Lima, Kássio Michell Gomes deCâmara, Ingrid de Moura2021-09-212021-09-272021-09-212021-09-272020-12-10CÂMARA, Ingrid de Moura. DESENVOLVIMENTO DE MODELOS DE CLASSIFICAÇÃO MULTIVARIADA PARA DIAGNÓSTICO PRECOCE DO CÂNCER DE MAMA A PARTIR DE TÉCNICAS ESPECTROSCÓPICAS. 2020. 43 f. TCC (Graduação) - Curso de Química do Petróleo, Instituto de Química, Universidade Federal do Rio Grande do Norte, Natal, 2020.https://repositorio.ufrn.br/handle/123456789/38316Worldwide, the incidence of breast cancer has led to a search for quick diagnostic methods. Its mortality could be reduced via screening programs where preliminary clinical tests employed in an asymptomatic well-population with the objective of identifying cancer biomarkers could allow earlier referral of women with altered results for deeper clinical analysis and treatment. The introduction of well-population screening using new and less-invasive technologies as a strategy for earlier detection of breast cancer is thus highly desirable. Herein, spectrochemical analyses harnessed to multivariate classification techniques are used as a bio-analytical tool for a Breast Cancer Screening Program using liquid biopsy in the form of blood plasma samples collected from 476 patients recruited over a 2-year period. This methodology is based on acquiring and analysing the spectrochemical fingerprint of plasma samples by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy; derived spectra reflect intrinsic biochemical composition, generating information on nucleic acids, carbohydrates, lipids and proteins. For that, multivariate classification models were tested: PCA-LDA, PCA-QDA e PCA-SVM; SPA-LDA, SPA-QDA e SPA-SVM; e GA-LDA, GA-QDA e GA-SVM. Excellent results in terms of sensitivity (94%) and specificity (91%) were obtained by the best model, SPA-SVM, in comparison with traditional mammography (88–93% and 85–94%, respectively). Additional advantages such as better disease prognosis, more effective treatment, lower associated morbidity, fewer false-positive and false-negative results, lower-cost, and higher analytical frequency make this method attractive to use on the clinical setting.Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/Química AnalíticaTécnicas multivariadasCâncer de mamaPlasma sanguíneobachelorThesisQuímica Analítica