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.