ObjectiveTo investigate the feasibility of using the ratio of (Cho + Cr)/Cit derived by MRS to predict the differentiation grades of prostatic cancer and Gleason grading.Methods Five postoperative prostate specimens were spitted and layered according to the region of interest of MRS inspections.The correlation between the CC/C values of each region of interest in each layer and the Gleason scores of the corresponding pathological sections was analyzed.The optimum diagnostic cutoff value was determined by conducting the hypothesis test of the area below the ROC curve of the well and moderately differentiated groups and poorly differentiated ones on the basis of CC/C values with a Spearman test.Results A total of 90 regions with valid pathologic diagnosis were obtained,70 cancer-affected and 20 cancer-free.In MRS,a CC/C value above 0.86 was used as a criterion for defining a cancer-affected region As a result,65 cancer-affected regions and 25 cancer-free regions were identified,among which pathologic diagnosis confirmed 59 and 14,respectively.Spearman′s rank correlation analysis revealed that the CC/C values of the prostatic carcinoma had significant positive correlation with Gleason scores ( r =0.746,P =0.000).For the well and moderately differentiated groups,the hypothesis test about the cutoff value,which was obtained by calculating the area below the ROC curve,was of no statistical significance.For the poorly differentiated groups,the optimum cutoff value was defined as 0.948,and the sensitivity and specificity were 81.4％ and 75.0％,respectively.It was also observed that the Gleason scores of the poorly differentiated endemic regions had positive correlation with the CC/C values ( r =0.605,P =0.000 ),suggesting that CC/C value was associated with the differentiation grade of the poorly differentiated prostatic cancer.When CC/C value was above 0.948,the poorly differentiated prostatic cancer was typically detected and Gleason score was often above 7.Conclusions CC/C values has positive correlation with Gleason scores.MRS may be used to predict the differentiation of prostate cancer.