Combined prostate diffusion tensor imaging and dynamic contrast enhanced MRI at 3T--quantitative correlation with biopsy

Abstract

The purpose of this work was to compare diagnostic accuracy of Diffusion Tensor Imaging (DTI), dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) and their combination in diagnosing prostate cancer. Twenty-five patients with clinical suspicion of prostate cancer underwent MRI, prior to transrectal ultrasound-guided biopsies. MRI data were correlated to biopsy results. Logistic regression models were constructed for the DTI parameters, DCE MRI parameters, and their combination. The areas under the receiver operator characteristic curves (AUC) were compared between the models. The nonparametric Wilcoxon signed rank test was used for statistical analysis. The sensitivity and specificity values were respectively 81% (74-87%) and 85% (79-90%) for DTI and 63% (55-70%) and 90% (85-94%) for DCE. The combination "DTI or DCE MRI" had 100% (97-100%) sensitivity and 77% (69-83%) specificity, while "DTI and DCE MRI" had 44% (37-52%) sensitivity and 98% (94-100%) specificity. The AUC for DTI+DCE parameters was significantly higher than that for either DTI (0.96 vs. 0.92, P=.0143) or DCE MRI parameters (0.96 vs. 0.87, P=.00187) alone. In conclusion, the combination of DTI and DCE MRI has significantly better accuracy in prostate cancer diagnosis than either technique alone.

Crown Copyright 2010. Published by Elsevier Inc. All rights reserved.

Figures

Figure 1

Figure 1a. T2 weighted images and the corresponding <D>, FA, Ktrans and ve maps from a 67 years old patient with PSA = 7.1 ng/mL, who had biopsy proven adenocarcinoma in the right midgland (top) and negative biopsies in another patient (69 years old, PSA = 4.6 ng/mL ) (bottom). The tumor in the right midgland of the peripheral zone is clearly visible as a hypointense area on the T2 weighted image and <D> map, and as a hyperintense area on the Ktrans map (top row). Note that neither FA nor ve show differences between the tumor and the normal peripheral zone, which is consistent with the average values of these parameters across the patient population in this study (see Table 2). Although Ktrans values in some areas of the central gland appear as high as in the tumor, the average Ktrans value across the patient population in the tumor was significantly higher than in the central gland. Figure 1b. AIF (left) and representative concentration vs. time curves (right) from PCa (crosses), normal PZ (open triangles) and CG (open circles) extracted from the DCE data shown at the top of Figure 1a. Zero on the time axis corresponds to the bolus arrival time in the external femoral artery. Note that the concentration vs. time curves were adjusted for the bolus arrival time.

Figure 1

Figure 1a. T2 weighted images and the corresponding <D>, FA, Ktrans and ve maps from a 67 years old patient with PSA = 7.1 ng/mL, who had biopsy proven adenocarcinoma in the right midgland (top) and negative biopsies in another patient (69 years old, PSA = 4.6 ng/mL ) (bottom). The tumor in the right midgland of the peripheral zone is clearly visible as a hypointense area on the T2 weighted image and <D> map, and as a hyperintense area on the Ktrans map (top row). Note that neither FA nor ve show differences between the tumor and the normal peripheral zone, which is consistent with the average values of these parameters across the patient population in this study (see Table 2). Although Ktrans values in some areas of the central gland appear as high as in the tumor, the average Ktrans value across the patient population in the tumor was significantly higher than in the central gland. Figure 1b. AIF (left) and representative concentration vs. time curves (right) from PCa (crosses), normal PZ (open triangles) and CG (open circles) extracted from the DCE data shown at the top of Figure 1a. Zero on the time axis corresponds to the bolus arrival time in the external femoral artery. Note that the concentration vs. time curves were adjusted for the bolus arrival time.

Figure 2

Figure 2a. ROC curves generated for each MRI parameter. The average diffusivity had the largest area under the ROC curve. ROC curves for the FA and ve lay close to the diagonal, suggesting that these parameters alone are not particularly useful in distinguishing between cancer and normal PZ. Figure 2b. ROC curves generated with the logistic regression model for: DTI parameters (red), DCE MRI parameters (blue), and DTI + DCE MRI parameters (green). The AUC calculated for DTI + DCE MRI parameters was significantly higher than that for either the DTI or the DCE MRI parameters alone.

Figure 2

Figure 2a. ROC curves generated for each MRI parameter. The average diffusivity had the largest area under the ROC curve. ROC curves for the FA and ve lay close to the diagonal, suggesting that these parameters alone are not particularly useful in distinguishing between cancer and normal PZ. Figure 2b. ROC curves generated with the logistic regression model for: DTI parameters (red), DCE MRI parameters (blue), and DTI + DCE MRI parameters (green). The AUC calculated for DTI + DCE MRI parameters was significantly higher than that for either the DTI or the DCE MRI parameters alone.