Desenvolvimento de modelos de classificação multivariada para diagnóstico precoce do câncer de mama a partir de técnicas espectroscópicas

Abstract

Worldwide, 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.