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. 2022 Jun 4;12(1):9329.
doi: 10.1038/s41598-022-13332-9.

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

Soheil Soltani  1 , Ashkan Ojaghi  1 , Hui Qiao  2 , Nischita Kaza  3 , Xinyang Li  2 , Qionghai Dai  2 , Adeboye O Osunkoya  4   5 , Francisco E Robles  6   7   8
Affiliations

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

Soheil Soltani et al. Sci Rep. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Processing of multi-spectral deep UV images using the two proposed methods. Top: geometrical representation of principal component analysis. Bottom: Unsupervised content-preserving transformation for optical microscopy. (a) A representative multi-spectral deep UV transmission data cube taken at 220, 255, 280 and 300 nm. (b) The 4 principal components resulting from 130 million spectra from representative select regions. (c) Calculated projections of the data cube on the principal components. (d) Schematic of data conversion from Cartesian to Spherical coordinates. (e) Representative 2D histogram of a data cube using azimuthal and elevation coordinates. (f) Schematic of trained Neural network. (g) Representative virtually H&E stained prostate tissue (Network output).
Figure 2
Figure 2
A representative demonstration of two “optical stains”. Colorization scheme using (a) principal components 1, 2 and 3 in Elevation direction and (b) principal components 2,3 and 4 in Azimuth direction. Scale bar: 150 μm. Comparison of the two color-coding schemes for (c) a nerve, (d) a region with inflammation (e) an entrapped benign prostate gland surrounded by Gleason Grade 3 and 4 cancer glands. (f) Prostate cancer glands with Gleason Grade 3 next to a Gleason Grade 4 glomeruloid gland. All rectangles are 210 µm x260µm.
Figure 3
Figure 3
Comparison of the two “optical stains” with corresponding H&E-stained tissue scans from various prostate tissue structures. (a) Benign gland. (b) High grade Prostatic intraepithelial neoplasia (PIN) region. (c) Cancer region with Gleason Grade 3 glands. (d) Cancer region with Cribriform Gleason Grade 4 region. (e) Region with Gleason Grade 5. (f) Region with necrosis inside a Gleason Grade 5 cancer gland. Necrosis is clearly distinguishable from the cancer cells on the left side of the image. (g) Inflammation. (h) Red blood cells.
Figure 4
Figure 4
Comparison of malignancy maps from different prostate regions with corresponding H&E scans. Insets show the 2D histograms for comparison. As clearly evident from Fig. 4 the malignancy optical stain shows diagnostic capabilities complimentary to H&E, where only morphological parameters are considered. In (b and d) we have manually removed stroma and inflammation regions to aid visibility. The cell-size red regions on the edges of the glands are originating from two sources: 1-ill-formed fused type Gleason Grade 4 regions have spread around benign glands and in fact it is an indicative of existence of cancer. These regions are sometimes missed by pathologists in H&E analysis 2- Existence of inflammation cells that were impossible to remove with manual segmentation. These inflammation cells are limited in number and do not contain any diagnostically important information and they are mostly in stromal region. (Scale bar: 200 μm).
Figure 5
Figure 5
Absolute and relative CoM azimuthal angles serves as a personalized malignancy biomarker and reveal unique glandular phenotypes. (a) Comparison of absolute maximum peak azimuthal coordinates of integrated histograms of benign regions for 15 patients. (b) Cumulative boxplots for calculated relative azimuthal shift for different prostate cancer grades in 15 patients. (c) Barplots for calculated relative azimuthal shift for different prostate cancer grades in 15 patients with the benign region used as reference for each patient. In (b and c) it is clear that the more aggressive phenotypes have an opposite shift even for lower grades of cancer. In (b) the red dotted line is the threshold of the shift for the more aggressive cancer regions on the opposite direction.
Figure 6
Figure 6
Schematic of colorization process and the UTOM method For the transformation from UV to HE, input channels N = 4, and output channels M = 3. Each coral rectangle represents a feature map extracted by corresponding convolutional kernels. The generator is a multi-layer residual network with downsampling input layers and upsampling output layers. The discriminator (PatchGAN classifier) uses multiple strided convolution for abstract representation. It generates a matrix, in which each element corresponds to a patch in the input image. The ultimate output is the average of the loss over all patches.
Figure 7
Figure 7
Comparison of translated virtual H&E images and corresponding H&E stained scans. Examples of (b)–(d) Two predicted output virtual images (a) and (c) along with their reference H&E images. (e)–(p) show three selected zoomed regions for each area. These regions have been selected to compare features on both H&E and translated virtual H&E images.
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