Rapid identification of breast cancer in different stages using micro-FTIR and supervised machine learning methods
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2024
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SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE
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According to the World Health Organization, breast cancer is the second most common cancer in the world; 11.5% of the total cases of cancer in both genders and 15.4% of deaths in females are reported. Accurate determination of the intrinsic subtype and disease stage of breast cancer will help in the adoption of optimal treatment strategies, thus improving overall outcomes. The aim of the present work, therefore, is to apply micro-FTIR spectroscopy combined with different supervised machine learning methods to classify various types and stages of breast cancer and to identify the chemometric areas that best distinguish between them. In the work reported here, PCA-LDA and PLS-DA models were carried out in the raw data in the fingerprint region (1800–900 cm−1) and the region characteristic for proteins (1750–1400 cm−1). Therefore, the analysis of these results reveals significant differences in the amide I and amide II regions, thus proving that both PCA-LDA and PLS-DA are useful frameworks for performing discrimination analyses.
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GERMANO, GLEICE; VALLE, MATHEUS D.; PERES, DANIELLA L.P.M. de O.; SILVA, DANIELA de F.T. da; PEREIRA, THIAGO M.; ZEZELL, DENISE M. Rapid identification of breast cancer in different stages using micro-FTIR and supervised machine learning methods. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, November 11-13, 2024, Salvador, BA. Proceedings... Piscataway, NJ, USA: IEEE, 2024. DOI: 10.1109/SBFotonIOPC62248.2024.10813523. Disponível em: https://repositorio.ipen.br/handle/123456789/49372. Acesso em: 26 Mar 2025.
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