The aim of the present study was to explore the association between the parameters of diffusion tensor imaging (DTI), including fractional anisotropy (FA) values, apparent diffusion coefficient (ADC) values and the diffusion tensor tractography (DTT) map, with the Gleason score of prostate cancer (PCa). A retrospective study of 50 cases of PCa confirmed by biopsy or surgical pathology was performed. Conventional magnetic resonance imaging and DTI scans were conducted in these cases. The 50 cases of PCa were divided into three groups, including low, intermediate and high grade, according to the Gleason score. Post-DTI processing was performed using Neuro 3D software, in order to measure the FA and ADC values, and map the prostate fibers. Differences in FA and ADC values among the various PCa groups were examined using analysis of variance, while the correlation of FA and ADC values with the Gleason score was studied using Pearson correlation analysis. The obtained DTT map clearly demonstrated the spatial structure of the prostate fibers. The fibers of the cancer area were dense without interruption in the low-grade group, sparse and disordered in the intermediate-grade group, and were disordered, sparse or even absent in the high-grade group. The FA values were 0.284 +/- 0.313, 0.293 +/- 0.347 and 0.369 +/- 0.347, respectively, with statistically significant differences observed among the three groups (F=234.533; P<0.05) and between each group (P<0.05). In addition, the FA value of PCa was positively correlated with the Gleason score (r=0.884; P<0.05). The ADC values of the low-, intermediate-and high-grade groups were 1.070 +/- 0.072x10(-3), 0.961 +/- 0.081x10(-3) and 0.821 +/- 0.048x10(-3), respectively, which demonstrated statistically significant differences among the three groups (F=49.987; P<0.05) and between each group (P<0.05). Furthermore, the ADC values of PCa were negatively correlated with Gleason score (r=-0.810; P<0.05). In conclusion, there was an association between DTI parameters and Gleason score, which may be used to evaluate the grading and prognosis of PCa.