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Classification of prostatic diseases by means of multivariate analysis on in vivo proton MRSI and DCE‐MRI data
Author(s) -
Valerio Mariacristina,
Panebianco Valeria,
Sciarra Alessandro,
Osimani Marcello,
Salsiccia Stefano,
Casciani Lorena,
Giuliani Alessandro,
Bizzarri Mariano,
Di Silverio Franco,
Passariello Roberto,
Conti Filippo
Publication year - 2009
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/nbm.1408
Subject(s) - multivariate statistics , magnetic resonance spectroscopic imaging , magnetic resonance imaging , multivariate analysis , pathological , in vivo , proton magnetic resonance , medicine , nuclear magnetic resonance , computer science , radiology , pathology , physics , machine learning , biology , microbiology and biotechnology
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. Copyright © 2009 John Wiley & Sons, Ltd.