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

• Published in: Chemometrics and intelligent laboratory systems 2017-06, Vol.165, p.38-45
• Main Authors: Aguado-Sarrió, E., Prats-Montalbán, J.M., Sanz-Requena, R., Garcia-Martí, G., Martí-Bonmatí, L., Ferrer, A.
• Format: Article
• Language: English
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2017.04.003

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. •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.