• N Tuncbilek, H M Karakas, and S Altaner.
• Department of Radiology, Trakya University School of Medicine, 22030 Edirne, Turkey. drtuncbilek@hotmail.com
• Abdom Imaging. 2004 Mar 1; 29 (2): 166-72.

AbstractThe relations of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters to microvessel density (MVD), histologic grade, and presence of metastasis were evaluated to establish new prognostic indicators in colorectal cancer (CRC). Fast-low angle shot DCE-MRI parameters (time-intensity curves, TICs; maximal relative enhancement within the first minute, E(max/1); maximal relative enhancement of the entire study, Emax; steepest slope of the contrast enhancement curve; and time to peak enhancement) of 21 CRCs (seven Duke stage B, 12 Duke stage C, and two Duke stage D) were retrospectively evaluated and correlated with corresponding postoperative MVD measurements, histologic grades, and presence of metastasis at 2 years. TICs were classified as type A in nine (43%), type B in seven (33%), and type C in five cases (24%). There was a significant difference between TIC types with regard to MVD (p < 0.05-0.001). Time to peak enhancement, steepest slope of TIC, and E(max/1) were strongly correlated with MVD (r = -0.765, p < 0.01; r = 0.681, p < 0.01; r = 0.634, p < 0.01; respectively). MVD, steepest slope of the enhancement curve, E(max/1), and Emax strongly correlated with histologic grade (r = 0.475, p < 0.05; r = 0.683, p < 0.01; r = 0.687, p < 0.01; r = 0.791, p < 0.01; respectively). There was a significant difference between groups of patients with and without metastasis with regard to histologic grade (p < 0.05) and two of the DCE-MRI parameters (p < 0.005 for E(max/1) and p < 0.05 for time to peak enhancement). Discriminant analysis correctly predicted the metastatic occurrence at 2 years in 90.5% of cases using E(max/1) (p < 0.001). Histologic grade resulted in lower rates of discrimination (66.7%; p < 0.05). DCE-MRI parameters may help in the prediction of MVD and histologic grade in CRC and may be used to predict therapeutic outcome.

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