Prostate tissue histology with computational methods

Normal prostate tissue histology

To better understand pathological changes characteristic of prostate diseases, we need to first take a look at the anatomy of healthy prostate tissue. 

As a part of the male reproductive system, the prostate is a gland that plays an important role in the generation of seminal fluid. The prostate is constituted of three histological zones: peripheral, transition and central zone. Structures adjacent to the prostate are the bladder, the prostatic urethra, the prostatic ducts and the seminal vesicles.   

Prostate gland tissue is composed of four components: nuclei, lumen, stroma and cytoplasm. Layers of epithelial cells constitute the gland boundaries and are involved in the generation of seminal fluid. Those cells are columnar in shape and have round nuclei positioned near the cell base. Lumen objects are described as empty white spaces within the glandular structure. The prostate basically is a set of tubulo-alveolar glands with lumina lined by epithelium of variable height (Singh et al., 2017).

The glandular tissue of the prostate includes secretory cells on the glandular facing side and basal cells on the basal side. The epithelial cells line a central lumen, which is filled with fluid produced by the epithelial cells (Denmeade & Isaacs, 2003).

A healthy prostate gland does not have a fixed size or shape: it can be smaller or larger, oval, round or branchy. Benign glands are characterized by large lumina and epithelial cells with prominent nuclei. The nuclei of benign prostatic gland tissue are uniformly dark or uniformly light throughout the areas and do not show prominent nucleoli (Nguyen et al., 2012b).                

In the normal prostate, prostatic stroma components vary in each zone. Stromal components include myofibroblasts, fibroblasts, collagen fibers and smooth muscle cells (Zhang et al., 2003). However, there are local differences in stroma morphology and function (e.g., gene expression) in the different zones of the prostate. Such differences may explain why cancer originates more commonly in the peripheral zone of the prostate and not in the transition zone where benign prostatic hyperplasia (BPH) usually develops. The transition zone accounts for only 10 % of prostate glandular tissue (Hägglöf & Bergh, 2012).

In cases of cancer, changes to lumen properties occur in the gland, which can be used as a feature in digital image analysis. However, prostate cancer can develop in benign basal and luminal stem cells. The aberrant proliferation of basal cells in the prostate ranges from hyperplasia to carcinoma (but carcinoma from basal cells of the prostate is rare).

Focusing on the tissue architecture in prostate cancer histology

Several pathological conditions like prostatitis, prostatic hyperplasia, nodular hyperplasia, prostatic intraepithelial neoplasia and prostatic adenocarcinoma can occur in prostate tissue. All those conditions are characterized by specific changes in tissue morphology. We take a look at how prostate glandular structures affected appear during histological analysis. 

Pathologists use the presence of atypical gland patterns as hallmarks through which they can distinguish cancerous regions from benign ones. The most common system pathologists use to grade prostate cancer is the Gleason score (Gleason, 1966).

The assigned cancer grade shows to what extent the appearance of cancer cells deviates from that of normal healthy cells. To assign  the Gleason score or Grade group to a given sample, pathologists look at biopsies taken from the prostate and grade each sample on a scale from 3 to 5. 

The two dominating Gleason grades are added together to calculate the overall Gleason score, which ranges from 6 to 10 (Chen & Zhou, 2016). 

Read more on the histologic grading of prostate cancer with automated methods in our blogpost on Pathologist advice on Prostate Cancer Microscopy Analysis.

​​​​Changes to nuclear features

Compared to normal prostate epithelial cells, the nuclei in cancerous prostate cancer cells are colored light blue and have prominent nucleoli appearing as small dark spots within the nucleus.  

Another feature prominent in prostatic tissue affected by cancer is nucleus shape irregularities or nuclear dysmorphia. These changes are caused by alterations in the nuclear lamina resulting in lobes and herniations.

Textural changes in the structure of nuclei are mostly due to alterations on the DNA level, including  gene fusions, mutations, copy number variations and translocations. These can be detected with specific staining methods such as fluorescence in situ hybridisation during histological analysis (Carleton et al., 2018). 

Further, glands in the cancer-affected region display a smaller nuclei count on the boundary of the gland as compared to non-cancerous regions. Cancerous glands tend to have only one nuclear layer on the boundary, while a normal gland contains multiple layers.Those nuclei are also characterized by a lighter blue color than those in healthy glands (Nguyen et al., 2012).

Also, cancerous prostate tissues might often display a cribriform pattern, where a number of glands are fused into one which results in the formation of nuclei clusters(Singh et al., 2017).

Cribriform refers, within this context, to a neoplastic epithelial proliferation in the form of large nests perforated by different-sized quite rounded spaces (Branca et al., 2017). This is important to note because the presence of a cribriform growth pattern in radical prostatectomy specimens has been previously associated with distant metastasis and disease-specific death from prostate cancer in patients with Gleason score 7 or higher (Kweldam et al., 2018).

Changes to lumen properties 

Lumen objects in atypical glands tend to be smaller in size and more circular than in normal glands. Blue stained mucin invading lumen objects might result in a distinct coloring on histology slides (Nguyen et al., 2012b).

Existing studies suggest that in cases of higher Gleason grade cancer a lesser density of lumen objects and a decreasing volume of lumen space can be observed in the affected tissue (Chatterjee et al., 2015; McGarry et al., 2018).

Changes to prostatic stroma

Cancerous prostatic tissue is composed of malignant epithelial cells and supportive stroma whose changes are important for the development of the tumor (Krušlin et al., 2015).

Cancerous stroma consists of fibroblasts, myofibroblasts, endothelial cells and immune cells, but fibroblasts and myofibroblasts are the predominant cellular entities. They play a significant role in the synthesis, deposition and remodeling of the extracellular matrix (ECM) and are in constant interaction with tumorous epithelial cells (Krušlin et al., 2015). 

With the help of various molecules of the ECM a microenvironment suitable for cancer cell proliferation, movement and differentiation is created promoting tumor growth. A complex interaction between cancer cells and various cells in the stroma plays a central role when it comes to the enhancement of tumor progression. This process is a key factor in stimulating angiogenesis and preserving cancer cell survival, proliferation and invasion (Krušlin et al., 2015). 

How does the automated histologic analysis of prostate tissue work?

Segmentation methodologies involve detecting and separating objects and structuresof interest in prostate tissue images. With the help of specialized software applications researchers are able to conduct an accurate segmentation of prostate tissue. These automated applications rely on state-of-the-art deep learning technology and facilitate a significantly faster and more efficient data collection than conventional manual methods. 

Various prostate segmentation models have been suggested in existing literature. Some of these methods rely on MR imaging data and are applied for tasks such as localizing prostate boundaries and zones, obtaining volume-related metrics and tracking disease progression. Prostate zone segmentation is used to determine cancer lesion invasion towards adjacent structures such as the urethra and the seminal vesicles (Litjens et al., 2014; Zhu et al., 2017).

Other methods relying on histology slide data and automated histopathological image analysis allow researchers to obtain valuable quantitative information on the structural features of prostate tissue. Different deep learning techniques for epithelium segmentation, nucleus segmentation, stroma and gland segmentation and lumen objects segmentation have been discussed in literature (Nguyen et al., 2012; Carleton et al.,  2018; Bulten et al., 2019).

Increase your knowledge about the use of deep-learning segmentation techniques in histopathology image analysis further by reading our blogpost on the methodology essentials. 

AI-backed methods also enable researchers to reliably classify specimens into the different stages of prostate cancer (Nguyen et al., 2012b). Yet, the varying shapes and sizes of prostatic glands often pose a major challenge to common segmentation techniques (Singh et al., 2017). 

However, with the help of advanced computational methods quantitative data related to prostatic tissue alterations can be collected. Such parameters include measures of count, size, shape and texture of tissue components as well as measures of the spatial information about the cellular microenvironment (Bhargava & Madabhushi,  2016).

Here is an overview of metrics collected with AI-based methods for prostate tissue segmentation:

Experts in prostate tissue histology share their experience with the IKOSA software

We contacted Prof. Johannes Schmid and Bernhard Hochreiter PhD, researchers at the Institute of Vascular Biology and Thrombosis Research at the Medical University of Vienna, and asked them to share their experience with the IKOSA platform regarding the automated histologic analysis of prostate tissue.

Here is what the research team reported about their recent study on prostate cancer with the help of advanced computational methods.

On the benefits of the IKOSA software

When asked about the benefits of the IKOSA software, Bernhard Hochreiter noted that he was particularly pleased with the many useful capabilities included in the IKOSA software. Especially, the option to view and process image files of different sizesincluding particularly large images had proven to be very helpful. Transforming images taken with different microscope modalities into an uniform size was no longer necessary.

The researcher also agrees that the entire analysis workflow has been faster and smoother since they implemented the IKOSA software in their lab. Being able to work remotely and perform the analysis of histologic data on an online platform has been an invaluable asset, especially during the COVID-19 pandemic.

The uses of IKOSA in applied histopathological prostate research

The researchers report how the use of the IKOSA platform helped them conduct the large-scale research project “FFG-BRIDGE Precision Histology.” The dataset used for the project consisted of complex microscopy data acquired with different imaging modalities. Especially prostate tissue images taken with multichannel fluorescence microscopes constituted the larger part of the dataset. This required an image analysis tool which supported these image formats and was flexible enough to adjust the different channels.  

Yet, as Hochreiter explains, the biggest asset of the IKOSA platform turned out to be its AI-backed analysis capability. It has helped the team avoid many mistakes, which might have occurred during manual analysis tasks. Image analysis automation has proven invaluable during the segmentation of prostate cell nuclei.

The “FFG-BRIDGE Precision Histology” study

Due to the scarcity of human prostate tissue biopsy samples and ethical considerations using patient data a large body of pre-clinical research has been conducted on animal prostate tissue samples. Making use of the similarities in mammalian prostate anatomy, a significant number of articles on prostate histology relies on mouse tissue samples (Fagerland et al., 2020; Ding et al., 2021).  

Similarly, the research team at the Medical University of Vienna used mouse prostate tissue histological images acquired with different microscope modalities like brightfield and fluorescence microscopy.