Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer. Issue 1 (25th June 2015)

• Record Type: Journal Article
• Authors: Singanamalli, Asha; Rusu, Mirabela; Sparks, Rachel E.; Shih, Natalie N.C.; Ziober, Amy; Wang, Li‐Ping; Tomaszewski, John; Rosen, Mark; Feldman, Michael; Madabhushi, Anant
• Is Part Of: Journal of magnetic resonance imaging. Volume 43:Issue 1(2016)
• Page Start: 149
• Page End: 158
• Publication Date: 2015-06-25
• Subjects: imaging biomarkers -- prostate cancer -- quantitative histomorphometry -- microvessel architecture -- DCE MRI -- Gleason grades; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.24975
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. Methods: This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy‐seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. Results: A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho = −0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = −0.52). Conclusion: Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS. J. MAGN. RESON. IMAGING 2016;43:149–158.