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Prostate cancer is the most common non-cutaneous cancer in men. Moreover, identifying the most effective treatment strategy for an individual with prostate cancer strongly depends on the Gleason score (Grade group) of the disease. However, this task continues to be a significant clinical challenge in a subset of patients. Currently, the gold standard for grading prostate cancer is the Pathologist’s visual assessment of hematoxylin-and-eosin-stained histological sections, and designation of a Gleason score (Grade group) based on the top two most common Gleason grades. However, this process is subjective and thus prone to error and high variability, especially amongst non-Urologic Pathologists or those who are not aware of recent modifications to the grading system. In addition, variations in protocols for staining, could make quantitative analysis of stained tissues challenging in some cases. Therefore, there is a significant clinical need to develop additional complementary quantitative methods that can provide robust, objective, reproducible, and accurate information of the aggressiveness and grade of prostate cancer. Here we address this issue by imaging unstained tissue sections using multi-spectral deep-UV microscopy. This method enables us to obtain valuable quantitative insight into the aggressiveness and grade of the disease due to its subcellular spatial resolution and high sensitivity to many endogenous biomolecules, including nucleic acid and proteins. The approach uses a simple, fast and cost effective wide-field imaging configuration that is well-suited for pathology applications. Spectral signatures form wavelengths ranging from 220 nm to 450 nm are analyzed using a geometrical representation of principal component analysis. Our results reveal distinct morphological and molecular alterations in tissue as they progress from benign to cancerous, and as they become more aggressive (higher grade). In this research project, we delineated the design of the multispectral, deep UV microscope and described our quantitative and qualitative image analysis. Our results show that multispectral deep UV microscopy provides a quantitative measure to differentiate between prostate cancer patients with varying grades of cancer.
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