LDA vs. QDA for FT-MIR prostate cancer tissue classification

Discrimination/classification of biological material a to molecular level is one of the key aims of chemometrics applied to biospectroscopic data. Two discriminant functions, namely Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), were considered in this study for prostate cancer classification based on FT-MIR data, and illustrated graphically as boundary methods. Principal Component Analysis (PCA) Was applied as a variable/dimensionality reduction method and Genetic Algorithm (GA) as variable selection method, followed by LDA and QDA. The performance of each Method was determined using 40-100 MIR spectra per tissue sample (n=45), previously classified according to Gleason traditional grading by pathologists. The methods were used to separate the two-category of prostate cancer: Low grade (Gleason grade 2) vs. High grade (Gleason grade 3 and 4). The models were optimized using a training set and their performance was evaluated using a test set. Classification rates and quality metrics (Sensitivity, Specificity, Positive (or Precision) and Negative Predictive Values, Youden's index, and Positive and Negative Likelihood Ratios) were computed for each model. QDA-based models obtained higher classification rates and quality performance than LDA-based models. The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular 'difference markers' for prostate cancer grades.