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

[Prognosis of Gleason 7 (3+4) prostatic adenocarcinoma with a low rate of grade 4 - Retrospective study of 104 cases]

INTRODUCTION

The aim of this study was to compare the prognosis of Gleason Score (GS) 7 (3+4) prostatic adenocarcinoma with a low percentage of grade 4 to that of GS6 (3+3) prostatic adenocarcinoma.

METHODS

All patients with GS6 or GS7 prostatic adenocarcinoma who underwent prostatectomy between 2014 and 2018 were selected. The prostate biopsy (PB) and prostatectomy resection specimen (RS) slides were reviewed by 2 pathologists. A statistical analysis was carried out to evaluate the relationship between the clinical, paraclinical and histological characteristics of the patients on biopsies and prostatectomies with the risk of recurrence.

RESULTS

One hundred and four patients were included. A recurrence occurred in 21 patients (20.2%). In univariate analysis, an association was observed between the risk of recurrence and the GS (P=0.014 for PB / P=0.006 for RS), grade 4 percentage (P=0.020/P=0.002), especially by applying the thresholds of 5% (P=0.008/P=0.018) and 10% (P=0.015/P<0.001), the tumor stage pT (P=0.045), the quality of surgical resection R (P=0.015) and the size of the tumor focus in contact with the limits (P<0.001). In multivariate analysis, grade 4 percentage greater than 10% was associated with the risk of recurrence on biopsy and prostatectomy (respectively OR 4.83 [IC95 1.38; 16.88]; P=0.014 and OR 6.29 [IC95 1.96; 20.20]; P=0.002), as well as R1 resection (OR 3.65 [IC95 1.24; 10.76]; P=0.019 and OR 4.06 [IC95 1.27; 13.03]; P=0.018).

CONCLUSION

Our study suggests that GS7 (3+4) tumors with less than 10% of grade 4 have a similar prognosis to that of GS6 (3+3) tumors. This could allow some GS7 (3+4) patients to benefit from the active surveillance therapy, instead of undergoing more aggressive treatments such as surgery or radiotherapy.

Centralized prostatectomy with intraoperative NeuroSAFE margin assessment improves surgical margin control

AIMS

To investigate the surgical margin status in patients with prostate cancer who underwent robot-assisted radical prostatectomy (RARP) with intraoperative neurovascular structure-adjacent frozen-section analysis (NeuroSAFE) and evaluate differences compared to patients who underwent radical prostatectomy without NeuroSAFE.

PATIENTS AND METHODS

Between September 2018 and January 2021, 962 patients underwent centralized RARP with NeuroSAFE. A secondary resection was performed in case of a positive surgical margin (PSM) on intraoperative frozen section (IFS) analysis to convert a PSM into a negative surgical margin (NSM). A retrospective cohort consisted of 835 patients who had undergone radical prostatectomy in a tertiary centre without NeuroSAFE between January 2000 and December 2017. We performed multivariable logistic regression to evaluate differences in risk of PSM between cohorts after controlling for clinicopathological variables.

RESULTS

Patients operated with NeuroSAFE in the centralized clinic had 29% PSM at a definitive pathological RP examination. The median cumulative length of definitive PSM was 1.1 mm (interquartile range: 0.4-3.8). Among 275 men with PSM, 136 (49%) had a cumulative length ≤1 mm and 198 (72%) ≤3 mm. After controlling for PSA, Grade group, cribriform pattern, pT-stage, and pN-stage, patients treated in the centralized clinic with NeuroSAFE had significantly lower odds on PSM (odds ratio [OR]: 0.70, 95% confidence interval [CI]: 0.56-0.88; P = 0.002), PSM length >1 mm (OR: 0.14, 95% CI: 0.09-0.22; P < 0.001), and >3 mm (OR: 0.21, 95% CI: 0.14-0.30; P < 0.001).

CONCLUSION

This study provides a detailed overview of surgical margin status in a centralized RP NeuroSAFE cohort. Centralization with NeuroSAFE was associated with lower PSM rates and significantly shorter PSM cumulative lengths, indicating improved control of surgical margin status.

Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort

Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for precise histopathological diagnosis to determine patient prognosis and guide treatment decisions. However, existing diagnostic methods face numerous challenges and are susceptible to inconsistencies between observers. To tackle these issues, artificial intelligence (AI) algorithms have been developed to aid in the diagnosis of prostate and breast cancer. This study focuses on validating the performance of two such algorithms, Galen Prostate and Galen Breast, in a Japanese cohort, with a particular focus on the grading accuracy and the ability to differentiate between invasive and non-invasive tumours. The research entailed a retrospective examination of 100 consecutive prostate and 100 consecutive breast biopsy cases obtained from a Japanese institution. Our findings demonstrated that the AI algorithms showed accurate cancer detection, with AUCs of 0.969 and 0.997 for the Galen Prostate and Galen Breast, respectively. The Galen Prostate was able to detect a higher Gleason score in four adenocarcinoma cases and detect a previously unreported cancer. The two algorithms successfully identified relevant pathological features, such as perineural invasions and lymphovascular invasions. Although further improvements are required to accurately differentiate rare cancer subtypes, these findings highlight the potential of these algorithms to enhance the precision and efficiency of prostate and breast cancer diagnosis in Japan. Furthermore, this validation paves the way for broader adoption of these algorithms as decision support tools within the Asian population.

Cribriform versus Intraductal: How to Determine the Difference

Over the years, our understanding of cribriform and intraductal prostate cancer (PCa) has evolved significantly, leading to substantial changes in their classification and clinical management. This review discusses the histopathological disparities between intraductal and cribriform PCa from a diagnostic perspective, aiming to aid pathologists in achieving accurate diagnoses. Furthermore, it discusses the ongoing debate surrounding the different recommendations between ISUP and GUPS, which pose challenges for practicing pathologists and complicates consensus among them. Recent studies have shown promising results in integrating these pathological features into clinical decision-making tools, improving predictions of PCa recurrence, cancer spread, and mortality. Future research efforts should focus on further unraveling the biological backgrounds of these entities and their implications for clinical management to ultimately improve PCa patient outcomes.

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.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

Reliability of whole mount radical prostatectomy histopathology as the ground truth for artificial intelligence assisted prostate imaging

The development of artificial intelligence-based imaging techniques for prostate cancer (PCa) detection and diagnosis requires a reliable ground truth, which is generally based on histopathology from radical prostatectomy specimens. This study proposes a comprehensive protocol for the annotation of prostatectomy pathology slides. To evaluate the reliability of the protocol, interobserver variability was assessed between five pathologists, who annotated ten radical prostatectomy specimens consisting of 74 whole mount pathology slides. Interobserver variability was assessed for both the localization and grading of PCa. The results indicate excellent overall agreement on the localization of PCa (Gleason pattern ≥ 3) and clinically significant PCa (Gleason pattern ≥ 4), with Dice similarity coefficients (DSC) of 0.91 and 0.88, respectively. On a per-slide level, agreement for primary and secondary Gleason pattern was almost perfect and substantial, with Fleiss Kappa of .819 (95% CI .659-.980) and .726 (95% CI .573-.878), respectively. Agreement on International Society of Urological Pathology Grade Group was evaluated for the index lesions and showed agreement in 70% of cases, with a mean DSC of 0.92 for all index lesions. These findings show that a standardized protocol for prostatectomy pathology annotation provides reliable data on PCa localization and grading, with relatively high levels of interobserver agreement. More complicated tissue characterization, such as the presence of cribriform growth and intraductal carcinoma, remains a source of interobserver variability and should be treated with care when used in ground truth datasets.

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.

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.

Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology

One in eight men will be affected by prostate cancer (PCa) in their lives. While the current clinical standard prognostic marker for PCa is the Gleason score, it is subject to inter-reviewer variability. This study compares two machine learning methods for discriminating between cancerous regions on digitized histology from 47 PCa patients. Whole-slide images were annotated by a GU fellowship-trained pathologist for each Gleason pattern. High-resolution tiles were extracted from annotated and unlabeled tissue. Patients were separated into a training set of 31 patients (Cohort A, n = 9345 tiles) and a testing cohort of 16 patients (Cohort B, n = 4375 tiles). Tiles from Cohort A were used to train a ResNet model, and glands from these tiles were segmented to calculate pathomic features to train a bagged ensemble model to discriminate tumors as (1) cancer and noncancer, (2) high- and low-grade cancer from noncancer, and (3) all Gleason patterns. The outputs of these models were compared to ground-truth pathologist annotations. The ensemble and ResNet models had overall accuracies of 89% and 88%, respectively, at predicting cancer from noncancer. The ResNet model was additionally able to differentiate Gleason patterns on data from Cohort B while the ensemble model was not. Our results suggest that quantitative pathomic features calculated from PCa histology can distinguish regions of cancer; however, texture features captured by deep learning frameworks better differentiate unique Gleason patterns.

Cribriform Lesions of the Prostate Gland

"Cribriform lesions of the prostate represent an important and often diagnostically challenging spectrum of prostate pathology. These lesions range from normal anatomical variation, benign proliferative lesions, premalignant, suspicious to frankly malignant and biologically aggressive entities. The concept of cribriform prostate adenocarcinoma (CrP4) and intraductal carcinoma of the prostate (IDC-P), in particular, has evolved significantly in recent years with a growing body of evidence suggesting that the presence of these morphologies is important for clinical decision-making in prostate cancer management. Therefore, accurate recognition and reporting of CrP4 and IDC-P architecture are especially important. This review discusses a contemporary diagnostic approach to cribriform lesions of the prostate with a focus on their key morphologic features, differential diagnosis, underlying molecular alterations, clinical significance, and reporting recommendations."

NeuroSAFE in radical prostatectomy increases the rate of nerve-sparing surgery without affecting oncological outcome

OBJECTIVES

To investigate the impact of intra-operative neurovascular structure-adjacent frozen-section examination (NeuroSAFE) on the rate of nerve-sparing surgery (NSS) and oncological outcome in a large radical prostatectomy (RP) cohort.

PATIENTS AND METHODS

Between January 2016 and December 2020, 1756 prostate cancer patients underwent robot-assisted RP, of whom 959 (55%) underwent this with NeuroSAFE and 797 (45%) without (control cohort). In cases where NeuroSAFE showed tumour in the margin, a secondary resection was performed. The effect of NeuroSAFE on NSS and positive surgical margin (PSM) status was analysed using logistic regression. Cox regression was used to identify predictors of biochemical recurrence-free survival (BCRFS).

RESULTS AND LIMITATIONS

Patients in the NeuroSAFE cohort had a higher tumour grade (P < 0.001) and clinical stage (P < 0.001) than those in the control cohort. NeuroSAFE enabled more frequent NSS for both pT2 (93% vs 76%; P < 0.001) and pT3 disease (83% vs 55%; P < 0.001). In adjusted analysis, NeuroSAFE resulted in more frequent unilateral (odds ratio [OR] 3.90, 95% confidence interval (CI) 2.90-5.30; P < 0.001) and bilateral (OR 5.22, 95% CI 3.90-6.98; P < 0.001) NSS. While the PSM rate decreased from 51% to 42% in patients with pT3 stage disease (P = 0.031), NeuroSAFE was not an independent predictor of PSM status (OR 0.85, 95% CI 0.68-1.06; P = 0.2) in the entire cohort. Patients who underwent NeuroSAFE had better BCRFS compared to the control cohort (hazard ratio 0.62, 95% CI 0.45-0.84; P = 0.002). This study is limited by its comparison with a historical cohort and lack of functional outcomes.

CONCLUSIONS

NeuroSAFE enables more unilateral and bilateral NSS without negatively affecting surgical margin status and biochemical recurrence. This validation study provides a comprehensive overview of the implementation, evaluation and intra-operative decision making associated with NeuroSAFE in clinical practice.

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.

WHO Classification of Tumours fifth edition: evolving issues in the classification, diagnosis, and prognostication of prostate cancer

The fifth edition of the WHO Classification of Tumours of the Urinary and Male Genital Systems encompasses several updates to the classification and diagnosis of prostatic carcinoma as well as incorporating advancements in the assessment of its prognosis, including recent grading modifications. Some of the salient aspects include: (1) recognition that prostatic intraepithelial neoplasia (PIN)-like carcinoma is not synonymous with a pattern of ductal carcinoma, but better classified as a subtype of acinar adenocarcinoma; (2) a specific section on treatment-related neuroendocrine prostatic carcinoma in view of the tight correlation between androgen deprivation therapy and the development of prostatic carcinoma with neuroendocrine morphology, and the emerging data on lineage plasticity; (3) a terminology change of basal cell carcinoma to "adenoid cystic (basal cell) cell carcinoma" given the presence of an underlying MYB::NFIB gene fusion in many cases; (4) discussion of the current issues in the grading of acinar adenocarcinoma and the prognostic significance of cribriform growth patterns; and (5) more detailed coverage of intraductal carcinoma of prostate (IDC-P) reflecting our increased knowledge of this entity, while recommending the descriptive term atypical intraductal proliferation (AIP) for lesions falling short of IDC-P but containing more atypia than typically seen in high-grade prostatic intraepithelial neoplasia (HGPIN). Lesions previously regarded as cribriform patterns of HGPIN are now included in the AIP category. This review discusses these developments, summarising the existing literature, as well as the emerging morphological and molecular data that underpins the classification and prognostication of prostatic carcinoma.

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.

Predicting biochemical recurrence of prostate cancer with artificial intelligence

Background

The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored.

Methods

To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns.

Results

The biomarker provides a strong correlation with biochemical recurrence in two sets (n = 182 and n = 204) from separate institutions. Concept-based explanations provided tissue patterns interpretable by pathologists.

Conclusions

These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading.

Prostate Pathology: What is New in the 2022 WHO Classification of Urinary and Male Genital Tumors?

In 2022, after a six-year interval, the International Agency for Research on Cancer (IARC) has published the 5th edition of the WHO Classification of Urinary and Male Genital Tumors, which provides a comprehensive update on tumor classification of the genitourinary system. This review article focuses on prostate carcinoma and underscores changes in the prostate chapter as well as those made across the entire series of the 5th edition of WHO Blue Books. Although no major alterations were made to this chapter, some of the most notable updates include restructure of contents and introduction of a new format; standardization of mitotic counts, genomic nomenclatures, and units of length; refined definition for the terms "variant", "subtype", and "histologic pattern"; reclassification of prostatic intraepithelial neoplasia (PIN)-like adenocarcinoma as a subtype of prostatic acinar adenocarcinoma; and recognition of treatment-related neuroendocrine prostatic carcinoma as a distinct tumor type. Evolving and unsettled issues related to grading of intraductal carcinoma of the prostate and reporting of tertiary Gleason pattern, the definition and prognostic significance of cribriform growth pattern, and molecular pathology of prostate cancer will also be covered in this review.

Percentage Gleason pattern 4 and PI-RADS score predict upgrading in biopsy Grade Group 2 prostate cancer patients without cribriform pattern

PURPOSE

To identify parameters to predict upgrading in biopsy Grade Group (GG) 2 prostate cancer patients without cribriform and intraductal carcinoma (CR/IDC) on biopsy.

METHODS

Preoperative biopsies from 657 men undergoing radical prostatectomy (RP) for prostate cancer were reviewed for GG, presence of CR/IDC, percentage Gleason pattern 4, and tumor length. In men with biopsy GG2 without CR/IDC (n = 196), clinicopathologic features were compared between those with GG1 or GG2 without CR/IDC on RP (GG ≤ 2-) and those with GG2 with CR/IDC or any GG > 2 (GG ≥ 2+). Logistic regression analysis was used to predict upgrading in the biopsy cohort.

RESULTS

In total 283 men had biopsy GG2 of whom 87 (30.7%) had CR/IDC and 196 (69.3%) did not. CR/IDC status in matched biopsy and RP specimens was concordant in 179 (63.3%) and discordant in 79 (27.9%) cases (sensitivity 45.1%; specificity 92.6%). Of 196 biopsy GG2 men without CR/IDC, 106 (54.1%) had GG ≥ 2+ on RP. Multivariable logistic regression analysis showed that age [odds ratio (OR): 1.85, 95% confidence interval (CI)1.09-3.20; p = 0.025], percentage Gleason pattern 4 (OR 1.54, 95% CI 1.17-2.07; p = 0.003), PI-RADS 5 lesion (OR 2.17, 95% CI 1.03-4.70; p = 0.045) and clinical stage T3 (OR 3.60; 95% CI 1.08-14.50; p = 0.049) were independent parameters to predict upgrading to GG ≥  2+ on RP in these men.

CONCLUSIONS

Age, clinical stage T3, percentage Gleason pattern 4 and presence of PI-RADS 5 lesions are independent predictors for upgrading in men with biopsy GG2 without CR/IDC. These findings allow for improved clinical decision-making on surveillance eligibility in intermediate-risk prostate cancer patients.

Cribriform architecture outperforms percent Gleason pattern 4 and tertiary pattern 5 in predicting outcome of Grade group 2 prostate cancer patients

Aims

Gleason pattern 4 (GP4) percentage, invasive cribriform and/or intraductal carcinoma (IC/IDC) and the presence of tertiary Gleason pattern 5 (TP5) in radical prostatectomy (RP) specimens all aid in the risk stratification of Grade Group (GG) 2 prostate cancer patients. However, it is unclear to what extent these pathological features are mutually related and what are their individual values if they are investigated simultaneously. The aims of this study were: (i) to determine the mutual relationships of the GP4 percentage, IC/IDC and TP5 in GG2 RP specimens; and (ii) to assess their prognostic value for biochemical recurrence‐free survival (BCRFS).

Methods and results

Of 1064 RP specimens, 472 (44.4%) showed GG2 prostate cancer. Patients with ≥25% GP4 more frequently had IC/IDC (67.0% versus 43.9%; P < 0.001) and TP5 (20.6% versus 5.8%; P < 0.001) than those with <25% GP4. In unadjusted analysis, an increased GP4 percentage [hazard ratio (HR) 1.3; 95% confidence interval (CI) 1.0–1.6; P = 0.04] and IC/IDC (log rank P < 0.001) were associated with shorter BCRFS, whereas TP5 (P = 0.12) and a dichotomised (<25%, ≥25%) GP4 percentage (P = 0.10) were not. In multivariable analysis, IC/IDC was an independent prognostic factor (HR 1.9; 95% CI 1.2–2.9; P = 0.005) for BCRFS, whereas a continuous or dichotomised GP4 percentage and TP5 were not independent prognostic factors.

Conclusion

In conclusion, a higher GP4 percentage in RP specimens was associated with more frequent IC/IDC and TP5. IC/IDC was an independent predictor for BCRFS, whereas the GP4 percentage and TP5 were not. These findings underscore the importance of routinely including the presence of IC/IDC in RP pathology reports.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.