Innovation for using dielectric properties to distinguish lung tumor from normal lung tissues and preliminary exploration for the relevance with metabolic features.

Background: Our previous studies successfully established deep learning methods to differentiate metastatic lymph nodes from lymphadenitis in patients with lung cancer using dielectric properties. In this study, we aimed to develop a simpler, more interpretable method to differentiate lung tumor tissue from normal lung tissue. Additionally, we explored the correlation between [ Methods: Dielectric properties of lung tumors were measured using an open-ended coaxial probe across a frequency range of 1-4,000 MHz after excision from patients. Normal lung tissues were also measured for comparison. Based on the data trends, the permittivity curves were fitted to logarithmic functions, while the conductivity curves were fitted to exponential functions. The intercepts and slopes of these permittivity and conductivity functions were then used to distinguish lung tumors from normal lung tissues. These parameters were also used to assess the correlation between dielectric properties and PET parameters. Results: Totally 21 lung tumor tissues and 19 normal lung tissues from 21 patients were included in this study. The permittivity and the conductivity values were acquired at frequency range from 1 to 4,000 MHz and then fitted into logarithmic function [ Conclusions: The fitted functions of dielectric properties, including permittivity and conductivity, were able to distinguish lung tumors from normal lung tissues. Further study should be carried out to explore the ability of dielectric properties for differentiating malignant lung tumor from benign lung disease. The metabolic activity and heterogeneity of the tumors, as reflected by [