Discrimination between human normal renal tissue and renal cell carcinoma by dielectric properties using in-vitro BIA - Wang, Hang; Shi, Xuetao; Cao, Xinsheng; Dong, Xiuzhen; Yang, Lin (Frontiers in Physiology, 2023)
Renal cell carcinoma (RCC) poses a serious threat to human health, which urgently requires a method that can quickly distinguish between human normal renal tissue (NRT) and RCC for the purpose of accurate detection in clinical practice. The significant difference in cell morphology between NRT and RCC tissue underlies the great potential of the bioelectrical impedance analysis (BIA) to distinguish two types of human tissues. The study aims to achieve such discrimination through comparison of their dielectric properties within the frequency range from 10 Hz to 100 MHz. The dielectric properties of 69 cases of human normal and cancer renal tissue were measured 15 min after tissue isolation in a strictly controlled environment (37°C, 90% humidity). In addition to the impedance parameters (resistivity, conductivity and relative permittivity), the characteristic parameters extracted from the Cole curve were also compared between NRT and RCC. Furthermore, a novel index, distinguishing coefficient (DC), was used to obtain the optimal frequency for discrimination between NRT and RCC. In terms of impedance parameters, the RCC conductivity at low frequencies (<1 kHz) was about 1.4 times as large as that of NRT, and its relative permittivity was also significantly higher (p < 0.05). In terms of characteristic parameters, two characteristic frequencies (14.1 ± 1.1 kHz and 1.16 ± 0.13 MHz) were found for NRT while only one for RCC (0.60 ± 0.05 MHz). A significant difference of low-frequency resistance (R0) between RCC and NRT was also observed (p < 0.05). As for the new index DC, relative permittivity DCs below 100 Hz and at around 14 kHz were both greater than 1. These findings further confirm the feasibility of discrimination between RCC and NRT and also provide data in favor of further clinical study of BIA to detect the surgical margins.