polarimetry from a polarized light system consisting of a helium-neon laser (wavelength of 632.5 nm), a quarter-wave plate, polarizers, and a Stokes polarimeter. Thus, this technique introduces no radiation. We extracted nine optical parameters of a breast cancer cell line - BT474 - and determined the relationshipand separation power of these parameters to cancerous cells and healthy cells. Specifically, the samples were designed as a two-dimensional cellular model of malignant breast tumours that combined a range of four cell densities - 104, 105, 106, and 107 cells - per an area of 9 cm2.Nine optical parameters - orientation angle of linear birefringence (α), retardance or linear birefringence (β), optical rotation angle or circular birefringence (γ), orientation angle of linear dichroism (θd), linear dichroism (D), circular dichroism (R), degrees of linear depolarization (e1 and e2), and degree of circular depolarization (e3) - were extracted from a total of 40 samples using the polarized light system. The results revealed the positive correlations between three cell densities (104, 105, and 106) and the orientation angle of linear birefringence (R2 = 0.8038), linear birefringence (R2 = 0.8627), and linear dichroism (R2 = 0.9662). Meanwhile, both the orientation angle of linear dichroism and circular dichroism illustrated the negative correlation with that range of cell densities with R2 = 0.9983 and 0.9447, respectively. This proves that the optical parameters measured demonstrate significant association with the cells’ characteristics and thus, the proposed method could pave the way for an accessible diagnosis of breast cancer.

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