Interobserver Reproducibility of Percent Gleason Pattern 4 in Prostatic Adenocarcinoma on Prostate Biopsies

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology

This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) and natural language processing (NLP) techniques. We examined CV-based AI/ML in digital pathology and radiomics, highlighting the prospective clinical studies demonstrating their utility. We also reviewed NLP-based AI/ML applications in clinical documentation analysis, knowledge assessment, and quality assurance. While acknowledging the challenges for clinical adoption, this review underscores the transformative potential of AI/ML in enhancing precision, efficiency, and quality of care in radiation oncology.

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.

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.

Large cribriform glands (> 0.25 mm diameter) as a predictor of adverse pathology in men with Grade Group 2 prostate cancer

AIMS

A recent outcome-based, radical prostatectomy study defined > 0.25 mm diameter to distinguish large versus small cribriform glands, with > 0.25 mm associated with worse recurrence-free survival. This study evaluates whether identification of > 0.25 mm cribriform glands in Grade Group 2 patients at biopsy is associated with adverse pathology at radical prostatectomy.

METHODS AND RESULTS

Tumours containing biopsy slides for 133 patients with Grade Group 2 prostate cancer with subsequent radical prostatectomy were re-reviewed for large cribriform glands (diameter > 0.25 mm). The primary outcome was adverse pathology (Grade Groups 3-5; stage pT3a or greater, or pN1). The secondary outcome was recurrence-free survival. Cribriform pattern was present in 52 of 133 (39%) patients; of these, 16 of 52 (31%) had large cribriform glands and 36 of 52 (69%) had only small cribriform glands. Patients with large cribriform glands had significantly more adverse pathology at radical prostatectomy compared to patients with small cribriform glands and no cribriform glands (large = 11 of 16, 69%; small = 12 of 36, 33%; no cribriform = 25 of 81, 31%; χ2 P-value 0.01). On multivariate analysis, large cribriform glands were also associated with adverse pathology, independent of age, prostate-specific antigen (PSA)/PSA density at diagnosis, year of diagnosis and biopsy cores percentage positive (global P-value 0.02). Large cribriform glands were also associated with increased CAPRA-S surgical risk score (Kruskal-Wallis P-value 0.02).

CONCLUSIONS

Large cribriform glands using a diameter > 0.25 mm definition in Grade Group 2 patients on biopsy are associated with increased risk of adverse pathology at radical prostatectomy. The presence of large cribriform histology should be considered when offering active surveillance for those with Grade Group 2 disease.

The grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) and positron emission tomography (PET) are widely used for the management of prostate cancer (PCa). However, how these modalities complement each other in PCa risk stratification is still largely unknown. We aim to provide insights into the potential of mpMRI and PET for PCa risk stratification.

METHODS

We analyzed data from 55 consecutive patients with elevated prostate-specific antigen and biopsy-proven PCa enrolled in a prospective study between December 2016 and December 2019. [68Ga]PSMA-11 PET (PSMA-PET), [11C]Acetate PET (Acetate-PET) and mpMRI were co-registered with whole-mount histopathology. Lower- and higher-grade lesions were defined by International Society of Urological Pathology (ISUP) grade groups (IGG). We used PET and mpMRI data to differentiate between grades in two cases: IGG 3 vs. IGG 2 (case 1) and IGG ≥ 3 vs. IGG ≤ 2 (case 2). The performance was evaluated by receiver operating characteristic (ROC) analysis.

RESULTS

We find that the maximum standardized uptake value (SUVmax) for PSMA-PET achieves the highest area under the ROC curve (AUC), with AUCs of 0.72 (case 1) and 0.79 (case 2). Combining the volume transfer constant, apparent diffusion coefficient and T2-weighted images (each normalized to non-malignant prostatic tissue) results in AUCs of 0.70 (case 1) and 0.70 (case 2). Adding PSMA-SUVmax increases the AUCs by 0.09 (p < 0.01) and 0.12 (p < 0.01), respectively.

CONCLUSIONS

By co-registering whole-mount histopathology and in-vivo imaging we show that mpMRI and PET can distinguish between lower- and higher-grade prostate cancer, using partially discriminative cut-off values.

Intermediate-risk Prostate Cancer-A Sheep in Wolf's Clothing?

This case-based discussion describes a 65-year-old man newly diagnosed with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa). According to the European Association of Urology classification system, the patient harbors an intermediate-risk cancer. In step-by step discussion, we elaborate guideline-based treatment modalities for intermediate-risk PCa focused on debating active surveillance versus active treatment. Thereby, we discuss the importance of patient characteristics, including age, hereditary factors, life expectancy and comorbidity status, findings of multiparametric magnetic resonance imaging, as well as prostate-specific antigen (PSA) density and PSA kinetics, in predicting the clinical course of the disease. In addition, we focus on cribriform pathology as a predictor of adverse outcomes and critically discuss its relevance in patient management. Lastly, we outline genomic stratification in ISUP 2 cancer as a future tool to predict PCa aggressiveness. PATIENT SUMMARY: Based on current guidelines, patients with intermediate-risk prostate cancer are treated actively or can alternatively undergo an active surveillance approach when favorable risk factors are present. One major issue is to discriminate between patients who benefit from an active therapy approach and those who benefit from a deferred treatment. Therefore, reliable biomarkers and early predictors of disease progression are needed urgently.

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.

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 value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study

BACKGROUND

The Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating possible negative clinical impacts. Artificial intelligence methods can be an important support for the pathologist, improving Gleason grade classifications. Consequently, our purpose is to construct and evaluate the potential of a Convolutional Neural Network (CNN) to classify Gleason patterns.

METHODS

The methodology included 6982 image patches with cancer, extracted from radical prostatectomy specimens previously analyzed by an expert uropathologist. A CNN was constructed to accurately classify the corresponding Gleason. The evaluation was carried out by computing the corresponding 3 classes confusion matrix; thus, calculating the percentage of precision, sensitivity, and specificity, as well as the overall accuracy. Additionally, k-fold three-way cross-validation was performed to enhance evaluation, allowing better interpretation and avoiding possible bias.

RESULTS

The overall accuracy reached 98% for the training and validation stage, and 94% for the test phase. Considering the test samples, the true positive ratio between pathologist and computer method was 85%, 93%, and 96% for specific Gleason patterns. Finally, precision, sensitivity, and specificity reached values up to 97%.

CONCLUSION

The CNN model presented and evaluated has shown high accuracy for specifically pattern neighbors and critical Gleason patterns. The outcomes are in line and complement others in the literature. The promising results surpassed current inter-pathologist congruence in classical reports, evidencing the potential of this novel technology in daily clinical aspects.

Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification

IMPORTANCE

The Gleason grading system has been the most reliable tool for the prognosis of prostate cancer since its development. However, its clinical application remains limited by interobserver variability in grading and quantification, which has negative consequences for risk assessment and clinical management of prostate cancer.

OBJECTIVE

To examine the impact of an artificial intelligence (AI)-assisted approach to prostate cancer grading and quantification.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted at the University of Wisconsin-Madison from August 2, 2017, to December 30, 2019. The study chronologically selected 589 men with biopsy-confirmed prostate cancer who received care in the University of Wisconsin Health System between January 1, 2005, and February 28, 2017. A total of 1000 biopsy slides (1 or 2 slides per patient) were selected and scanned to create digital whole-slide images, which were used to develop and validate a deep convolutional neural network-based AI-powered platform. The whole-slide images were divided into a training set (n = 838) and validation set (n = 162). Three experienced academic urological pathologists (W.H., K.A.I., and R.H., hereinafter referred to as pathologists 1, 2, and 3, respectively) were involved in the validation. Data were collected between December 29, 2018, and December 20, 2019, and analyzed from January 4, 2020, to March 1, 2021.

MAIN OUTCOMES AND MEASURES

Accuracy of prostate cancer detection by the AI-powered platform and comparison of prostate cancer grading and quantification performed by the 3 pathologists using manual vs AI-assisted methods.

RESULTS

Among 589 men with biopsy slides, the mean (SD) age was 63.8 (8.2) years, the mean (SD) prebiopsy prostate-specific antigen level was 10.2 (16.2) ng/mL, and the mean (SD) total cancer volume was 15.4% (20.1%). The AI system was able to distinguish prostate cancer from benign prostatic epithelium and stroma with high accuracy at the patch-pixel level, with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.88-0.95). The AI system achieved almost perfect agreement with the training pathologist (pathologist 1) in detecting prostate cancer at the patch-pixel level (weighted κ = 0.97; asymptotic 95% CI, 0.96-0.98) and in grading prostate cancer at the slide level (weighted κ = 0.98; asymptotic 95% CI, 0.96-1.00). Use of the AI-assisted method was associated with significant improvements in the concordance of prostate cancer grading and quantification between the 3 pathologists (eg, pathologists 1 and 2: 90.1% agreement using AI-assisted method vs 84.0% agreement using manual method; P < .001) and significantly higher weighted κ values for all pathologists (eg, pathologists 2 and 3: weighted κ = 0.92 [asymptotic 95% CI, 0.90-0.94] for AI-assisted method vs 0.76 [asymptotic 95% CI, 0.71-0.80] for manual method; P < .001) compared with the manual method.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, an AI-powered platform was able to detect, grade, and quantify prostate cancer with high accuracy and efficiency and was associated with significant reductions in interobserver variability. These results suggest that an AI-powered platform could potentially transform histopathological evaluation and improve risk stratification and clinical management of prostate cancer.

A Hybrid Human-Machine Learning Approach for Screening Prostate Biopsies Can Improve Clinical Efficiency Without Compromising Diagnostic Accuracy

CONTEXT.—

Prostate cancer is a common malignancy, and accurate diagnosis typically requires histologic review of multiple prostate core biopsies per patient. As pathology volumes and complexity increase, new tools to improve the efficiency of everyday practice are keenly needed. Deep learning has shown promise in pathology diagnostics, but most studies silo the efforts of pathologists from the application of deep learning algorithms. Very few hybrid pathologist-deep learning approaches have been explored, and these typically require complete review of histologic slides by both the pathologist and the deep learning system.

OBJECTIVE.—

To develop a novel and efficient hybrid human-machine learning approach to screen prostate biopsies.

DESIGN.—

We developed an algorithm to determine the 20 regions of interest with the highest probability of malignancy for each prostate biopsy; presenting these regions to a pathologist for manual screening limited the initial review by a pathologist to approximately 2% of the tissue area of each sample. We evaluated this approach by using 100 biopsies (29 malignant, 60 benign, 11 other) that were reviewed by 4 pathologists (3 urologic pathologists, 1 general pathologist) using a custom-designed graphical user interface.

RESULTS.—

Malignant biopsies were correctly identified as needing comprehensive review with high sensitivity (mean, 99.2% among all pathologists); conversely, most benign prostate biopsies (mean, 72.1%) were correctly identified as needing no further review.

CONCLUSIONS.—

This novel hybrid system has the potential to efficiently triage out most benign prostate core biopsies, conserving time for the pathologist to dedicate to detailed evaluation of malignant biopsies.

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.

Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer

Virtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998–2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen’s kappa and Bland and Altman’s limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.

Standardization of reporting discontinuous tumor involvement in prostatic needle biopsy: a systematic review

Discontinuous tumor involvement (DTI) is a not uncommon finding in the tumor in prostate needle core biopsies undertaken for diagnosis of prostate cancer (PCa). The objective of this review is to establish a clear definition of DTI in order to provide a standardized method of measurement which reliably reflects pathologic features and disease progression following radical prostatectomy (RP). A systematic literature search was performed using PubMed up to March 2020 to identify studies of PCa patients which included needle biopsies containing DTI and matched subsequent RP treatment with or without follow-up information. The methodology and quality of reporting of DTI are reviewed, compared, and summarized. DTI is a frequent finding in diagnostic biopsy for PCa (up to 30%). Six studies were compared by methods of measurement used for predicting pathologic features and outcomes which are observed in subsequent RP. In most cases with DTI (> 90%), intervening benign tissue in the tumor core was less than 5 mm. DTI found in the biopsy was likely to be associated with a single, irregular tumor nodule going in and out of the plane of the section, but DTI was not associated with multiple small foci of the tumor. Immunohistochemistry (IHC) also demonstrated that about 75% of cases of DTI shared an IHC profile which supports the concept that DTI most likely comes from a homogeneous tumor nodule. Furthermore, DTI was associated with positive surgical margin (PSM) and bilateral tumor in RP specimens. Compared to additive measurement (with the subtraction of intervening benign tissue), linear measurement (including intervening benign tissue) of DTI was more accurately predictive of aggressive disease in the RP including higher pT stage, PSM, and greater actual extent of the tumor. However, the advantage of linear measurement was lost in cases where there was an upgrade from the biopsy to the RP which may result from undersampling. For cases with either very small tumor foci or very extensive cancer volume, no difference was observed in these two methods of measurement. DTI in core biopsies may represent undersampling of a larger irregular nodule but likely does not result from multifocality and is similarly unlikely to represent multiclonality. Linear measurement of DTI was more accurately predictive of post-RP pathologic findings and oncologic prognosis. This method should be applied for patient selection for AS.

Practice patterns related to prostate cancer grading: results of a 2019 Genitourinary Pathology Society clinician survey

PURPOSE

To survey urologic clinicians regarding interpretation of and practice patterns in relation to emerging aspects of prostate cancer grading, including quantification of high-grade disease, cribriform/intraductal carcinoma, and impact of magnetic resonance imaging-targeted needle biopsy.

MATERIALS AND METHODS

The Genitourinary Pathology Society distributed a survey to urology and urologic oncology-focused societies and hospital departments. Eight hundred and thirty four responses were collected and analyzed using descriptive statistics.

RESULTS

Eighty percent of survey participants use quantity of Gleason pattern 4 on needle biopsy for clinical decisions, less frequently with higher Grade Groups. Fifty percent interpret "tertiary" grade as a minor/<5% component. Seventy percent of respondents would prefer per core grading as well as a global/overall score per set of biopsies, but 70% would consider highest Gleason score in any single core as the grade for management. Seventy five percent utilize Grade Group terminology in patient discussions. For 45%, cribriform pattern would affect management, while for 70% the presence of intraductal carcinoma would preclude active surveillance.

CONCLUSION

This survey of practice patterns in relationship to prostate cancer grading highlights similarities and differences between contemporary pathology reporting and its clinical application. As utilization of Gleason pattern 4 quantification, minor tertiary pattern, cribriform/intraductal carcinoma, and the incorporation of magnetic resonance imaging-based strategies evolve, these findings may serve as a basis for more nuanced communication and guide research efforts involving pathologists and clinicians.

Inter-observer variability of cribriform architecture and percent Gleason pattern 4 in prostate cancer: relation to clinical outcome

The Grade group is an important parameter for clinical decision-making in prostate cancer. Recently, percent Gleason pattern 4 and presence of invasive cribriform and/or intraductal carcinoma (CR/IDC) have been recognized for their independent predictive value for prostate cancer outcome. There is sparse data on the inter-observer agreement for these pathologic features in practice. Our objectives were to investigate inter-observer variability of percent Gleason pattern and CR/IDC and to relate individual tumour scores to clinical outcome. Our cohort included 80 consecutive radical prostatectomies with a median follow-up 87.1 months (interquartile range 43.3-119.2), of which the slide with largest tumour volume was scored by six pathologists for Grade group (four tiers: 1, 2, 3 and 4/5), percent Gleason pattern 4 (four tiers: 0-25%, 26-50%, 51-75% and 76-100%) and presence of CR/IDC (two tiers: absent, present). The individual assignments were related to post-operative biochemical recurrence (20/80). Inter-observer agreement was substantial (Krippendorff's α 0.626) for assessment of Grade group and moderate for CR/IDC (α 0.507) and percent Gleason pattern 4 (α 0.551). For each individual pathologist, biochemical recurrence rates incremented by Grade group and presence of CR/IDC, although such relation was less clear for percent Gleason pattern 4. In conclusion, inter-observer agreement for CR/IDC and percent Gleason pattern 4 is lower than for Grade groups, indicating awareness of these features needs further improvement. Grade group and CR/IDC, but not percent Gleason pattern 4 was related to biochemical recurrence for each pathologist, indicating overall validity of individual grade assignments despite inter-observer variability.

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.

Historical and contemporary perspectives on cribriform morphology in prostate cancer

The Gleason scoring system is widely used for the grading and prognostication of prostate cancer. A Gleason pattern 4 subtype known as cribriform morphology has now been recognized as an aggressive and often lethal pattern of prostate cancer. The vast majority of published and ongoing prostate cancer studies still do not acknowledge the prognostic differences between various Gleason pattern 4 morphologies. As a result, current treatment recommendations are likely to be imprecise and not tailored towards patients who are most likely to die from the disease. Use of active surveillance for patients with Gleason score 3 + 4 prostate cancer has been suggested. However, the success of such paradigms would require cribriform morphology to be reported at the time of prostate biopsy, as patients harbouring such a pattern are poor candidates for surveillance. To date, only a limited number of studies have described the molecular alterations that occur in the cribriform morphological pattern. Further refinement of prostate cancer grading paradigms to distinguish cribriform from noncribriform Gleason pattern 4 is essential.

Controversial issues in Gleason and International Society of Urological Pathology (ISUP) prostate cancer grading: proposed recommendations for international implementation

The Gleason Grading system has been used for over 50 years to prognosticate and guide the treatment for patients with prostate cancer. At consensus conferences in 2005 and 2014 under the guidance of the International Society of Urological Pathology (ISUP), the system has undergone major modifications to reflect modern diagnostic and therapeutic practices. The 2014 consensus conference yielded recommendations regarding cribriform, mucinous, glomeruloid and intraductal patterns, the most significant of which was the removal of any cribriform pattern from Gleason grade 3. Furthermore, a Gleason score grouping system was endorsed which consisted of five grades where Gleason score 6 (3+3) was classified as grade 1 which better reflected the mostly indolent behaviour of these tumours. Another issue discussed at the meeting and subsequently endorsed was that in Gleason score 7 cases, the percentage pattern 4 should be recorded. This is especially important in situations where modern active surveillance protocols expand to include men with low volume pattern 4. While major progress was made at the conference, several issues were either not resolved or not discussed at all. Most of these items relate to details of assignment of Gleason score and ISUP grade in specific specimen types and grading scenarios. This detailed review looks at the 2014 ISUP conference results and subsequent literature from an international perspective and proposes several recommendations. The specific issues addressed are percentage pattern 4 in Gleason score 7 tumours, percentage patterns 4 and 5 or 4/5 in Gleason score 8-10 disease, minor (≤5%) high grade patterns when either 2 or 3 patterns are present, level of reporting (core, specimen, case), dealing with grade diversity among site (highest and composite scores) and reporting scores in radical prostatectomy specimens with multifocal disease. It is recognised that for many of these issues, a strong evidence base does not exist, and further research studies are required. The proposed recommendations mostly reflect consolidated expert opinion and they are classified as established if there was prior agreement by consensus and provisional if there was no previous agreement or if the item was not discussed at prior consensus conferences. For some items there are reporting options that reflect the local requirements and diverse practice models of the international urological pathology community. The proposed recommendations provide a framework for discussion at future consensus meetings.

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.

Percent Gleason pattern 4 in stratifying the prognosis of patients with intermediate-risk prostate cancer

The Gleason score remains the most reliable prognosticator in men with prostate cancer. One of the recent important modifications in the Gleason grading system recommended from the International Society of Urological Pathology consensus conference is recording the percentage of Gleason pattern 4 in the pathology reports of prostate needle biopsy and radical prostatectomy cases with Gleason score 7 prostatic adenocarcinoma. Limited data have indeed suggested that the percent Gleason pattern 4 contributes to stratifying the prognosis of patients who undergo radical prostatectomy. An additional obvious benefit of reporting percent pattern 4 includes providing critical information for treatment decisions. This review summarizes and discusses available studies assessing the utility of the percentage of Gleason pattern 4 in the management of prostate cancer patients.

Three-dimensional reconstruction of prostate cancer architecture with serial immunohistochemical sections: hallmarks of tumour growth, tumour compartmentalisation, and implications for grading and heterogeneity

AIMS

Conventional morphology of prostate cancer considers only the two-dimensional (2D) architecture of the tumour. Our aim was to examine the feasibility of three-dimensional (3D) reconstruction of tumour morphology based on multiple consecutive histological sections and to decipher relevant features of prostate cancer architecture.

METHODS AND RESULTS

Seventy-five consecutive histological sections (5 μm) of a typical prostate adenocarcinoma (Gleason score of 3 + 4 = 7) were immunostained (pan-cytokeratin) and scanned for further 3D reconstructions with fiji/imagej software. The main findings related to the prostate cancer architecture in this case were: (i) continuity of all glands, with the tumour being an integrated system, even in Gleason pattern 4 with poorly formed glands-no short-range migration of cells by Gleason pattern 4 (poorly formed glands); (ii) no repeated interconnections between the glands, with a tumour building a tree-like branched structure with very 'plastic' branches (maximal depth of investigation 375 μm); (iii) very stark compartmentalisation of the tumour related to extensive branching, the coexistence of independent terminal units of such branches in one 2D slice explaining intratumoral heterogeneity; (iv) evidence of a craniocaudal growth direction in interglandular regions of the prostate and for a lateromedial growth direction in subcapsular posterolateral regions; and (v) a 3D architecture-based description of Gleason pattern 4 with poorly formed glands, and its continuum with Gleason pattern 3.

CONCLUSIONS

Consecutive histological sections provide high-quality material for 3D reconstructions of the tumour architecture, with excellent resolution. The reconstruction of multiple regions in this typical case of a Gleason score 3 + 4 = 7 tumour provides insights into relevant aspects of tumour growth, the continuity of Gleason patterns 3 and 4, and tumour heterogeneity.

Prostate cancer grading: a decade after the 2005 modified system

This review article will cover the evolution of grading of prostate cancer from the original Gleason system in the 1960-1970s to a more patient-centric grading system proposed in 2013 from a group at Johns Hopkins Hospital, validated in 2014 by a large multi-institutional study, and subsequently accepted by the World Health Organization (WHO), College of American Pathology (CAP), and the AJCC TNM system. Covered topics include: (1) historical background; (2) 2005 and 2014 International Society of Urological Pathology Grading Conferences; (3) Description of Gleason patterns; (4) new approaches to display Gleason grades; (5) grading variants and variations of acinar adenocarcinoma; (6) reporting rules for Gleason grading reporting secondary patterns of higher grade when present to a limited extent; (7) reporting secondary patterns of lower grade when present to a limited extent; (8) reporting percentage pattern 4; (9) general applications of the Gleason grading system; (10) needle biopsy with different cores showing different grades; (11) radical prostatectomy specimens with separate tumor nodules; and (12) a new grading system for prostate cancer.

Future Perspectives and Challenges of Prostate MR Imaging

MR imaging is an important part of prostate cancer diagnosis. Variations in quality and skill in general practice mean results are not as impressive as they were in academic centers. This observation provides an impetus to improve the method. Improved quality assurance will likely result in better outcomes. Improved characterization of clinically significant prostate cancer may assist in making MR imaging more useful. Improved methods of registering MR imaging with transrectal ultrasound imaging and robotic arms controlling the biopsy can reduce the impact of inexperienced operators and make the entire system of MR imaging-guided biopsies more robust.

Computer aided detection in prostate cancer diagnostics: A promising alternative to biopsy? A retrospective study from 104 lesions with histological ground truth

BACKGROUND

Prostate cancer (PCa) diagnosis by means of multiparametric magnetic resonance imaging (mpMRI) is a current challenge for the development of computer-aided detection (CAD) tools. An innovative CAD-software (Watson Elementary™) was proposed to achieve high sensitivity and specificity, as well as to allege a correlate to Gleason grade.

AIM/OBJECTIVE

To assess the performance of Watson Elementary™ in automated PCa diagnosis in our hospital´s database of MRI-guided prostate biopsies.

METHODS

The evaluation was retrospective for 104 lesions (47 PCa, 57 benign) from 79, 64.61±6.64 year old patients using 3T T2-weighted imaging, Apparent Diffusion Coefficient (ADC) maps and dynamic contrast enhancement series. Watson Elementary™ utilizes signal intensity, diffusion properties and kinetic profile to compute a proportional Gleason grade predictor, termed Malignancy Attention Index (MAI). The analysis focused on (i) the CAD sensitivity and specificity to classify suspect lesions and (ii) the MAI correlation with the histopathological ground truth.

RESULTS

The software revealed a sensitivity of 46.80% for PCa classification. The specificity for PCa was found to be 75.43% with a positive predictive value of 61.11%, a negative predictive value of 63.23% and a false discovery rate of 38.89%. CAD classified PCa and benign lesions with equal probability (P 0.06, χ2 test). Accordingly, receiver operating characteristic analysis suggests a poor predictive value for MAI with an area under curve of 0.65 (P 0.02), which is not superior to the performance of board certified observers. Moreover, MAI revealed no significant correlation with Gleason grade (P 0.60, Pearson´s correlation).

CONCLUSION

The tested CAD software for mpMRI analysis was a weak PCa biomarker in this dataset. Targeted prostate biopsy and histology remains the gold standard for prostate cancer diagnosis.

Histopathology of Prostate Cancer

This review focuses on histopathological aspects of carcinoma of the prostate. A tissue diagnosis of adenocarcinoma is often essential for establishing a diagnosis of prostate cancer, and the foundation for a tissue diagnosis is currently light microscopic examination of hematoxylin and eosin (H&E)-stained tissue sections. Markers detected by immunohistochemistry on tissue sections can support a diagnosis of adenocarcinoma that is primary in the prostate gland or metastatic. Histological variants of carcinoma of the prostate are important for diagnostic recognition of cancer or as clinicopathologic entities that have prognostic and/or therapeutic significance. Histological grading of adenocarcinoma of the prostate, including use of the 2014 International Society of Urological Pathology (ISUP) modified Gleason grades and the new grade groups, is one of the most powerful prognostic indicators for clinically localized prostate cancer, and is one of the most critical factors in determination of management of these patients.

Contemporary Gleason Grading of Prostatic Carcinoma: An Update With Discussion on Practical Issues to Implement the 2014 International Society of Urological Pathology (ISUP) Consensus Conference on Gleason Grading of Prostatic Carcinoma

The primary proceedings of the 2014 International Society of Urological Pathology Grading Conference were published promptly in 2015 and dealt with: (1) definition of various grading patterns of usual acinar carcinoma, (2) grading of intraductal carcinoma; and (3) support for the previously proposed new Grade Groups. The current manuscript in addition to highlighting practical issues to implement the 2014 recommendations, provides an updated perspective based on numerous studies published after the 2014 meeting. A major new recommendation that came from the 2014 Consensus Conference was to report percent pattern 4 with Gleason score 7 in both needle biopsies and radical prostatectomy (RP) specimens. This manuscript gives the options how to record percentage pattern 4 and under which situations recording this information may not be necessary. Another consensus from the 2014 meeting was to replace the term tertiary-grade pattern with minor high-grade pattern. Minor high-grade indicates that the term tertiary should not merely be just the third most common pattern but that it should be minor or limited in extent. Although a specific cutoff of 5% was not voted on in the 2014 Consensus meeting, the only quantification of minor high-grade pattern that has been used in the literature with evidence-based data correlating with outcome has been the 5% cutoff. At the 2014 Consensus Conference, there was agreement that the grading rule proposed in the 2005 Consensus Conference on needle biopsies be followed, that tertiary be not used, and that the most common and highest grade patterns be summed together as the Gleason score. Therefore, the term tertiary or minor high-grade pattern should only be used in RP specimens when there are 3 grade patterns, such as with 3+4=7 or 4+3=7 with <5% Gleason pattern 5. It was recommended at the 2014 Conference that for the foreseeable future, the new Grade Groups would be reported along with the Gleason system. The minor high-grade patterns do not change the Grade Groups, such that in current practice one would, for example, report Gleason score 3+4=7 (Grade Group 2) with minor (tertiary) pattern 5. It was discussed at the 2014 Consensus Conference how minor high-grade patterns would be handled if Grade Groups 1 to 5 eventually were to replace Gleason scores 2 to 10. In the above example, it could be reported as Grade Group 2 with minor high-grade pattern or potentially this could be abbreviated to Grade Group 2+. The recommendation from the 2014 meeting was the same as in the 2005 consensus for grading separate cores with different grades: assign individual Gleason scores to separate cores as long as the cores were submitted in separate containers or the cores were in the same container yet specified by the urologist as to their location (ie, by different color inks). It is the practice of the majority of the authors of this manuscript that if the cores are submitted in a more specific anatomic manner than just left versus right (ie, per sextant site, MRI targets, etc.), that the grade of multiple cores in the same jar from that specific site are averaged together, given they are from the same location within the prostate. In cases with multiple fragmented cores in a jar, there was agreement to give a global Gleason score for that jar. The recommendation from the 2014 meeting was the same as in the 2005 consensus for grading separate nodules of cancer in RP specimens: one should assign a separate Gleason score to each dominant nodule(s). In the unusual occurrence of a nondominant nodule (ie, smaller nodule) that is of higher stage, one should also assign a grade to that nodule. If one of the smaller nodules is the highest grade focus within the prostate, the grade of this smaller nodule should also be recorded. An emerging issue in the studies and those published subsequent to the meeting was that cribriform morphology is associated with a worse prognosis than poorly formed or fused glands and in the future may be specifically incorporated into grading practice. We believe that the results from the 2014 Consensus Conference and the updates provided in this paper are vitally important to our specialty to promote uniformity in reporting of prostate cancer grade and in the contemporary management of prostate cancer.

Endoscopic Evidence That Randall's Plaque is Associated with Surface Erosion of the Renal Papilla

OBJECTIVE

This study was conducted to assess the reliability and precision of an endoscopic grading scale to identify renal papillary abnormalities across a spectrum of equipment, locations, graders, and patients.

MATERIALS AND METHODS

Intra- and interobserver reliability of the papillary grading system was assessed using weighted kappa scoring among 4 graders reviewing a single renal papilla from 50 separate patients on 2 occasions. Grading was then applied to a cohort of patients undergoing endoscopic stone removal procedures at two centers. Patient factors were compared with papillary scores on the level of the papilla, kidney, and patient.

RESULTS

Graders achieved substantial (kappa >0.6) intra- and inter-rater reliability in scored domains of ductal plugging, surface pitting, and loss of contour. Agreement for Randall's Plaque (RP) was moderate. Papillary scoring was then performed for 76 patients (89 kidneys, 533 papillae). A significant association was discovered between pitting and RP that held both within and across institutions. A general linear model was then created to further assess this association and it was found that RP score was a highly significant independent correlate of pitting score (F = 7.1; p < 0.001). Mean pitting scores increased smoothly and progressively with increasing RP scores. Sums of the scored domains were then calculated as a reflection of gross papillary abnormality. When analyzed in this way, a history of stone recurrence and shockwave lithotripsy were strongly predictive of higher sums.

CONCLUSIONS

Renal papillary pathology can be reliably assessed between different providers using a newly described endoscopic grading scale. Application of this scale to stone-forming patients suggests that the degree of RP appreciated in the papilla is strongly associated with the presence of pitting. It also suggests that patients with a history of recurrent stones and lithotripsy have greater burdens of gross papillary disease.