title: Comparison between PUN and Tofts models in the quantification of dynamic contrast-enhanced MR imaging.
abstract: Dynamic contrast-enhanced study in magnetic resonance imaging (DCE-MRI) is an important tool in oncology to visualize tissues vascularization and to define tumour aggressiveness on the basis of an altered perfusion and permeability. Pharmacokinetic models are generally used to extract hemodynamic parameters, providing a quantitative description of the contrast uptake and wash-out. Empirical functions can also be used to fit experimental data without the need of any assumption about tumour physiology, as in pharmacokinetic models, increasing their diagnostic utility, in particular when automatic diagnosis systems are implemented on the basis of an MRI multi-parametric approach. Phenomenological universalities (PUN) represent a novel tool for experimental research and offer a simple and systematic method to represent a set of data independent of the application field. DCE-MRI acquisitions can thus be advantageously evaluated by the extended PUN class, providing a convenient diagnostic tool to analyse functional studies, adding a new set of features for the classification of malignant and benign lesions in computer aided detection systems. In this work the Tofts pharmacokinetic model and the class EU1 generated by the PUN description were compared in the study of DCE-MRI of the prostate, evaluating complexity of model implementation, goodness of fitting results, classification performances and computational cost. The mean R² obtained with the EU1 and Tofts model were equal to 0.96 and 0.90, respectively, and the classification performances achieved by the EU1 model and the Tofts implementation discriminated malignant from benign tissues with an area under the receiver operating characteristic curve equal to 0.92 and 0.91, respectively. Furthermore, the EU1 model has a simpler functional form which reduces implementation complexity and computational time, requiring 6 min to complete a patient elaboration process, instead of 8 min needed for the Tofts model analysis.
authors.affiliation: Institute for Cancer Research and Treatment, Strada Provinciale 142, km 3.95, 10060 Candiolo, Torino, Italy. simone.mazzetti@ircc.it
authors.forename: S
authors.initials: S
authors.lastname: Mazzetti
authors.forename: A S
authors.initials: AS
authors.lastname: Gliozzi
authors.forename: C
authors.initials: C
authors.lastname: Bracco
authors.forename: F
authors.initials: F
authors.lastname: Russo
authors.forename: D
authors.initials: D
authors.lastname: Regge
authors.forename: M
authors.initials: M
authors.lastname: Stasi
mesh_terms: D000368:Aged; D003287:Contrast Media / Q000493:pharmacokinetics*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000378:metabolism
chemical_list: D003287:Contrast Media