Identification of Cancer Cell Types by Electrical Impedance Spectroscopy Based on Principal Component Analysis...

This study aims to enhance the identification of cancer cell types using electrical impedance spectroscopy (EIS) by introducing a novel analysis method, ECM-PCA, which integrates an equivalent circuit model with principal component analysis.

The ECM-PCA method addresses the limitations of conventional PCA and kernel PCA (kPCA) in handling non-linear and frequency-dependent data. Impedance data of four cancer cell types (DLD-1, T.Tn, U138, and U87) were acquired across a frequency range of 0.1 MHz to 300 MHz. The ECM-PCA method was applied to analyze the frequency-dependent impedance behaviour and compare its clustering performance with PCA and kPCA.

ECM-PCA demonstrated clustering performance comparable to kPCA while capturing the frequency-dependent features of impedance spectra, which kPCA lacks. The phase angle component as the ECM-PCA input achieved the highest Calinski-Harabasz (CH) score of 935, and the method achieved an identification accuracy of 93.6% in the PC1 and PC2 plane.

ECM-PCA improves the accuracy and interpretability of cancer cell type identification based on electrical impedance data.

This study highlights the potential of ECM-PCA in advancing cancer diagnostics through enhanced analysis of impedance spectra.