Raman spectroscopy for classification of neoplastic and non-neoplastic CAM colon tumors

• Published in: Heliyon Vol. 10; no. 17; p. e36981
• Main Authors: Esteves, B., Pimenta, S., Maciel, M.J., Costa, M., Baltazar, F., Cerqueira, M.F., Alpuim, P., Silva, C.A., Correia, J.H.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15-09-2024; Elsevier
• Subjects: Chicken chorioallantoic membrane assay; Colon cancer; Principal component analysis; Raman peaks; Support vector machine; Colon cancer; Raman peaks; Support vector machine; Chicken chorioallantoic membrane assay; Principal component analysis
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e36981

This paper demonstrates the potential of Raman spectroscopy for differentiating neoplastic from non-neoplastic colon tumors, obtained with the CAM (chicken chorioallantoic membrane) model. For the CAM model two human cell lines were used to generate two types of tumors, the RKO cell line for neoplastic colon tumors and the NCM460 cell line for non-neoplastic colon tumors. The Raman spectra were acquired with a 785 nm excitation laser. The measured Raman spectra from the CAM samples (n = 14) were processed with several methods for baseline correction and to remove artifacts. The corrected spectra were analyzed with PCA (principal component analysis). Additionally, machine learning based algorithms were used to create a model capable of classifying neoplastic and non-neoplastic tumors. The principal component scores showed a clear differentiation between neoplastic and non-neoplastic colon tumors. The classification model had an accuracy of 93 %. Thus, a complete methodology to process and analyze Raman spectra was validated, using a rapid, accessible, and well-established tumor model that mimics the human tumor pathology with minor ethical concerns.