Multiparametric MRI for prostate cancer detection: Preliminary results on quantitative analysis of dynamic contrast enhanced imaging, diffusion-weighted imaging and spectroscopy imaging

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• 作者：Roberta Fusco^a ; ^{r.fusco@istitutotumori.na.it" class="auth_mail" title="E-mail the corresponding author} ; Mario Sansone^b ; Mario Petrillo^a ; Sergio Venanzio Setola^a ; Vincenza Granata^a ; Gerardo Botti^c ; Sisto Perdonà ; ^d ; Valentina Borzillo^a ; Paolo Muto^a ; Antonella Petrillo^a
• 关键词：Prostate cancer ; Magnetic resonance imaging ; Multiparametric analysis ; Cancer detection
• 刊名：Magnetic Resonance Imaging
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：34
• 期：7
• 页码：839-845
• 全文大小：1079 K

文摘

Early promising data suggest that combined use of both morphological and functional MRI (multi-parametric MR, mpMRI) including MRSI, DWI and DCE may be of additional value for prostate cancer localization and its local staging. The objective of this paper is to evaluate the diagnostic performance of mpMRI in the detection of prostate cancer.

Methods

Thirty-one consecutive male patients were screened to be enrolled in a single center prospective observational study. All eligible patients underwent multi-parametric MRI and TRUS (Trans Rectal Ultra Sound) guided prostate biopsies. A register, approved by the Institutional Ethics Committee, included patients enrolled in this study. All patients who decided to undergo the MRI examination signed an explicit informed consensus. MRI data were aligned on a common spatial grid and several functional parameters (perfusion, diffusion and metabolic parameters) were computed. Statistical analysis was conducted in order to compare mpMRI with biopsy-based analysis.

Results

Statistically significant differences between median values in high Gleason score (≥ 5) and low Gleason score (< 5) to Wilcox on rank sum test were obtained for MRSI parameters and for plasma fraction (Tofts model) of DCE-MRI. The area under curve obtained with ROC analysis showed that the best-performing single-parameter was v_p (plasma fraction of Tofts model), while the best parameters combination to discriminate the area with high Gleason score were (Cho + Cr)/Cit and Cho + Cr. Linear Discrimination Analysis showed that the best results were obtained considering the linear combination of all MRSI parameters and the linear combination of all features (perfusion, diffusion and metabolic parameters).

Conclusions

In conclusion, our findings showed that by combining morphological MRI, DWI, DCE-MRI and MRSI, an increase in sensitivity and specificity correlated to biopsy Gleason grade could be obtained. Furthermore, morphological and functional MRI could have a diagnostic role in patients with prostate cancer, identifying those patients who will have a negative work-up and those patients at high risk for a high Gleason score cancer of the prostate.