Most prostate cancers (PCa) are slowly growing, and only the aggressive ones require early diagnosis and effective treatment. The current standard for PCa diagnosis remains histopathology. Nonetheless, for the differentiation between Gleason score 6 (low-risk PCa), which can be left without treatment, and Gleason score 7 (high-risk PCa), which requires active treatment, the inter-observer discordance can be up to 40%. Our previous study reveals that cholesteryl ester (CE) accumulation induced by PI3K/AKT activation underlies human PCa aggressiveness. However, Raman spectromicroscopy used in this study could only provide compositional information of certain lipid droplets (LDs) selected by the observer, which overlooked cell-to-cell variation and hindered translation to accurate automated diagnosis. Here, we demonstrated quantitative mapping of CE level in human prostate tissues using hyperspectral stimulated Raman scattering (SRS) microscopy that renders compositional information for every pixel in the image. Specifically, hundreds of SRS images at Raman shift between 1620-1800 cm-1 were taken, and multivariate curve resolution algorism was used to retrieve concentration images of acyl C=C bond, sterol C=C bond, and ester C=O bond. Given that the ratio between images of sterol C=C and ester C=O (sterol C=C/C=O) is nonlinearly proportional to CE percentage out of total lipid, we were able to quantitatively map CE level. Our data showed that CE level was significantly greater in high Gleason grade compared to low Gleason grade, and could be a factor that significantly contributed to cancer recurrence. Our study provides an opportunity towards more accurate PCa diagnosis and prediction of aggressiveness.