Lung cancer kills more than 150,000 people a year in the United States of America, according to the National Cancer Institute. Over 220,000 people will be diagnosed with the lung cancer in 2012 [1]. The most common cause of form of lung cancer is non-small cell lung cancer and it’s usually attributed to smoking tobacco or use of tobacco related products. Currently, the success rate of detecting and diagnosing early cancer stages is near zero while the 5 year survival rate after diagnosis is less than 20% [1]. New methodologies and modalities for imaging lung cells must be developed in order to increase detection and diagnosis of early stages of lung cancer in order to allow the use of more effective early stage treatments.
Near infrared polarized light was used to investigate the power spectral density of the backscattered light from lung cancer samples of stage 2 Adenocarcinoma, Acinar Adenocarcinoma and Squamous cell carcinoma monoline cells. Experiments were performed on several samples with various polarizations. The backscattered light was normalized to investigate the differences in their Welch estimated power spectral density and the consistency of results the width of the backscattered power spectrum [2]. Welch estimated power spectral density was used to reduce the effects of harmonics on the result from a Fast Fourier Transform power spectral density.
This study’s emerging research shows the viability of optical imaging techniques in lung cancer screening and diagnosis. The power spectral density, using the estimating technique developed by Welch, was used to analyze Stage 2 Squamous, Acinar Adenocarcinoma, and Adenocarcinoma monoline cancer cells that showed statistically significant ANOVA results. In the cases where the results were not statistically significant, the addition of a mixture of cancer types separated the results enough to be statistically significant and therefore provide a means of detection. This study provides solid ground work for Welch estimated power spectral density analysis to aid in lung cancer detection.