Evaluation of a Peak-Free Chemometric Laser-Induced Breakdown Spectroscopy Method for Direct Rapid Cancer Detection via Trace Metal Biomarkers in Tissue

Abstract

The ability to perform direct rapid analysis in air and at atmospheric pressure is a remarkable attraction of laser-induced breakdown spectroscopy (LIBS) for the diagnostic quantification of disease biomarker metals in body tissue. However, accurate trace analysis is limited by matrix effects and a pronounced background that masks the subtle (peak-free) analyte signals because tissue plasma is dense and most lines are optically thick. In this work, a peak-free chemometric LIBS method based on a single-shot (for rapidity and nondestructiveness) and an artificial neural network multivariate calibration strategy with spectral feature selection was evaluated for its utility for direct trace quantitative analysis of copper (Cu), iron (Fe), manganese (Mg), magnesium (Mg), and zinc (Zn) in model soft body tissue. The spectral signatures corresponding to the biometals (so-called because the metals are intrinsic to tissue biochemistry) were generated by spiking their known human-body-representative concentrations in molten paraffin wax. The developed multivariate analytical model achieved ≥95% accuracy as determined from the analysis of oyster tissue-certified reference material. The analytical models were tested on the liver, breast, and abdominal tissue biopsies. The results of applying the model to the clinical tissues indicated the absence or presence (including severity) of cancer as either malignant or benign, in agreement with the pathological examination report.