Due to the subject nature of histopathology, there is a significant inter-observer discordance for the differentiation between low-risk prostate cancer (Gleason score ≤ 6), which can be left without treatment, and high-risk prostate cancer (Gleason score >6), which requires active treatment. Our previous study using Raman spectromicroscopy reveals that cholesteryl ester accumulation underlies human prostate cancer aggressiveness. However, Raman spectromicroscopy could only provide compositional information of certain lipid droplets of interest, which overlooked cell-to-cell variation and hindered translation to accurate automated diagnosis. Here, we demonstrated quantitative mapping of cholesteryl ester molar percentage in human prostate cancer tissues using hyperspectral stimulated Raman scattering microscopy that renders compositional information for every pixel in the image. Specifically, hundreds of SRS images at Raman shift between 2800~3000 cm-1 were taken, and multivariate curve resolution algorism was used to retrieve concentration images of lipid, lipofuscin, and protein. We found that the height ratio between the prominent cholesterol band at 2870 cm-1 and the CH2 stretching band at 2850 cm-1 was proportional to the molar percentage of cholesteryl ester present in the total lipids. Based on the calibration curve, we were able to quantitatively map cholesteryl ester level in intact prostate cancer tissues. Our data showed that not only the amount of cholesteryl ester-rich lipid droplets, but also the CE molar percentage, was significantly greater in prostate cancer tissues with Gleason score > 6 compared to the ones with Gleason score ≤ 6. Our study offers an opportunity towards more accurate prostate cancer diagnosis.