Lung cancer diagnosis using statistical methods and the Raman spectroscopy model

Lung cancer affects both men and women and is caused by uncontrolled cell proliferation in the lungs. This results in severe respiratory issues in both the intake and exhaust ports of the chest. According to the Health Organization, smoking cigarettes and passive smoking are indeed the leading causes of lung disease. Despite the tremendous advancement in medical research, diagnosing lung cancer remains difficult. Surface-enhanced Raman spectroscopy (SERS) offers vast applicability in biomedicine, particularly in the realm of tumor blood identification, due to its extremely high detectability. While RS can detect lung cancer in tissue sections, its weak cross-sections are indeed a challenge. Silver nanoparticles were used to increase the Raman scattering signals of macromolecules on the surfaces of lung tissue sections in this investigation. COMSOL model was used to simulate the electromagnetic field dispersion of Ag nanoparticles. The biological compounds in normal and malignant lung tissues were represented in SERS collected from the sections. Lung cancer and normal lung samples were classified using principal component-linear discriminate analysis. The PCA-LDA theory’s specificity and sensitivity were determined via the area under curve. This research expands on the general application of SERS tissue slicing evaluation in medical studies.