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. 2022 Sep 2:2022:9847962.
doi: 10.34133/2022/9847962. eCollection 2022.

Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics

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Deep UV Microscopy Identifies Prostatic Basal Cells: An Important Biomarker for Prostate Cancer Diagnostics

Soheil Soltani et al. BME Front. .

Abstract

Objective and Impact Statement. Identifying benign mimics of prostatic adenocarcinoma remains a significant diagnostic challenge. In this work, we developed an approach based on label-free, high-resolution molecular imaging with multispectral deep ultraviolet (UV) microscopy which identifies important prostate tissue components, including basal cells. This work has significant implications towards improving the pathologic assessment and diagnosis of prostate cancer. Introduction. One of the most important indicators of prostate cancer is the absence of basal cells in glands and ducts. However, identifying basal cells using hematoxylin and eosin (H&E) stains, which is the standard of care, can be difficult in a subset of cases. In such situations, pathologists often resort to immunohistochemical (IHC) stains for a definitive diagnosis. However, IHC is expensive and time-consuming and requires more tissue sections which may not be available. In addition, IHC is subject to false-negative or false-positive stains which can potentially lead to an incorrect diagnosis. Methods. We leverage the rich molecular information of label-free multispectral deep UV microscopy to uniquely identify basal cells, luminal cells, and inflammatory cells. The method applies an unsupervised geometrical representation of principal component analysis to separate the various components of prostate tissue leading to multiple image representations of the molecular information. Results. Our results show that this method accurately and efficiently identifies benign and malignant glands with high fidelity, free of any staining procedures, based on the presence or absence of basal cells. We further use the molecular information to directly generate a high-resolution virtual IHC stain that clearly identifies basal cells, even in cases where IHC stains fail. Conclusion. Our simple, low-cost, and label-free deep UV method has the potential to improve and facilitate prostate cancer diagnosis by enabling robust identification of basal cells and other important prostate tissue components.

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Conflict of interest statement

Dr. Robles has a financial interest in Cellia Science, the company that holds a licensing agreement for part of the technology described in this study. The terms of this arrangement have been reviewed and approved by Georgia Tech in accordance with its conflict of interest policies.

Figures

Figure 1
Figure 1
Summary of data processing steps and average spectral data: (a) the 4 principal components resulting from 130 million spectra from representative select regions. (b) Elevation integral of the two-dimensional molecular histogram calculated using projection of multispectral data on principal components 2, 3, and 4. The specified points correspond to the average azimuthal coordinates of inflammation (yellow), basal cells (red), luminal epithelial cells (green), and cytoplasm/stroma (blue). The arrows illustrate the azimuthal angle interval attributed to each component. The inset shows a schematic of coordinate transformation from Cartesian to spherical coordinates. (c) Calculated average spectra from basal cells, luminal epithelial cells, cytoplasm/stroma, and inflammation. (d, e) A representative example of high-contrast molecular colorization using this geometrical representation of the PCs from a prostate tissue region including various components. The insets show the two-dimensional geometrical representation angular distribution histogram of the molecular images using PC 1, 2, and 3 and PC 2, 3, and 4, respectively.
Figure 2
Figure 2
Virtual IHC and 4-channel molecular map colorization for a (a–d) benign and (e–h) prostate cancer tissue. (a, b, e, and f) Show H&E- and p63-stained sections of the same regions (from adjacent sections). As clearly observed, the virtual and stained p63 images are in excellent agreement. Color coding for the 4-channel molecular map in (d, h) is based on the azimuth angle as shown in Figure 1(c) and the cell segmentation procedure described above (red, yellow, green, and blue represent basal cells, inflammation, luminal epithelial cells, and stroma and cytoplasm, respectively).
Figure 3
Figure 3
Virtual IHC and 4-channel molecular representation of an entrapped high-grade PIN gland. The existence of basal cells clearly identifies the entrapped high-grade PIN gland with adjacent cancer glands. H&E and p63 stains of the same region are shown for comparison.
Figure 4
Figure 4
Virtual IHC and 4-channel molecular representation of benign prostate glands with basal cell hyperplasia.
Figure 5
Figure 5
Virtual IHC and 4-channel molecular representation of (a–d) crowded small foci of benign glands with negative p63 expression and (e–h) two cystic atrophic glands with negative p63 expression. Cystic atrophic glands have very few basal cells and might be misinterpreted as cancer. (c–g) Virtual p63 images from deep UV microscopy clearly show the basal cells while the p63 IHC fails to stain them. H&E and p63 stains from adjacent sections are shown for comparison.
Figure 6
Figure 6
Virtual H&E and p63 stains of a representative prostate region with foci of small benign glands (mimics Gleason Grade 3 prostatic adenocarcinoma). (a) H&E and (b) virtual H&E rendered using a deep learning model as described in Ref. [29]. (c) p63 and (d) virtual p63 rendered using a geometrical representation of PCA.

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References

    1. https://seer.cancer.gov/statfacts/html/prost.html.
    1. Surveillance,Epidemiology, and End Results (SEER) Program, National Cancer Institute: Cancer Stat Facts: Prostate Cancer, 2021
    1. Sakr W. A., Grignon D. J., Crissman J. D., Heilbrun L. K., Cassin B. J., Pontes J. J., and Haas G. P., “High grade prostatic intraepithelial neoplasia (HGPIN) and prostatic adenocarcinoma between the ages of 20-69: an autopsy study of 249 cases,” In vivo (Athens, Greece), vol. 8, no. 3, pp. 439–443, 1994 - PubMed
    1. Findakly D., and Wang J., “Misdiagnosis of small cell prostate cancer: lessons learned,” Cureus, vol. 12, no. 5, pp. e8356–e8356, 2020 - PMC - PubMed
    1. Beltran L., Ahmad A. S., Sandu H., Kudahetti S., Soosay G., Møller H., Cuzick J., Berney D. M., and Transatlantic Prostate Group, “Histopathologic false-positive diagnoses of prostate cancer in the age of immunohistochemistry,” The American Journal of Surgical Pathology, vol. 43, no. 3, pp. 361–368, 2019 - PMC - PubMed

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