Prostate cancer histopathology using label-free multispectral deep-UV microscopy quantifies phenotypes of tumor aggressiveness and enables multiple diagnostic virtual stains

Abstract

Identifying prostate cancer patients that are harboring aggressive forms of prostate cancer remains a significant clinical challenge. Here we develop an approach based on multispectral deep-ultraviolet (UV) microscopy that provides novel quantitative insight into the aggressiveness and grade of this disease, thus providing a new tool to help address this important challenge. We find that UV spectral signatures from endogenous molecules give rise to a phenotypical continuum that provides unique structural insight (i.e., molecular maps or “optical stains") of thin tissue sections with subcellular (nanoscale) resolution. We show that this phenotypical continuum can also be applied as a surrogate biomarker of prostate cancer malignancy, where patients with the most aggressive tumors show a ubiquitous glandular phenotypical shift. In addition to providing several novel “optical stains” with contrast for disease, we also 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, thus providing multiple stains (including the gold-standard H&E) from the same unlabeled specimen. Agreement between the virtual H&E images and the 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.

Deep learning-enabled virtual histological staining of biological samples

“…c Blind inference of a trained virtual staining model. The virtual histology images are rapidly generated from label-free images using a digital computer smears 39 , as well as H&E 40 and IHC 41 staining on prostate tissue sections; photoacoustic microscopy was also demonstrated to achieve virtual H&E staining of mouse brain 42 and frozen sections of bone tissue 43 . As another example, Mayerich.…”

Deep-UV microscopy as a tool to capture intracellular dynamics

“…Initial studies demonstrated UV microscopy for live-cell nucleic acid and protein mass mapping with label-free biomolecular contrast 9,11,12 . Multispectral UV microscopy has also been used for label-free prostate tissue characterization 13 . Recently, UV microscopy has been demonstrated for robust hematology analysis including automated classification of various WBC subtypes and pseudo-colorization of blood smear images to mimic hematological staining 11,12,14,15 .…”

Building predictive disease models using extracellular vesicle microscale flow cytometry and machine learning

“…Most men are diagnosed with low‐grade, localized prostate cancer and have a 5‐year net survival rate of 100%. However, the 5‐year survival rate drops to 32% for men diagnosed with high‐grade (grade group (GG) ≥ 3), metastatic prostate cancer [ 17 , 18 ]. Current diagnostic tests, such as the prostate‐specific antigen (PSA) blood test, lack specificity for predicting grade group (GG) ≥ 3 prostate cancer.…”