  Identifying prostate cancer patients that are harboring aggressive forms of
prostate cancer remains a significant clinical challenge. To shed light on this
problem, we develop an approach based on multispectral deep-ultraviolet (UV)
microscopy that provides novel quantitative insight into the aggressiveness and
grade of this disease. First, we find that UV spectral signatures from
endogenous molecules give rise to a phenotypical continuum that differentiates
critical structures of thin tissue sections with subcellular spatial
resolution, including nuclei, cytoplasm, stroma, basal cells, nerves, and
inflammation. Further, we show that this phenotypical continuum can be applied
as a surrogate biomarker of prostate cancer malignancy, where patients with the
most aggressive tumors show a ubiquitous glandular phenotypical shift. Lastly,
we adapt a two-part Cycle-consistent Generative Adversarial Network to
translate the label-free deep-UV images into virtual hematoxylin and eosin
(H&E) stained images. Agreement between the virtual H&E images and the gold
standard H&E-stained tissue sections is evaluated by a panel of pathologists
who find that the two modalities are in excellent agreement. This work has
significant implications towards improving our ability to objectively quantify
prostate cancer grade and aggressiveness, thus improving the management and
clinical outcomes of prostate cancer patients. This same approach can also be
applied broadly in other tumor types to achieve low-cost, stain-free,
quantitative histopathological analysis.
