Morphometric signature differences in nuclei of Gleason pattern 4 areas in Gleason 7 prostate cancer with differing primary grades on needle biopsy

Histopathology of Prostate Cancer and its Precursors

Starting in the mid-1970s, we formed a group of pathologists with a major interest in uropathology. Originally, it included 2 (R.M. and M.S.). In the years the followed, the group was enlarged to include 4 more people, 2 in the mid- and late-1980s (A.L.B. and L.C.) and another in the mid-1990s (R.Ma.); a sixth (A.C.) joined the group ∼5 years ago. Two have reached the retirement age (R.M. and M.S.), while others are in the process of joining the group to replace them. A fruitful collaboration spanned for ∼45 years. This contribution is based on a series of personal recollections of the successive changes in the interpretation of prostate cancer and its precursors, starting in the mid-1970s. Here we have retraced our involvement steps, sharing issues related to them with a junior uropathologist (A.C.).

Narrative review of prostate cancer grading systems: will the Gleason scores be replaced by the Grade Groups?

The Gleason grading system, proposed by Dr. Donald F. Gleason in 1966, is one of the most important prognostic factors in men with prostate cancer (PCa). At consensus conferences held in 2005 and 2014, organized by the International Society of Urological Pathology (ISUP), the system was modified to reflect the current diagnostic and therapeutic approaches. In particular, in the 2014 Conference, it was recognized that there were weaknesses with the original and the 2005 ISUP modified Gleason systems. Based on the results of a research conducted by Prof. JI Epstein and his group, a new grading system was proposed by the ISUP in order to address some of such deficiencies: i.e., the five distinct Grade Groups (GGs). Since 2014, results of studies have been published by different groups and societies, including the Genitourinary Pathology Society (GUPS), giving additional support to the prognostic role of the architectural Gleason patterns and, in particular, of the GGs. A revised GG system, taking into account the percentage of Gleason pattern (GP) 4, cribriform and intraductal carcinoma, tertiary GP 5, and reactive stroma grade, has shown to have some advantages, however not ready for adoption in the current practice. The aim of this contribution was to review the major updates and recommendations regarding the GPs and GSs, as well as the GGs, trying to give an answer to the following questions: “How has the grade group system been used in the routine?” and “will the Gleason scoring system be replace by the grade groups?” We also discussed the potential implementation in the future of molecular pathology and artificial intelligence in grading to further define risk groups in patients with PCa.

[Grading of prostate cancer. For a more precise prognosis]

The simplification of the Gleason grading system, together with the reclassification of some of its patterns, has improved correlation with the clinical reality of prostate cancer, whilst maintaining the basic principles established fifty years ago. The subsequent grouping of the patterns into five degrees has allowed a more rational unification and enhanced the physician/patient communication. However, a greater precision in the assessment of the prognosis for each patient is still necessary and, to this end, elements that allow greater discrimination are continually being sought. The purpose of this brief review is to discuss the value and possible future incorporation in international recommendations of the percentage of pattern 4, the quantification of the cribriform pattern, the detection of intraductal carcinoma, the regrouping of some 'scores' and the possible stratification of the grade group 1.

Does prostate acinar adenocarcinoma with Gleason Score 3+3=6 have the potential to metastasize?

Background

There is a worldwide debate involving clinicians, uropathologists as well as patients and their families on whether Gleason score 6 adenocarcinoma should be labelled as cancer.

Case description

We report a case of man diagnosed with biopsy Gleason score 6 acinar adenocarcinoma and classified as low risk (based on a PSA of 5 ng/mL and stage cT2a) whose radical prostatectomy specimen initially showed organ confined Gleason score 3 + 3 = 6, WHO nuclear grade 3, acinar adenocarcinoma with lymphovascular invasion and secondary deposit in a periprostatic lymph node. When deeper sections were cut to the point that almost all the slice present in the paraffin block was sectioned, a small tumor area (<5% of the whole tumor) of Gleason pattern 4 (poorly formed glands) was found in an extraprostatic position.

Conclusion

The epilogue was that the additional finding changed the final Gleason score to 3 + 3 = 6 with tertiary pattern 4 and the stage to pT3a.

Virtual Slides

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_190

Implications of the International Society of Urological Pathology modified Gleason grading system

CONTEXT

Histologic grading is the clinically most useful tissue-based predictor of prognosis for prostate cancer. Over the years, there has been a gradual shift in how the Gleason grading is applied in practice, with a general trend toward upgrading. A consensus conference was organized in 2005 by the International Society of Urological Pathology (ISUP) for standardizing both the perception of histologic patterns and how the grade information is compiled and reported.

OBJECTIVE

To review the implications of the ISUP modified Gleason grading system.

DATA SOURCES

Personal experience and review of the current literature.

CONCLUSIONS

The recommendations regarding pattern interpretation and reporting are summarized. The practical consequences of the ISUP modification of the Gleason grading are reported. The prognostic importance of the Gleason score, its reproducibility, and its preoperative assessment are discussed. Subsequent proposals for slight modifications to the ISUP grading system are described.

Nuclear morphometry, nucleomics and prostate cancer progression

Prostate cancer (PCa) results from a multistep process. This process includes initiation, which occurs through various aging events and multiple insults (such as chronic infection, inflammation and genetic instability through reactive oxygen species causing DNA double-strand breaks), followed by a multistep process of progression. These steps include several genetic and epigenetic alterations, as well as alterations to the chromatin structure, which occur in response to the carcinogenic stress-related events that sustain proliferative signaling. Events such as evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis are readily observed. In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system, appear to be involved. In addition, when cancer spread and metastasis occur, the 'tumor microenvironment' in the bone of PCa patients may provide a way to sustain dormancy or senescence and eventually establish a 'seed and soil' site where PCa proliferation and growth may occur over time. When PCa is initiated and progression ensues, significant alterations in nuclear size, shape and heterochromatin (DNA transcription) organization are found, and key nuclear transcriptional and structural proteins, as well as multiple nuclear bodies can lead to precancerous and malignant changes. These series of cellular and tissue-related malignancy-associated events can be quantified to assess disease progression and management.

A high-throughput active contour scheme for segmentation of histopathological imagery

In this paper a minimally interactive high-throughput system which employs a color gradient based active contour model for rapid and accurate segmentation of multiple target objects on very large images is presented. While geodesic active contours (GAC) have become very popular tools for image segmentation, they tend to be sensitive to model initialization. A second limitation of GAC models is that the edge detector function typically involves use of gray scale gradients; color images usually being converted to gray scale, prior to gradient computation. For color images, however, the gray scale gradient image results in broken edges and weak boundaries, since the other channels are not exploited in the gradient computation. To cope with these limitations, we present a new GAC model that is driven by an accurate and rapid object initialization scheme; hierarchical normalized cuts (HNCut). HNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. HNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale, mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial contour for a GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel. We show that the color gradient based edge-detector function results in more prominent boundaries compared to the classical gray scale gradient based function. By integrating the HNCut initialization scheme with color gradient based GAC (CGAC), HNCut-CGAC embodies five unique and novel attributes: (1) efficiency in segmenting multiple target structures; (2) the ability to segment multiple objects from very large images; (3) minimal human interaction; (4) accuracy; and (5) reproducibility. A quantitative and qualitative comparison of the HNCut-CGAC model against other state of the art active contour schemes (including a Hybrid Active Contour model (Paragios-Deriche) and a region-based AC model (Rousson-Deriche)), across 196 digitized prostate histopathology images, suggests that HNCut-CGAC is able to outperform state of the art hybrid and region based AC techniques. Our results show that HNCut-CGAC is computationally efficient and may be easily applied to a variety of different problems and applications.

Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features

PURPOSE

To investigate relationships between magnetic resonance (MR) imaging measurements and the underlying composition of normal and malignant prostate tissue.

MATERIALS AND METHODS

Twenty-four patients (median age, 63 years; age range, 44-72 years) gave informed consent to be examined for this research ethics board-approved study. Before undergoing prostatectomy, patients were examined with T2-weighted, diffusion-weighted, T2 mapping, and dynamic contrast material-enhanced MR imaging at 1.5 T. Maps of apparent diffusion coefficient (ADC), T2, volume transfer constant (K(trans)), and extravascular extracellular space (v(e)) were calculated. Whole-mount hematoxylin-eosin-stained sections were generated and digitized at histologic resolution. Percentage areas of tissue components (nuclei, cytoplasm, stroma, luminal space) were measured by using image segmentation. Corresponding regions on MR images and histologic specimens were defined by using anatomically defined segments in peripheral zone (PZ) and central gland tissue. Cancer and normal PZ regions were identified at histopathologic analysis. Each MR parameter-histologic tissue component pair was assessed by using linear mixed-effects models, and cancer versus normal PZ values were compared by using nonparametric tests.

RESULTS

ADC and T2 were inversely related to percentage area of nuclei and percentage area of cytoplasm and positively related to percentage area of luminal space (P < or = .01). These trends were reversed for K(trans) (P < .001). K(trans) had a significantly negative (P = .01) slope versus percentage area of stroma, and v(e) had a positive (P = .008) slope versus percentage area of stroma. The v(e) was inversely proportional to the percentage area of nuclei (P = .05). All MR imaging parameters (P < or = .05) and the percentage areas of all tissue components (P < or = .001) except stroma (P > .48) were significantly different between cancer and normal PZ tissue.

CONCLUSION

MR imaging-derived parameters measured in the prostate were significantly related to the proportion of specific histologic components that differ between normal and malignant PZ tissue. These relationships may help define imaging-related histologic prognostic parameters for prostate cancer.