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摘要
OBJECTIVE. The objective of this study was to explore whether diffusion kurtosis imaging (DKI) combined with DWI can improve the performance of DWI in detection and assessment of aggressiveness of prostate cancer (PCa). @@@ MATERIALS AND METHODS. One hundred twenty patients with complete DK and MR images and diagnosis confirmed by prostate biopsy, including 67 patients with PCa and 53 patients with benign prostatic hyperplasia (BPH), were retrospectively analyzed. The patients with PCa were divided into a low-grade PCa group (Gleason score [GS] s 3 + 3) and intermediate- and high-grade PCa group (GS >= 3 + 4). A DKI-derived parameter (i.e., apparent kurtosis coefficient [K-app]) and a DWI-derived parameter (i.e., apparent diffusion coefficient aP [ADC]) were fitted. The P intraclass correlation coefficient (ICC) test, t test, ROC curves, Delong test, and Spearman correlation were performed. @@@ RESULTS. Ninety ROIs in 67 patients with PCa were drawn, including ROIs in 37 low-grade tumors and ROIs in 53 intermediate- and high-grade tumors. PCa and intermediate- and high-grade PCa had significantly lower ADC values and significantly higher K-app values than BPH and low-grade PCa (p < 0.01 for all). The AUCs of K-app were significantly lower than the Mrs of ADC in the diagnosis (0.947 vs 0.978, p < 0.001) and grading (0.689 vs 0.894, p = 0.008) of PCa. The AL Vs of the combination of the two metrics were significantly higher than the AUCs of K-app for the diagnosis (0.979 vs 0.947, p = 0.013) and grading (0.934 vs 0.689, p < 0.001) of PCa and were higher than the AUCs of ADC without significance between groups (both p > 0.05). The combination of the two metrics significantly increased the specificity in grading of PCa compared with K app alone (0.838 vs 0.730, p = 0.035). @@@ CONCLUSION. Both ADC and K-app can be used as quantitative parameters in detection p and assessment of aggressiveness of PCa. The combination of DKI and DWI showed no significant superiority to DWI alone in detection and assessment of the aggressiveness of PCa.
