Abstract

Objective: In this paper we propose a novel use of the wavelet transform in analyzing spectral impedance to discriminate cancerous and non-cancerous lung tissue. Methods: Cancerous and Non Cancerous ex-vivo tissue samples were obtained during a human clinical trial of patients undergoing a lobectomy. Spectral Impedance measurements of the tissues samples were performed using a custom impedance bridge and measurement system. Three methods are described using the wavelet transform of the spectral impedances and comparing results to pathological assessments of the lesion. Results: In a cohort of n=46 patients, for which both tumor and away-from-tumor samples have been collected, we report sensitivity of 87% and specificity of 84%. Conclusion: Three techniques have been proposed as an algorithm basis to improve the sensitivity and specificity of discriminating between cancerous and non-cancerous tissue. Significance: Lung Cancer is the cause of 20% of all cancer related death globally and is the most diagnosed cancer worldwide. Utilizing the proposed methods of analyzing tissue samples and reporting on cancer state at the surgical suite offers clinicians an additional tool to better diagnose and subsequently treat the deadly disease.