Multivariate classification and Fourier-Transform Mid- Infrared Spectroscopy (FT-MIR) in cancer prostate tissue

Abstract

This thesis is a theoretical-practical contribution for differentiation of prostate cancer stages through multivariate classification applied in MIR spectra from human tissues. The aim of this study was to identify spectral differences between prostate cancer stages, to determine potential biochemical markers responsible for differentiation, and to compare the performance of multivariate classification models from prostate tissue samples previously classified in Gleason II, III and IV for cancer. In a first study, the PCA-LDA, SPA-LDA and GA-LDA models were constructed aiming at a methodology to discriminate prostate cancer stages based on Gleason graduation criteria vs. the categorization of 'Low and High Degrees'; and, to identify potential spectral markers. The models performances were compared. GA-LDA produced the most satisfactory results, being better in the perspective of 'Low and High degrees', with correct classification rate of 83% and sensitivity and specificity values 100% and 80%, respectively. In a second study, PCA-LDA/QDA and GA-LDA/QDA had their performances compared in the classification of 'Low and High grades' of prostate cancer, considering linear or quadratic character in the differentiation. The QDA models obtained better results than the LDA, as well as variables selection method (GA) were better than the variables reduction method (PCA). GA-QDA obtained better performance with classification rates for calibration and prediction samples of 97% and 100%, respectively; and sensitivity and specificity of 75% and 100%, respectively. In a third study, independent SVM models (linear-, polynomial-, RBF- and quadratic-SVMs) and the PCA-SVM, SPA-SVM and GASVM algorithms were applied in order to evaluate the use of variables reduction and selection methods in a nonlinear approach for screening 'Low and High grades' of prostate cancer. Independent SVM models had lower performance than the others. The best model was GASVM with 100% and 90% of 'Low Grade' calibration and prediction samples correctly classified, respectively; and sensitivity and specificity of 90%. The potential spectral biomarkers identified by the studies were attributed to the regions of amides I, II, III and proteins (≈1,591–1,483 cm-1), DNA and RNA (≈1,000–1,490 cm-1) and protein phosphorylation (≈970 cm-1). The intensities variation was more pronounced in 'High degree' spectra. Changes in these regions may indicate metabolic changes caused by cancer advance. The proposed methods showed potentially better performance than traditional diagnostic methods. The results showed that the multivariate classification combined with FT-MIR can differentiate pathological states of tissues mainly in the early stages of cancer ('Low grade') with speed, accuracy, easy proceedings, independence of intra- and inter-observer variability, and high sensitivity and specificity; in comparison to traditional techniques (which suffer with operator-dependence, high intra- and inter-observer variability, high time consuming, difficult preparation and lower sensitivity and specificity). In addition, the methodologies proposed here may imply economic and social benefits based on early diagnosis and treatments, allowing improvement in quality of life and survival of patients.