Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI)

- MCR-ALS and pharmacokinetic biomarkers has been studied for cancer depiction.
- PLS-DA and variable selection have been employed for tissue classification.
- Significant results prove that MCR biomarkers perform better than pharmacokinetics.

In this work, the capability of imaging biomarkers obtained from multivariate curve resolution-alternating least squares (MCR-ALS), in combination with those obtained from first and second-generation pharmacokinetic models, have been studied for improving prostate cancer tumor depiction using partial least squares-discriminant analysis (PLS-DA). The main goal of this work is to improve tissue classification properties selecting the best biomarkers in terms of prediction. A wrapped double cross-validation method has been applied for the variable selection process. Using the best PLS-DA model, prostate tissues can be classified obtaining 13.4% of false negatives and 7.4% of false positives. Using MCR-ALS biomarkers yields the best models in terms of parsimony and classification performance.