Classification of prostatic diseases by means of multivariate analysis on in vivo proton MRSI and DCE-MRI data

Abstract

Multivariate analysis has been applied on proton magnetic resonance spectroscopic imaging ((1)H-MRSI) and dynamic contrast enhanced MRI (DCE-MRI) data of patients with different prostatic diseases such as chronic inflammation, fibrosis and adenocarcinoma. Multivariate analysis offers a global view of the entire range of information coming from both the imaging and spectroscopic side of NMR technology, leading to an integrated picture of the system relying upon the entire metabolic and dynamic profile of the studied samples. In this study, we show how this approach, applied to (1)H-MRSI/DCE-MRI results, allows us to differentiate among the various prostatic diseases in a non-invasive way with a 100% accuracy. These findings suggest that multivariate analysis of (1)H-MRSI/DCE-MRI can significantly improve the diagnostic accuracy for these pathological entities. From a more theoretical point of view, the complementation of a single biomarker approach with an integrated picture of the entire metabolic and dynamic profile allows for a more realistic appreciation of pathological entities.