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

• Type of Material: Article
• Authors: Soheil Soltani, Georgia Institute of Technology; Ashkan Ojaghi, Georgia Institute of Technology; Hui Qiao, Tsinghua University; Nischita Kaza, Georgia Institute of Technology; Xinyang Li, Tsinghua University; Qionghai Dai, Tsinghua University; Adeboye Osunkoya, Emory University; Francisco Robles, Emory University
• Language: English
• Date: 2022-06-04
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-022-13332-9
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 12
• Issue: 1
• Start Page: 9329
• End Page: 9329
• Grant/Funding Information: This work was supported by the Burroughs Wellcome Fund (CASI BWF 1014540), National Science Foundation (NSF CBET CAREER 1752011), Galloway Foundation, and Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University.
• Keywords: Multidisciplinary Sciences; INTEROBSERVER VARIABILITY; CARCINOMA; Science & Technology; LESIONS; IDENTIFICATION; CLASSIFICATION; Science & Technology - Other Topics; PROTEIN; SPECTROSCOPY; EXPRESSION; BIOLOGY; FIXATIVES
• Research Categories: Engineering, Biomedical

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.