Diagnosis of "Poorly Formed Glands" Gleason Pattern 4 Prostatic Adenocarcinoma on Needle Biopsy: An Interobserver Reproducibility Study Among Urologic Pathologists With Recommendations

Histopathological evaluation and grading for prostate cancer: current issues and crucial aspects

ABSTRACT

A crucial aspect of prostate cancer grading, especially in low- and intermediate-risk cancer, is the accurate identification of Gleason pattern 4 glands, which includes ill-formed or fused glands. However, there is notable inconsistency among pathologists in recognizing these glands, especially when mixed with pattern 3 glands. This inconsistency has significant implications for patient management and treatment decisions. Conversely, the recognition of glomeruloid and cribriform architecture has shown higher reproducibility. Cribriform architecture, in particular, has been linked to the worst prognosis among pattern 4 subtypes. Intraductal carcinoma of the prostate (IDC-P) is also associated with high-grade cancer and poor prognosis. Accurate identification, classification, and tumor size evaluation by pathologists are vital for determining patient treatment. This review emphasizes the importance of prostate cancer grading, highlighting challenges like distinguishing between pattern 3 and pattern 4 and the prognostic implications of cribriform architecture and intraductal proliferations. It also addresses the inherent grading limitations due to interobserver variability and explores the potential of computational pathology to enhance pathologist accuracy and consistency.

External Validation of a Digital Pathology-based Multimodal Artificial Intelligence Architecture in the NRG/RTOG 9902 Phase 3 Trial

BACKGROUND

Accurate risk stratification is critical to guide management decisions in localized prostate cancer (PCa). Previously, we had developed and validated a multimodal artificial intelligence (MMAI) model generated from digital histopathology and clinical features. Here, we externally validate this model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

OBJECTIVE

To externally validate the MMAI model on men with high-risk or locally advanced PCa treated and followed as part of a phase 3 randomized control trial.

DESIGN, SETTING, AND PARTICIPANTS

Our validation cohort included 318 localized high-risk PCa patients from NRG/RTOG 9902 with available histopathology (337 [85%] of the 397 patients enrolled into the trial had available slides, of which 19 [5.6%] failed due to poor image quality).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Two previously locked prognostic MMAI models were validated for their intended endpoint: distant metastasis (DM) and PCa-specific mortality (PCSM). Individual clinical factors and the number of National Comprehensive Cancer Network (NCCN) high-risk features served as comparators. Subdistribution hazard ratio (sHR) was reported per standard deviation increase of the score with corresponding 95% confidence interval (CI) using Fine-Gray or Cox proportional hazards models.

RESULTS AND LIMITATIONS

The DM and PCSM MMAI algorithms were significantly and independently associated with the risk of DM (sHR [95% CI] = 2.33 [1.60-3.38], p < 0.001) and PCSM, respectively (sHR [95% CI] = 3.54 [2.38-5.28], p < 0.001) when compared against other prognostic clinical factors and NCCN high-risk features. The lower 75% of patients by DM MMAI had estimated 5- and 10-yr DM rates of 4% and 7%, and the highest quartile had average 5- and 10-yr DM rates of 19% and 32%, respectively (p < 0.001). Similar results were observed for the PCSM MMAI algorithm.

CONCLUSIONS

We externally validated the prognostic ability of MMAI models previously developed among men with localized high-risk disease. MMAI prognostic models further risk stratify beyond the clinical and pathological variables for DM and PCSM in a population of men already at a high risk for disease progression. This study provides evidence for consistent validation of our deep learning MMAI models to improve prognostication and enable more informed decision-making for patient care.

PATIENT SUMMARY

This paper presents a novel approach using images from pathology slides along with clinical variables to validate artificial intelligence (computer-generated) prognostic models. When implemented, clinicians can offer a more personalized and tailored prognostic discussion for men with localized prostate cancer.

Contemporary Diagnostic Reporting for Prostatic Adenocarcinoma: Morphologic Aspects, Molecular Correlates, and Management Perspectives

The diagnosis and reporting of prostatic adenocarcinoma have evolved from the classic framework promulgated by Dr Donald Gleason in the 1960s into a complex and nuanced system of grading and reporting that nonetheless retains the essence of his remarkable observations. The criteria for the "Gleason patterns" originally proposed have been continually refined by consensuses in the field, and Gleason scores have been stratified into a patient-friendly set of prognostically validated and widely adopted Grade Groups. One product of this successful grading approach has been the opportunity for pathologists to report diagnoses that signal carefully personalized management, placing the surgical pathologist's interpretation at the center of patient care. At one end of the continuum of disease aggressiveness, personalized diagnostic care means to sub-stratify patients with more indolent disease for active surveillance, while at the other end of the continuum, reporting histologic markers signaling aggression allows sub-stratification of clinically significant disease. Whether contemporary reporting parameters represent deeper nuances of more established ones (eg, new criteria and/or quantitation of Gleason patterns 4 and 5) or represent additional features reported alongside grade (intraductal carcinoma, cribriform patterns of carcinoma), assessment and grading have become more complex and demanding. Herein, we explore these newer reporting parameters, highlighting the state of knowledge regarding morphologic, molecular, and management aspects. Emphasis is made on the increasing value and stakes of histopathologists' interpretations and reporting into current clinical risk stratification and treatment guidelines.

Expected impact of MRI-targeted biopsy interreader variability among uropathologists on ProScreen prostate cancer screening trial: a pre-trial validation study

PURPOSE

Prostate cancer (PCa) histology, particularly the Gleason score, is an independent prognostic predictor in PCa. Little is known about the inter-reader variability in grading of targeted prostate biopsy based on magnetic resonance imaging (MRI). The aim of this study was to assess inter-reader variability in Gleason grading of MRI-targeted biopsy among uropathologists and its potential impact on a population-based randomized PCa screening trial (ProScreen).

METHODS

From June 2014 to May 2018, 100 men with clinically suspected PCa were retrospectively selected. All men underwent prostate MRI and 86 underwent targeted prostate of the prostate. Six pathologists individually reviewed the pathology slides of the prostate biopsies. The five-tier ISUP (The International Society of Urological Pathology) grade grouping (GG) system was used. Fleiss' weighted kappa (κ) and Model-based kappa for associations were computed to estimate the combined agreement between individual pathologists.

RESULTS

GG reporting of targeted prostate was highly consistent among the trial pathologists. Inter-reader agreement for cancer (GG1-5) vs. benign was excellent (Model-based kappa 0.90, Fleiss' kappa κ = 0.90) and for clinically significant prostate cancer (csPCa) (GG2-5 vs. GG0 vs. GG1), it was good (Model-based kappa 0.70, Fleiss' kappa κ 0.67).

CONCLUSIONS

Inter-reader agreement in grading of MRI-targeted biopsy was good to excellent, while it was fair to moderate for MRI in the same cohort, as previously shown. Importantly, there was wide consensus by pathologists in assigning the contemporary GG on MRI-targeted biopsy suggesting high reproducibility of pathology reporting in the ProScreen trial.

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

The highest percentage of Gleason Pattern 4 is a predictor in intermediate-risk prostate cancer

Objectives

This study aims to clarify the clinicopathological significance of several novel pathological markers, including the percentage of Gleason pattern 4 and small/non-small cribriform pattern, in intermediate-risk Gleason score 3 + 4 = 7 prostate cancer.

Subjects and Methods

Two-hundred and twenty-eight patients with Gleason score 3 + 4 = 7 intermediate-risk prostate cancer who underwent radical prostatectomy between 2009 and 2019 at our institute were selected. Preoperative clinicopathological characteristics, including serum prostate-specific antigen level, clinical T stage, percentage of cancer-positive cores at biopsy, small/non-small cribriform pattern, the highest percentage of Gleason pattern 4, the total length of Gleason pattern 4 and percentage of Gleason score 7 cores were examined in univariate/multivariate logistic regression analysis to determine their predictive value for postoperative adverse pathological findings, defined as an upgrade to Gleason score 4 + 3 = 7 or higher, pN1 or pT3b disease.

Results

Fifty-four cases (23.7%) showed adverse pathological findings. Although a non-small cribriform pattern, highest Gleason pattern 4 percentage and total length of Gleason pattern 4 were predictive of adverse pathological findings in univariate analysis, only the highest Gleason pattern 4 percentage was an independent predictive factor in multivariate analysis (odds ratio: 1.610; 95% confidence interval: 1.260-2.070; P = 0.0002).

Conclusion

The highest Gleason pattern 4 percentage was a potent predictive parameter for Gleason score 3 + 4 = 7 intermediate-risk prostate cancer and should be considered in the risk classification scheme for prostate cancer.

Region Segmentation of Whole-Slide Images for Analyzing Histological Differentiation of Prostate Adenocarcinoma Using Ensemble EfficientNetB2 U-Net with Transfer Learning Mechanism

Recent advances in computer-aided detection via deep learning (DL) now allow for prostate cancer to be detected automatically and recognized with extremely high accuracy, much like other medical diagnoses and prognoses. However, researchers are still limited by the Gleason scoring system. The histopathological analysis involved in assigning the appropriate score is a rigorous, time-consuming manual process that is constrained by the quality of the material and the pathologist's level of expertise. In this research, we implemented a DL model using transfer learning on a set of histopathological images to segment cancerous and noncancerous areas in whole-slide images (WSIs). In this approach, the proposed Ensemble U-net model was applied for the segmentation of stroma, cancerous, and benign areas. The WSI dataset of prostate cancer was collected from the Kaggle repository, which is publicly available online. A total of 1000 WSIs were used for region segmentation. From this, 8100 patch images were used for training, and 900 for testing. The proposed model demonstrated an average dice coefficient (DC), intersection over union (IoU), and Hausdorff distance of 0.891, 0.811, and 15.9, respectively, on the test set, with corresponding masks of patch images. The manipulation of the proposed segmentation model improves the ability of the pathologist to predict disease outcomes, thus enhancing treatment efficacy by isolating the cancerous regions in WSIs.

Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

BACKGROUND

Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer.

METHODS

Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). In addition, we performed an inter-rater reliability evaluation on the WSI classification.

RESULTS

We evaluated the models on a test set (n=645), achieving ROC-AUCs of 0.846 for indolent and 0.980 for aggressive. The inter-rater reliability evaluation showed s-scores in the range of 0.10 to 0.95, with the lowest being on the WSIs with both indolent and aggressive classification by the model, and the highest on benign WSIs.

CONCLUSION

The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Prostate Cancer: Update on Grading and Reporting

The Gleason scoring system and Grade Group systems facilitate accurate grading and reporting of prostate cancer, which are essential tasks for surgical pathologists. Gleason Pattern 4 is critical to recognize because it signifies a risk for more aggressive behavior than Gleason Pattern 3 carcinoma. Prostatic adenocarcinoma with radiation or androgen therapy effect, with aberrant P63 expression, or with Paneth cell-like differentiation represent pitfalls in prostate cancer grading because although they display architecture associated with aggressive behavior in usual prostatic adenocarcinoma, they do not behave aggressively and using conventional Gleason scoring in these tumors would significantly overstate their biologic potential.

The 2022 World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs-Part B: Prostate and Urinary Tract Tumors

The 2022 World Health Organization (WHO) classification of the urinary and male genital tumors was recently published by the International Agency for Research on Cancer. This fifth edition of the WHO "Blue Book" offers a comprehensive update on the terminology, epidemiology, pathogenesis, histopathology, diagnostic molecular pathology, and prognostic and predictive progress in genitourinary tumors. In this review, the editors of the fifth series volume on urologic and male genital neoplasms present a summary of the salient changes introduced to the classification of tumors of the prostate and the urinary tract.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

[Localized prostate cancer]

Prostate cancer is the most prevalent noncutaneous cancer in men. The Gleason grading is considered to be the strongest prognostic parameter regarding progression-free survival and overall survival. The original grading system has been modified during the last decade resulting in a more precise prognostic tool. The pretreatment Gleason score guides clinical management and is a key component in S3 guidelines for prostate cancer. In addition to Gleason score several other histologic findings in prostate needle biopsy influence patient management. In this second part of our CME series about prostate cancer, we will discuss the diagnosis of prostate cancer and current guidelines for reporting prostate cancer. In addition, we will highlight prostate lesions of urothelial origin and neuroendocrine prostate cancer as well as prognostic biomarkers.

The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer

CONTEXT.—

Controversies and uncertainty persist in prostate cancer grading.

OBJECTIVE.—

To update grading recommendations.

DATA SOURCES.—

Critical review of the literature along with pathology and clinician surveys.

CONCLUSIONS.—

Percent Gleason pattern 4 (%GP4) is as follows: (1) report %GP4 in needle biopsy with Grade Groups (GrGp) 2 and 3, and in needle biopsy on other parts (jars) of lower grade in cases with at least 1 part showing Gleason score (GS) 4 + 4 = 8; and (2) report %GP4: less than 5% or less than 10% and 10% increments thereafter. Tertiary grade patterns are as follows: (1) replace "tertiary grade pattern" in radical prostatectomy (RP) with "minor tertiary pattern 5 (TP5)," and only use in RP with GrGp 2 or 3 with less than 5% Gleason pattern 5; and (2) minor TP5 is noted along with the GS, with the GrGp based on the GS. Global score and magnetic resonance imaging (MRI)-targeted biopsies are as follows: (1) when multiple undesignated cores are taken from a single MRI-targeted lesion, an overall grade for that lesion is given as if all the involved cores were one long core; and (2) if providing a global score, when different scores are found in the standard and the MRI-targeted biopsy, give a single global score (factoring both the systematic standard and the MRI-targeted positive cores). Grade Groups are as follows: (1) Grade Groups (GrGp) is the terminology adopted by major world organizations; and (2) retain GS 3 + 5 = 8 in GrGp 4. Cribriform carcinoma is as follows: (1) report the presence or absence of cribriform glands in biopsy and RP with Gleason pattern 4 carcinoma. Intraductal carcinoma (IDC-P) is as follows: (1) report IDC-P in biopsy and RP; (2) use criteria based on dense cribriform glands (>50% of the gland is composed of epithelium relative to luminal spaces) and/or solid nests and/or marked pleomorphism/necrosis; (3) it is not necessary to perform basal cell immunostains on biopsy and RP to identify IDC-P if the results would not change the overall (highest) GS/GrGp part per case; (4) do not include IDC-P in determining the final GS/GrGp on biopsy and/or RP; and (5) "atypical intraductal proliferation (AIP)" is preferred for an intraductal proliferation of prostatic secretory cells which shows a greater degree of architectural complexity and/or cytological atypia than typical high-grade prostatic intraepithelial neoplasia, yet falling short of the strict diagnostic threshold for IDC-P. Molecular testing is as follows: (1) Ki67 is not ready for routine clinical use; (2) additional studies of active surveillance cohorts are needed to establish the utility of PTEN in this setting; and (3) dedicated studies of RNA-based assays in active surveillance populations are needed to substantiate the utility of these expensive tests in this setting. Artificial intelligence and novel grading schema are as follows: (1) incorporating reactive stromal grade, percent GP4, minor tertiary GP5, and cribriform/intraductal carcinoma are not ready for adoption in current practice.

A hybrid deep learning approach for gland segmentation in prostate histopathological images

BACKGROUND

In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. The automated segmentation of prostate glands can be very challenging too due to large appearance variation and serious degeneration of these histological structures.

METHOD

A new image segmentation method, called RINGS (Rapid IdentificatioN of Glandural Structures), is presented to segment prostate glands in histopathological images. We designed a novel glands segmentation strategy using a multi-channel algorithm that exploits and fuses both traditional and deep learning techniques. Specifically, the proposed approach employs a hybrid segmentation strategy based on stroma detection to accurately detect and delineate the prostate glands contours.

RESULTS

Automated results are compared with manual annotations and seven state-of-the-art techniques designed for glands segmentation. Being based on stroma segmentation, no performance degradation is observed when segmenting healthy or pathological structures. Our method is able to delineate the prostate gland of the unknown histopathological image with a dice score of 90.16 % and outperforms all the compared state-of-the-art methods.

CONCLUSIONS

To the best of our knowledge, the RINGS algorithm is the first fully automated method capable of maintaining a high sensitivity even in the presence of severe glandular degeneration. The proposed method will help to detect the prostate glands accurately and assist the pathologists to make accurate diagnosis and treatment. The developed model can be used to support prostate cancer diagnosis in polyclinics and community care centres.

Harnessing non-destructive 3D pathology

High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.

Multi-resolution open-top light-sheet microscopy to enable efficient 3D pathology workflows

Open-top light-sheet (OTLS) microscopes have been developed for user-friendly and versatile high-throughput 3D microscopy of thick specimens. As with all imaging modalities, spatial resolution trades off with imaging and analysis times. A hierarchical multi-scale imaging workflow would therefore be of value for many volumetric microscopy applications. We describe a compact multi-resolution OTLS microscope, enabled by a novel solid immersion meniscus lens (SIMlens), which allows users to rapidly transition between air-based objectives for low- and high-resolution 3D imaging. We demonstrate the utility of this system by showcasing an efficient 3D analysis workflow for a diagnostic pathology application.

Prostate cancer growth patterns beyond the Gleason score: entering a new era of comprehensive tumour grading

The Gleason grading system is one of the most important factors in clinical decision‐making for prostate cancer patients, and is entirely based on the classification of tumour growth patterns. In recent years it has become clear that some individual growth patterns themselves have independent prognostic value, and could be used for better personalised risk stratification. In this review we summarise recent literature on the clinicopathological value and molecular characteristics of individual prostate cancer growth patterns, and show how these, most particularly cribriform architecture, could alter treatment decisions for prostate cancer patients.

The 2019 International Society of Urological Pathology (ISUP) Consensus Conference on Grading of Prostatic Carcinoma

Five years after the last prostatic carcinoma grading consensus conference of the International Society of Urological Pathology (ISUP), accrual of new data and modification of clinical practice require an update of current pathologic grading guidelines. This manuscript summarizes the proceedings of the ISUP consensus meeting for grading of prostatic carcinoma held in September 2019, in Nice, France. Topics brought to consensus included the following: (1) approaches to reporting of Gleason patterns 4 and 5 quantities, and minor/tertiary patterns, (2) an agreement to report the presence of invasive cribriform carcinoma, (3) an agreement to incorporate intraductal carcinoma into grading, and (4) individual versus aggregate grading of systematic and multiparametric magnetic resonance imaging-targeted biopsies. Finally, developments in the field of artificial intelligence in the grading of prostatic carcinoma and future research perspectives were discussed.

Updates in Histologic Grading of Urologic Neoplasms

CONTEXT.—

Tumor histology offers a composite view of the genetic, epigenetic, proteomic, and microenvironmental determinants of tumor biology. As a marker of tumor histology, histologic grading has persisted as a highly relevant factor in risk stratification and management of urologic neoplasms (ie, renal cell carcinoma, prostatic adenocarcinoma, and urothelial carcinoma). Ongoing research and consensus meetings have attempted to improve the accuracy, consistency, and biologic relevance of histologic grading, as well as provide guidance for many challenging scenarios.

OBJECTIVE.—

To review the most recent updates to the grading system of urologic neoplasms, including those in the 2016 4th edition of the World Health Organization (WHO) Bluebook, with emphasis on issues encountered in routine practice.

DATA SOURCES.—

Peer-reviewed publications and the 4th edition of the WHO Bluebook on the pathology and genetics of the urinary system and male genital organs.

CONCLUSIONS.—

This article summarizes the recently updated grading schemes for renal cell carcinoma, prostate adenocarcinomas, and bladder neoplasms of the genitourinary tract.

Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading

The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68-0.84) and 0.50 (range 0.40-0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.

Clinical implications of intratumor heterogeneity: challenges and opportunities

In this review, we highlight the role of intratumoral heterogeneity, focusing on the clinical and biological ramifications this phenomenon poses. Intratumoral heterogeneity arises through complex genetic, epigenetic, and protein modifications that drive phenotypic selection in response to environmental pressures. Functionally, heterogeneity provides tumors with significant adaptability. This ranges from mutual beneficial cooperation between cells, which nurture features such as growth and metastasis, to the narrow escape and survival of clonal cell populations that have adapted to thrive under specific conditions such as hypoxia or chemotherapy. These dynamic intercellular interplays are guided by a Darwinian selection landscape between clonal tumor cell populations and the tumor microenvironment. Understanding the involved drivers and functional consequences of such tumor heterogeneity is challenging but also promises to provide novel insight needed to confront the problem of therapeutic resistance in tumors.

Gleason score 5 + 3 = 8 (grade group 4) prostate cancer-a rare occurrence with contemporary grading

Grade Group (GG) 4 prostate cancer includes Gleason scores (GS) 3 + 5 = 8, 4 + 4 = 8, and 5 + 3 = 8. Some studies without pathology re-review of historical cohorts proposed that the presence of pattern 5 worsens prognosis compared to GS 4 + 4 = 8 cancer. We assessed how often historically graded GS 5 + 3 = 8 cancers retain this grade with contemporary grading recommendations. Sixteen prostate biopsies and 24 radical prostatectomies (RP) reported from 2005 to 2019 as GS 5 + 3 = 8 were re-reviewed and graded according to contemporary recommendations. In discrepant cases, an attempt was made to explain the different grading. One (6%) biopsy and 3 (12%) RPs remained GS 5 + 3 = 8 (GG4) after re-review. Two (12%) biopsies remained GG4 but were re-graded as GS 3 + 5 = 8 and 1 (4%) RP was reclassified as GS 4 + 4 = 8 (GG4). Eight (50%) biopsies and 15 (64%) RPs were upgraded to Gleason scores 9-10 (GG5). Five (32%) biopsies and 1 (4%) RPs were downgraded to Gleason score 7 (GG2 and 3). One (4%) RP showed GS 3 + 3 = 6 (GG1) cancer. Data from 2013-current from the 3 institutions were available to assess the incidence of GS 5 + 3 = 8 following re-review of the cases. Out of 14 359 biopsies with cancer and 6727 radical prostatectomy specimens, only 1 case (0.007%) and no cases (0%) were graded as GS 5 + 3 = 8, respectively. Reasons for grading discrepancies included: 1) assigning an overall common grade to separate needle cores or tumor nodules; 2) inclusion of <5% lower grade pattern into grading; and 3) misinterpretation of variant histology and patterns. Challenging patterns were poorly-formed glands, signet ring cell-like features, atrophic carcinoma, ductal carcinoma, and mucinous fibroplasia. GS 5 + 3 = 8 (GG4) cancer is very rare with contemporary grading. The reliability of conclusions from retrospective databases regarding the clinical significance of this grade combination without slide re-review is questionable.

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Grading of prostate cancer: a work in progress

Grading of prostate cancer has evolved substantially over time, not least because of major changes in diagnostic approach and concomitant shifts from late- to early-stage detection since the adoption of PSA testing from the late 1980s. After the conception of the architecture-based nine-tier Gleason grading system more than 50 years ago, several changes were made in order to increase its prognostic impact, to reduce interobserver variation and to improve concordance between prostate needle biopsy and radical prostatectomy grading. This eventually resulted in the current five-tier grading system, with a much more detailed description of the individual architectural patterns constituting the remaining three Gleason patterns (i.e. grades 3-5). Nevertheless, there is room for improvement. For instance, distinction of common grade 4 subpatterns such as ill-formed and fused glands from the grade 3 pattern is challenging, blurring the division between low-risk patients who could be eligible for deferred therapy and those who need curative therapy. The last few years have witnessed the publication of several studies on the prognostic impact of individual architectural subpatterns showing that, in particular, the cribriform pattern exceeded the prognostic impact of other grade 4 subpatterns. This review provides an overview of the changes in prostate cancer grading over time and provides a thorough description of the various Gleason subpatterns, the current evidence of their prognostic impact and areas of contention. Potential practical ways for improvements of the current grading system are also put forward.

Essential Updates in Grading, Morphotyping, Reporting, and Staging of Prostate Carcinoma for General Surgical Pathologists

CONTEXT.—

Within this decade, several important updates in prostate cancer have been presented through expert international consensus conferences and influential publications of tumor classification and staging.

OBJECTIVE.—

To present key updates in prostate carcinoma.

DATA SOURCES.—

The study comprised a review of literature and our experience from routine and consultation practices.

CONCLUSIONS.—

Grade groups, a compression of the Gleason system into clinically meaningful groups relevant in this era of active surveillance and multidisciplinary care management for prostate cancer, have been introduced. Refinements in the Gleason patterns notably result in the contemporarily defined Gleason score 6 cancers having a virtually indolent behavior. Grading of tertiary and minor higher-grade patterns in radical prostatectomy has been clarified. A new classification for prostatic neuroendocrine tumors has been promulgated, and intraductal, microcystic, and pleomorphic giant cell carcinomas have been officially recognized. Reporting the percentage of Gleason pattern 4 in Gleason score 7 cancers has been recommended, and data on the enhanced risk for worse prognosis of cribriform pattern are emerging. In reporting biopsies for active surveillance criteria-based protocols, we outline approaches in special situations, including variances in sampling or submission. The 8th American Joint Commission on Cancer TNM staging for prostate cancer has eliminated pT2 subcategorization and stresses the importance of nonanatomic factors in stage groupings and outcome prediction. As the clinical and pathology practices for prostate cancer continue to evolve, it is of utmost importance that surgical pathologists become fully aware of the new changes and challenges that impact their evaluation of prostatic specimens.

Three-dimensional analysis reveals two major architectural subgroups of prostate cancer growth patterns

The Gleason score is one of the most important parameters for therapeutic decision-making in prostate cancer patients. Gleason growth patterns are defined by their histological features on 4- to 5-µm cross sections, and little is known about their three-dimensional architecture. Our objective was to characterize the three-dimensional architecture of prostate cancer growth patterns. Intact tissue punches (n = 46) of representative Gleason growth patterns from radical prostatectomy specimens were fluorescently stained with antibodies targeting Keratin 8/18 and Keratin 5 for the detection of luminal and basal epithelial cells, respectively. Punches were optically cleared in benzyl alcohol-benzyl benzoate and imaged using a confocal laser scanning microscope up to a depth of 500 µm. Gleason pattern 3, poorly formed pattern 4, and cords pattern 5 all formed a continuum of interconnecting tubules in which the diameter of the structures and the lumen size decreased with higher grades. In fused pattern 4, the interconnections between the tubules were markedly closer together. In these patterns, all tumor cells were in direct contact with the surrounding stroma. In contrast, cribriform Gleason pattern 4 and solid pattern 5 demonstrated a three-dimensional continuum of contiguous tumor cells, in which the vast majority of cells had no contact with the surrounding stroma. Transitions between cribriform pattern 4 and solid pattern 5 were seen. There was a decrease in the number and size of intercellular lumens from cribriform to solid growth pattern. Glomeruloid pattern 4 formed an intermediate structure consisting of a tubular network with intraluminal epithelial protrusions close to the tubule splitting points. In conclusion, three-dimensional microscopy revealed two major architectural subgroups of prostate cancer growth patterns: (1) a tubular interconnecting network including Gleason pattern 3, poorly formed and fused Gleason pattern 4, and cords Gleason pattern 5, and (2) serpentine contiguous epithelial proliferations including cribriform Gleason pattern 4 and solid Gleason pattern 5.

Updates on Grading and Staging of Prostate Cancer

Since its development between 1966 and 1977, the Gleason grading system has remained one of the most important prognostic indicators in prostatic acinar adenocarcinoma. The grading system was first majorly revised in 2005 and again in 2014. With the publication of the 8th edition of the American Joint Committee on Cancer TNM staging manual in 2018, the classification of prostate cancer and its reporting have further evolved and are now included as part of staging criteria. This article reflects the aspects that are most influential on daily practice. A brief summary of 3 ancillary commercially available genomic tests is also provided.

Clinical significance of subtypes of Gleason pattern 4 prostate cancer

Major updates in prostate cancer grading have been adopted in recent times. These include redefinitions of Gleason pattern (GP) 4 architectural variants and reporting of the grade group (GG) system, which divides prostate cancer into five groups that better stratify patients. Still, the GG system uses the GPs 3, 4 or 5 to define each GG. Patients belonging to GG 2, 3 and 4 have increasing amounts of GP 4 in the composition of their tumors. GP4 is a heterogeneous group of morphologic variants that include poorly formed glands, glomeruloid structures, cribriform glands, and fused glands. Recently published studies suggest that the morphologic subtypes of GP 4 have different clinical significance. While the diagnostic reproducibility of poorly formed glands and fused glands is low, glomeruloid structures and cribriform glands are easier to be distinguished from other morphologies. A growing body of evidence suggests that the presence of cribriform glands is associated with an aggressive clinical course compared with other architectural subtypes. The latest 2014 guidelines issued by the International Society of Urologic Pathology recommend that the percentage of GP 4 be reported on needle biopsies and radical prostatectomy (RP) specimens. The data reviewed here invites consideration for the need to report the subtype of GP 4, especially the presence or absence of cribriform glands.

Prostate cancer with cribriform morphology: diagnosis, aggressiveness, molecular pathology and possible relationships with intraductal carcinoma

INTRODUCTION

The Gleason grading system is one of the most important prognostic factors in prostate cancer (PCa). From the 2005 to the 2014 conference organized by the International Society of Urological Pathology (ISUP), the histological criteria for the Gleason patterns were improved, resulting in the shrinkage of the Gleason pattern (GP) 3 and expansion of the GP 4. Areas Covered: Cribriform, fused, ill-defined and glomeruloid glands are part of the morphologic spectrum of the current GP 4. Cribriform, derived from the Latin word cribrum (i.e. sieve), was introduced by Gleason to describe glands composed of a solid sheet with perforations or lumina. Cribriform morphology has a worse prognosis compared with the other, non-cribriform, GP4 morphologies. A practical implication is that a cribriform growth precludes a patient from selecting an active surveillance (AS) protocol. Expert commentary: The presence of these four growth patterns should be incorporated into the current Grade Group (GG) system. Enhancing our understanding of cribriform tumor behavior will lead to correctly identifying and treating those patients that will die because of PCa, while sparing treatment in those who do not require it.

Utility of Pathology Imagebase for standardisation of prostate cancer grading

AIMS

Despite efforts to standardise grading of prostate cancer, even among experts there is still a considerable variation in grading practices. In this study we describe the use of Pathology Imagebase, a novel reference image library, for setting an international standard in prostate cancer grading.

METHODS AND RESULTS

The International Society of Urological Pathology (ISUP) recently launched a reference image database supervised by experts. A panel of 24 international experts in prostate pathology reviewed independently microphotographs of 90 cases of prostate needle biopsies with cancer. A linear weighted kappa of 0.67 (95% confidence interval = 0.62-0.72) and consensus was reached in 50 cases. The interobserver weighted kappa varied from 0.48 to 0.89. The highest level of agreement was seen for Gleason score (GS) 3 + 3 = 6 (ISUP grade 1), while higher grades and particularly GS 4 + 3 = 7 (ISUP grade 3) showed considerable disagreement. Once a two-thirds majority was reached, images were moved automatically into a public database available for all ISUP members at www.isupweb.org. Non-members are able to access a limited number of cases.

CONCLUSIONS

It is anticipated that the database will assist pathologists to calibrate their grading and, hence, decrease interobserver variability. It will also help to identify instances where definitions of grades need to be clarified.

Assessment of the need for DCE MRI in the detection of dominant lesions in the whole gland: Correlation between histology and MRI of prostate cancer

The purpose of this study was to evaluate the utility of dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) in the detection of dominant prostate tumors with multi-parametric MRI of the whole gland. Combined diffusion tensor imaging (DTI) and DCE MRI from 16 patients with biopsy-proven prostate cancer and no previous treatment were acquired with a 3.0-T MRI scanner prior to radical prostatectomy, and used to identify dominant tumors. MRI results were validated by whole-mount histology. Paired t-test and Wilcoxon test, logistic generalized linear mixed effect models and receiver operating characteristic (ROC) analyses were used for the estimation of the statistical significance of the results. In the peripheral zone (PZ), the areas under the ROC curve (ROC-AUC) were 0.98 (sensitivity, 96%; specificity, 98%) for DTI, 0.96 (sensitivity, 92%; specificity, 97%) for DCE and 0.99 (sensitivity, 98%; specificity, 98%) for DTI + DCE. In the entire prostate, the ROC-AUC values were 0.96 (sensitivity, 84%; specificity, 95%) for DTI, 0.87 (sensitivity, 45%; specificity, 94%) for DCE and 0.96 (sensitivity, 88%; specificity, 98%) for DTI + DCE. The increase in ROC-AUC by the addition of DCE was not statistically significant in either PZ or the entire prostate. The results of this study have shown that DTI identified dominant tumors with high accuracy in both PZ and the entire prostate, whereas the inclusion of DCE MRI had no significant impact on the identification of either PZ or entire prostate dominant lesions. Our results suggest that the inclusion of DCE MRI may not increase the accuracy of dominant lesion detection, allowing for faster, better tolerated imaging studies.

On cribriform prostate cancer

The management of newly diagnosed prostate cancer is challenging because of its heterogeneity in histology, genetics and clinical outcome. The clinical outcome of patients with Gleason score 7 prostate cancer varies greatly. Improving risk assessment in this group is of particular interest, as Gleason score 7 prostate cancer on biopsy is an important clinical threshold for active treatment. Architecturally, four Gleason grade 4 growth patterns are recognized: ill-formed, fused, glomeruloid and cribriform. The aim of this review is to describe the role of cribriform growth in prostate cancer with respect to diagnosis, prognosis and molecular pathology. Secondly, we will discuss clinical applications for cribriform prostate cancer and give recommendations for future research.

Variability in Outcomes for Patients with Intermediate-risk Prostate Cancer (Gleason Score 7, International Society of Urological Pathology Gleason Group 2-3) and Implications for Risk Stratification: A Systematic Review

CONTEXT

Optimal management for patients with intermediate-risk (IR) prostate cancer (PCa) remains controversial. Clinical metrics provide guidance on appropriate management options.

OBJECTIVE

To report estimates for clinically relevant outcomes in men with IR PCa based on clinical and pathological features.

EVIDENCE ACQUISITION

PubMed and programs from key 2015 uro-oncology congresses were searched using the terms "intermediate", "Gleason 3 + 4", "Gleason 4 + 3", "active surveillance", "treatment", "adverse pathology", AND "prostate cancer." Articles meeting prespecified criteria were retrieved. Bibliographies were scanned for additional relevant references.

EVIDENCE SYNTHESIS

Men with IR PCa have a wide range of predicted clinically relevant outcomes. Within the IR category, estimate ranges for adverse surgical pathology and 5-yr disease progression are 15-64% and 21-91%, respectively. Clinical parameters and predictive nomograms refine these estimates, but do not uniformly differentiate favorable and unfavorable IR PCa. Variations in study design and data quality in source manuscripts mandate caution in interpreting results.

CONCLUSIONS

Outcomes in IR PCa are heterogeneous. Refinements in personalized risk assessment are needed to better select IR PCa patients for surveillance.

PATIENT SUMMARY

Current and future risk stratification tools may provide additional information to identify men with intermediate-risk prostate cancer who may consider active surveillance.

Light-sheet microscopy for slide-free non-destructive pathology of large clinical specimens

For the 1.7 million patients per year in the U.S. who receive a new cancer diagnosis, treatment decisions are largely made after a histopathology exam. Unfortunately, the gold standard of slide-based microscopic pathology suffers from high inter-observer variability and limited prognostic value due to sampling limitations and the inability to visualize tissue structures and molecular targets in their native 3D context. Here, we show that an open-top light-sheet microscope optimized for non-destructive slide-free pathology of clinical specimens enables the rapid imaging of intact tissues at high resolution over large 2D and 3D fields of view, with the same level of detail as traditional pathology. We demonstrate the utility of this technology for various applications: wide-area surface microscopy to triage surgical specimens (with ~200 μm surface irregularities), rapid intraoperative assessment of tumour-margin surfaces (12.5 sec/cm2), and volumetric assessment of optically cleared core–needle biopsies (1 mm in diameter, 2 cm in length). Light-sheet microscopy can be a versatile tool for both rapid surface microscopy and deep volumetric microscopy of human specimens.

Accuracy of Grading Gleason Score 7 Prostatic Adenocarcinoma on Needle Biopsy: Influence of Percent Pattern 4 and Other Histological Factors

BACKGROUND

Recognition of Gleason pattern 4 in prostatic needle biopsies is crucial for both prognosis and therapy. Recently, it has been recommended to record percent pattern 4 when Gleason score 7 cancer is the highest grade in a case.

METHODS

Four hundred and five prostate needle core biopsies received for a second opinion at our institution from February-June, 2015 were prospectively diagnosed with prostatic adenocarcinoma Gleason score 7 as the highest score on review by a consultant urological pathologist. Percentage of core involvement by cancer, percentage of Gleason pattern 4 per core, distribution of Gleason pattern 4 (clustered, scattered), morphology of pattern 4 (cribriform, non-cribriform), and whether the cancer was continuous or discontinuous were recorded.

RESULTS

Better agreement was noted between the consultant and referring pathologists when pattern 4 was clustered as opposed to dispersed in biopsies (P = 0.009). The percentage of core involvement by cancer, morphology of pattern 4, and continuity of cancer did not affect the agreement between the consultant and referring pathologists. There was a trend (P = 0.06) for better agreement based on the percent of pattern 4.

CONCLUSIONS

When pattern 4 is scattered amongst pattern 3 as opposed to being discrete foci, there is less interobserver reproducibility in grading Gleason score 7 cancer, and in this setting pathologists should consider obtaining second opinions either internally within their group or externally. Prostate 77: 681-685, 2017. © 2017 Wiley Periodicals, Inc.

Limitations in Predicting Organ Confined Prostate Cancer in Patients with Gleason Pattern 4 on Biopsy: Implications for Active Surveillance

PURPOSE

In prostate cancer biopsy Gleason score predicts stage and helps determine active surveillance suitability. Evidence suggests that small incremental differences in the quantitative percent of Gleason pattern 4 on biopsy stratify disease extent, biochemical failure following surgery and eligibility for active surveillance. We explored the overall quantitative percent of Gleason pattern 4 levels and adverse outcomes in patients with low and intermediate risk prostate cancer to whom active surveillance may be offered under expanded criteria.

MATERIALS AND METHODS

We analyzed the records of patients with biopsy Gleason score 6 (3 + 3) or 7 (3 + 4) who underwent radical prostatectomy from January 2008 to August 2015. Age, prostate specific antigen, Gleason score, quantitative percent of Gleason pattern 4, overall percent positive cores (percent of prostate cancer) and clinical stage were explored as predictors of nonorgan confined disease and time to failure after radical prostatectomy.

RESULTS

In 1,255 patients biopsy Gleason score 7 (3 + 4) was associated with T3 or greater disease at radical prostatectomy in 35.0% compared with Gleason score 6 (3 + 3) in 19.0% (p <0.001). On multivariate analysis for each quantitative percent of Gleason pattern 4 increase there were 2% higher odds of T3 or greater disease (OR 1.02, 95% CI 1.01-1.04, p <0.001). When stratified, patients with Gleason score 7 (3 + 4) only approximated the pT3 rates of Gleason score 6 (3 + 3) when prostate specific antigen was less than 8 ng/ml and the percent of prostate cancer was less than 15%. In those cases the quantitative percent of Gleason pattern 4 had less effect. Time to failure after radical prostatectomy was worse in Gleason score 7 (3 + 4) than 6 (3 + 3) cases.

CONCLUSIONS

The quantitative percent of Gleason pattern 4 helps predict advanced disease and Gleason score 7 (3 + 4) is associated with worse outcomes. However, the impact of the quantitative percent of Gleason pattern 4 on adverse pathological and clinical outcomes is best used in combination with prostate specific antigen, age and disease volume since each has a greater impact on predicting nonorgan confined disease. The calculated absolute risk of T3 or greater can be used in shared decision making on prostate cancer treatment by patients and clinicians.

Disease-specific survival of patients with invasive cribriform and intraductal prostate cancer at diagnostic biopsy

Invasive cribriform and intraductal carcinoma in radical prostatectomy specimens have been associated with an adverse clinical outcome. Our objective was to determine the prognostic value of invasive cribriform and intraductal carcinoma in pre-treatment biopsies on time to disease-specific death. We pathologically revised the diagnostic biopsies of 1031 patients from the first screening round of the European Randomized Study of Screening for Prostate Cancer (1993-2000). Ninety percent of all patients (n=923) had received active treatment, whereas 10% (n=108) had been followed by watchful waiting. The median follow-up was 13 years. Patients who either had invasive cribriform growth pattern or intraductal carcinoma were categorized as CR/IDC+. The outcome was disease-specific survival. Relationships with outcome were analyzed using multivariable Cox regression and log-rank analysis. In total, 486 patients had Gleason score 6 (47%) and 545 had ≥7 (53%). The 15-year disease-specific-survival probabilities were 99% in Gleason score 6 (n=486), 94% in CR/IDC- Gleason score ≥7 (n=356) and 67% in CR/IDC+ Gleason score ≥7 (n=189). CR/IDC- Gleason score 3+4=7 patients did not have statistically different survival probabilities from those with Gleason score 6 (P=0.30), while CR/IDC+ Gleason score 3+4=7 patients did (P<0.001). In multivariable analysis, CR/IDC+ status was independently associated with a poorer disease-specific survival (HR 2.6, 95% CI 1.4-4.8, P=0.002). We conclude that CR/IDC+ status in prostate cancer biopsies is associated with a worse disease-specific survival. Our findings indicate that men with biopsy CR/IDC- Gleason score 3+4=7 prostate cancer could be candidates for active surveillance, as these patients have similar survival probabilities to those with Gleason score 6.

Gleason grade 4 prostate adenocarcinoma patterns: an interobserver agreement study among genitourinary pathologists

AIMS

To assess the interobserver reproducibility of individual Gleason grade 4 growth patterns.

METHODS AND RESULTS

Twenty-three genitourinary pathologists participated in the evaluation of 60 selected high-magnification photographs. The selection included 10 cases of Gleason grade 3, 40 of Gleason grade 4 (10 per growth pattern), and 10 of Gleason grade 5. Participants were asked to select a single predominant Gleason grade per case (3, 4, or 5), and to indicate the predominant Gleason grade 4 growth pattern, if present. 'Consensus' was defined as at least 80% agreement, and 'favoured' as 60-80% agreement. Consensus on Gleason grading was reached in 47 of 60 (78%) cases, 35 of which were assigned to grade 4. In the 13 non-consensus cases, ill-formed (6/13, 46%) and fused (7/13, 54%) patterns were involved in the disagreement. Among the 20 cases where at least one pathologist assigned the ill-formed growth pattern, none (0%, 0/20) reached consensus. Consensus for fused, cribriform and glomeruloid glands was reached in 2%, 23% and 38% of cases, respectively. In nine of 35 (26%) consensus Gleason grade 4 cases, participants disagreed on the growth pattern. Six of these were characterized by large epithelial proliferations with delicate intervening fibrovascular cores, which were alternatively given the designation fused or cribriform growth pattern ('complex fused').

CONCLUSIONS

Consensus on Gleason grade 4 growth pattern was predominantly reached on cribriform and glomeruloid patterns, but rarely on ill-formed and fused glands. The complex fused glands seem to constitute a borderline pattern of unknown prognostic significance on which a consensus could not be reached.

Prostate Cancer Grading: A Decade After the 2005 Modified Gleason Grading System

Since 1966, when Donald Gleason, MD, first proposed grading prostate cancer based on its histologic architecture, there have been numerous changes in clinical and pathologic practices relating to prostate cancer. Patterns 1 and 2, comprising more than 30% of cases in the original publications by Gleason, are no longer reported on biopsy and are rarely diagnosed on radical prostatectomy. Many of these cases may even have been mimickers of prostate cancer that were described later with the use of contemporary immunohistochemistry. The original Gleason system predated many newly described variants of prostate cancer and our current concept of intraductal carcinoma. Gleason also did not describe how to report prostate cancer on biopsy with multiple cores of cancer or on radical prostatectomy with separate tumor nodules. To address these issues, the International Society of Urological Pathology first made revisions to the grading system in 2005, and subsequently in 2014. Additionally, a new grading system composed of Grade Groups 1 to 5 that was first developed in 2013 at the Johns Hopkins Hospital and subsequently validated in a large multi-institutional and multimodal study was presented at the 2014 International Society of Urological Pathology meeting and accepted both by participating pathologists as well as urologists, oncologists, and radiation therapists. In the present study, we describe updates to the grading of prostate cancer along with the new grading system.