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Introduction. Gleason grading is one of the key parameters for determining the prostate cancer (risk group after receiving the results of a prostate biopsy in addition to the level of prostate-specific antigen (PSA). However, an important clinical issue arises from changes in this grading following radical surgery. In some cases, such discrepancies in data can lead to a shift in patient management tactics from radical surgery to active follow-up recommended for patients with low cancer risk.Objective. To assess the consistency of the results of prostate biopsy and radical prostatectomy with an assessment of possible predictors of increased tumor Gleason grading.Materials & methods. Between 2019 and 2021, 193 patients with prostate cancer were selected for treatment at the Medical Centre of Moscow State University. Of these, 139 patients were chosen for the final analysis of Gleason tumour grading consistency after biopsy and radical prostatectomy.Results. The results of prostate biopsy and radical prostatectomy were comparable in 54.7% of patients (n = 76), while the remaining 45.3% (n = 63) showed a change in Gleason gradaing after radical prostatectomy: 29.5% (n = 41) patients showed an increased gradation, and 15.8% (n = 22) — decrease. Most often, the data were consistent for patients with a Gleason score 7: 63.6% for 3 + 4 = 7 and 57.9% for 4 + 3 = 7. The highest percentage of graduation increase was observed for patients from the group 3 + 3 = 6 and amounted to 48%. Cohen's kappa value was 0.351 (p < 0.001), indicating poor consistency between the results of the two studies. The total number of biopsies was associated with an increase in Gleason score after radical prostatectomy (OR = 0.816; 95% CI = 0.680 – 0.978). For example, the odds of Gleason tumour grading decreases by 18.4% when the number of biopsy specimens per unit increasesConclusion. The concordance of Gleason grading of prostate cancer after prostate biopsy and radical prostatectomy remains poor. The only predictor of improved concordance is the biopsy count, thereby reflecting biopsy quality and encouraging clinicians to move away from sextant biopsies to systematic and targeted biopsies. Increasing the consistency of results can provide more accurate staging and classification of patients according to cancer risk, thereby influencing future management.

International Society of Urological Pathology grade group 1 (GG 1) prostate cancer (PCa) is generally considered insignificant, with recent suggestions that it should even be considered as “noncancerous”. We evaluated outcomes for patients with GG 1 PCa on biopsy (bGG 1) and high-risk features (prostate-specific antigen [PSA] >20 ng/ml and/or cT3–4 stage) to challenge the hypothesis that every case of bGG 1 PCa has a benign disease course. We used the multi-institutional EMPaCT database, which includes data for 9508 patients with high-risk PCa undergoing surgery. We included patients with bGG 1 PCa (n = 848) in our analysis and divided them into three groups according to PSA >20 ng/ml, cT3–4 stage, or both. The estimated 10-yr cancer-specific survival (CSS) rate was 96% in the overall population, 88% in the group with both PSA >20 ng/ml and cT3–4 stage, 97% in the group with PSA >20 ng/ml alone, and 98% in the group with cT3–4 stage alone. Similar CSS outcomes were found in subgroups with GG 1 PCa on pathology (n = 502) and with GG 1 on biopsy diagnosed after 2005 (n = 253). Study limitations include the lack of magnetic resonance imaging (MRI) staging and MRI-targeted biopsies. In conclusion, patients with GG 1 and either PSA >20 ng/ml or cT3–4 stage have a low risk of dying from their cancer after surgery. However, patients with GG 1 PCa and both PSA >20 ng/ml and cT3–4 stage are at higher risk of cancer-specific mortality and active treatment should be discussed for this subgroup. Patient summary: We assessed outcomes for patients diagnosed with low-grade prostate cancer on biopsy who also had one or two factors associated with high risk disease. Men with both of those risk factors had a higher risk of dying from their prostate cancer. Active treatment should be discussed for this subgroup of patients.

Objectives Materials and Methods Results Conclusion To externally validate the performance of the DeepDx Prostate artificial intelligence (AI) algorithm (Deep Bio Inc., Seoul, South Korea) for Gleason grading on whole‐mount prostate histopathology, considering potential variations observed when applying AI models trained on biopsy samples to radical prostatectomy (RP) specimens due to inherent differences in tissue representation and sample size.The commercially available DeepDx Prostate AI algorithm is an automated Gleason grading system that was previously trained using 1133 prostate core biopsy images and validated on 700 biopsy images from two institutions. We assessed the AI algorithm's performance, which outputs Gleason patterns (3, 4, or 5), on 500 1‐mm2 tiles created from 150 whole‐mount RP specimens from a third institution. These patterns were then grouped into grade groups (GGs) for comparison with expert pathologist assessments. The reference standard was the International Society of Urological Pathology GG as established by two experienced uropathologists with a third expert to adjudicate discordant cases. We defined the main metric as the agreement with the reference standard, using Cohen's kappa.The agreement between the two experienced pathologists in determining GGs at the tile level had a quadratically weighted Cohen's kappa of 0.94. The agreement between the AI algorithm and the reference standard in differentiating cancerous vs non‐cancerous tissue had an unweighted Cohen's kappa of 0.91. Additionally, the AI algorithm's agreement with the reference standard in classifying tiles into GGs had a quadratically weighted Cohen's kappa of 0.89. In distinguishing cancerous vs non‐cancerous tissue, the AI algorithm achieved a sensitivity of 0.997 and specificity of 0.88; in classifying GG ≥2 vs GG 1 and non‐cancerous tissue, it demonstrated a sensitivity of 0.98 and specificity of 0.85.The DeepDx Prostate AI algorithm had excellent agreement with expert uropathologists and performance in cancer identification and grading on RP specimens, despite being trained on biopsy specimens from an entirely different patient population. [ABSTRACT FROM AUTHOR]

To investigate the predictors of Gleason Grading Group (GGG) upgrading in low-risk prostate cancer (Gleason score = 3 + 3) from transperineal biopsy after radical prostatectomy (RP). The clinical data of 160 patients who underwent transperineal biopsy and RP from January 2017 to December 2022 were retrospectively analyzed. First, univariate and multivariate logistic regression analysis were used to obtain independent predictors of postoperative GGG upgrading. Then receiver operating characteristic curve was used to evaluate the diagnostic efficacy of predictors. Finally, Linear-by-Linear Association test was used to analyze the risk trends of patients in different predictor groups in the postoperative GGG. In this study, there were 81 cases (50.6%) in the GGG concordance group and 79 cases (49.4%) in the GGG upgrading group. Univariate analysis showed age, free/total prostate-specific antigen (f/tPSA), proportion of positive biopsies, positive target of magnetic-resonance imaging (MRI) and positive target of contrast-enhanced ultrasound had significant effects on GGG upgrading (all P <.05). In multivariate logistic regression analysis, age (odds ratio [OR] = 1.066, 95%CI = 1.007-1.127, P =.027), f/tPSA (OR = 0.001, 95%CI = 0-0.146, P =.001) and positive target of MRI (OR = 3.005, 95%CI = 1.353-76.674, P =.007) were independent predictors. The prediction model (area under curve = 0.751 P <.001) had higher predictive efficacy than all independent predictors. The proportion of patients in exposed group of different GGG increased with the level of GGG, but decreased in nonexposed group, and the linear trend was significantly different (all P <.001). Age, f/tPSA, and positive target of MRI were independent predictors of postoperative GGG upgrading. The predictive model constructed had the best diagnostic efficacy. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer ranks as the most frequently diagnosed cancer in men in the USA, with significant mortality rates. Early detection is pivotal for optimal patient outcomes, providing increased treatment options and potentially less invasive interventions. There remain significant challenges in prostate cancer histopathology, including the potential for missed diagnoses due to pathologist variability and subjective interpretations. Methods: To address these challenges, this study investigates the ability of artificial intelligence (AI) to enhance diagnostic accuracy. The Galen™ Prostate AI algorithm was validated on a cohort of Puerto Rican men to demonstrate its efficacy in cancer detection and Gleason grading. Subsequently, the AI algorithm was integrated into routine clinical practice during a 3-year period at a CLIA certified precision pathology laboratory. Results: The Galen™ Prostate AI algorithm showed a 96.7% (95% CI 95.6–97.8) specificity and a 96.6% (95% CI 93.3–98.8) sensitivity for prostate cancer detection and 82.1% specificity (95% CI 73.9–88.5) and 81.1% sensitivity (95% CI 73.7–87.2) for distinction of Gleason Grade Group 1 from Grade Group 2+. The subsequent AI integration into routine clinical use examined prostate cancer diagnoses on >122,000 slides and 9200 cases over 3 years and had an overall AI Impact ™ factor of 1.8%. Conclusions: The potential of AI to be a powerful, reliable, and effective diagnostic tool for pathologists is highlighted, while the AI Impact™ in a real-world setting demonstrates the ability of AI to standardize prostate cancer diagnosis at a high level of performance across pathologists.

Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in prostate carcinoma focusing on comparing traditional and recent architectures. A standardized image classification pipeline, based on the AUCMEDI framework, facilitated robust evaluation using an in-house dataset consisting of 34,264 annotated tissue tiles. The results indicated varying sensitivity across architectures, with ConvNeXt demonstrating the strongest performance. Notably, newer architectures achieved superior performance, even though with challenges in differentiating closely related Gleason grades. The ConvNeXt model was capable of learning a balance between complexity and generalizability. Overall, this study lays the groundwork for enhanced Gleason grading systems, potentially improving diagnostic efficiency for prostate cancer. [ABSTRACT FROM AUTHOR]

Simple Summary: Though intraductal carcinoma of the prostate has been observed and recognized for years, more recently this process has emerged, to become simultaneously both a consensus, major prognostic factor and controversial parameter as relates to potential impact on the histologic grading in prostate cancer. Due to the increasing incorporation of intraductal carcinoma into risk stratification approaches and management considerations in this disease, the differential diagnoses of this lesion have become of increasing importance. Herein, we review the background of recognition of and evolution of the criteria for intraductal carcinoma, emphasizing its differential diagnoses, both neoplastic and non-neoplastic. We furthermore recommend useful immunohistochemical markers to help correctly recognize and diagnose this key process. Despite its first recognition even longer ago, in the past nearly 20 years, intraductal carcinoma of the prostate has become a standard histopathologic reporting parameter conveying a strong negative prognostic factor for prostatic adenocarcinoma. When seen at biopsy, intraductal carcinoma of the prostate is associated with risk for aggressive prostatectomy outcomes, including frequently high-grade, high-stage, high-volume disease, with increased risk for recurrence and progression. Multiple organizations, including the uropathology subspecialty societies to the World Health Organization, recognize and recommend reporting the presence of intraductal carcinoma, whether sampled in "pure" form or present with concomitant invasive adenocarcinoma. Moreover, emerging scholarship relates intraductal carcinoma to higher prevalence of homologous recombination repair deficiency mutations in prostatic adenocarcinoma, whether somatic or germline, which serve as indications for approved targeted therapies. Taken together, this is a diagnosis for the histopathologist not to miss. In view of these elevated stakes and the opportunity to further precision medicine, this review details neoplastic and non-neoplastic simulants in the differential diagnosis of intraductal carcinoma of the prostate. [ABSTRACT FROM AUTHOR]

• Prostate cancer diagnosis is subject to inter-pathologist variability. • Artificial intelligence can assuage pain points by streamlining diagnosis. • Artificial intelligence is evolving to diagnose, grade, and predict outcomes. • Challenges remain before wide-scale autonomous prostate cancer diagnosis. The diagnosis of prostate cancer (PCa) depends on the evaluation of core needle biopsies by trained pathologists. Artificial intelligence (AI) derived models have been created to address the challenges posed by pathologists' increasing workload, workforce shortages, and variability in histopathology assessment. These models with histopathological parameters integrated into sophisticated neural networks demonstrate remarkable ability to identify, grade, and predict outcomes for PCa. Though the fully autonomous diagnosis of PCa remains elusive, recently published data suggests that AI has begun to serve as an initial screening tool, an assistant in the form of a real-time interactive interface during histological analysis, and as a second read system to detect false negative diagnoses. Our article aims to describe recent advances and future opportunities for AI in PCa histopathology. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the most widespread and mortal cancers among men worldwide. Pathological evaluation is essential for the definitive diagnosis of prostate cancer, the symptoms of which are not evident during the early disease. During the pathological evaluation to determine the severity of cancer, Gleason grading, which is difficult to assign, is time-consuming, and the result may vary depending on the pathologist’s experience. In order to make a fast and objective evaluation, deep learning systems have been recently used to automate prostate cancer diagnosis and grading. The inherent difficulties of detecting Gleason grades can be overcome with deep learning architectures, which present fast and precise classification rates. The performance of the methods proposed for Gleason grading in the literature is low, especially in multi-class problems, due to their model structures. Some mechanisms for better learning of critical representations are needed to determine the Gleason grading accurately. Therefore, in this study, a novel deep learning model called Eff4-Attn is proposed for detecting and grading prostate cancer. In this model, combining the Efficient Channel Attention (ECA) mechanism with EfficientNet-B4 distinguishes the tissues in the prostate cancer pathology images much better. In addition, the effect of data distribution in classes in the public DiagSet dataset on performance was investigated. Experimental studies have been carried out to perform three classification tasks for various magnification ratios: 8-class classification, 4-class classification, and 2-class classification for diagnosing prostate cancer and determining cancer severity, and promising results have been obtained. At 40x magnification ratio, the best result was achieved with 96.18% test accuracy in the 2-class classification task, that is, in detecting the presence of cancer. In determining the severity of cancer, at a 40x magnification ratio, a test accuracy of 94.86% was obtained. The proposed deep and light model has considerable potential to assist pathologists in detecting the characteristics of Gleason grades.

PC (Prostate Cancer) is the second highest cause of death due to cancer in men globally. Proper detection and treatment are critical for halting or controlling the growth and spread of cancer cells within the human organism. However, evaluating these sorts of images is difficult and time-consuming, requiring histopathological image recognition as the most reliable method for treating PC because of its distinct visual characteristics. Risk evaluation and treatment planning rely heavily on histological image-based Gleason grading of prostate tumors. This work introduces an innovative approach to histological image analysis for prostate cancer diagnosis and Gleason grading. The Elephant Herding Optimization-based Hyper-parameter Convolutional Deep Belief Network (CDBN-EHO) is presented alongside a grading network head-optimized deep belief network technique for multi-task prediction. Leveraging an effective Bayesian inference method, fully linked Conditional Random Field (CRF) techniques are utilized for segmentation, with pairwise boundary capacities determined by a linear mixture of Gaussian kernels. The multi-task approach aims to enhance performance by incorporating contextual information, leading to breakthrough results in the identification of epithelial cells and the grading of Gleason scores. The objective of this study is to demonstrate the effectiveness of the optimized deep belief network technique in improving diagnostic accuracy and efficiency for prostate cancer diagnosis and Gleason grading in histological images.

Background: Prostate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality. Methods: Based on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive. Results: The statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation. Conclusions: The consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface). [ABSTRACT FROM AUTHOR]

Background: The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer's aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA). Methods: Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label. Results: Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98. Conclusion: Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results. [ABSTRACT FROM AUTHOR]

We show that the gene regulatory potential of the androgen receptor, the driver of prostate cancer disease, in low-risk grade group 1 lesions is very similar to that in higher-grade tumors and distinctly different from that in normal prostate epithelium, positioning grade group 1 lesions as bona fide cancers from an epigenetic perspective. Grade group 1 (GG1) primary prostate cancers with a pathologic Gleason score of 6 are considered indolent and generally not associated with fatal outcomes, so treatment is not indicated for most cases. These low-grade cancers have an overall negligible risk of locoregional progression and metastasis to distant organs, which is why there is an ongoing debate about whether these lesions should be reclassified as "noncancerous". However, the underlying molecular activity of key disease drivers, such as the androgen receptor (AR), have thus far not been thoroughly characterized in low-grade tumors. Therefore, we set out to delineate the AR chromatin-binding landscape in low-grade GG1 prostate cancers to gain insights into whether these AR-driven programs are actually tumor-specific or are normal prostate epithelium-like. These analyses showed that GG1 tumors do not harbor a distinct AR cistrome and, similar to higher-grade cancers, AR preferentially binds to tumor-defining cis-regulatory elements. Furthermore, the enhancer activity of these regions and the expression of their respective target genes were not significantly different in GG1 tumors. From an epigenetic perspective, this finding supports the cancer designation currently given to these low-grade tumors and clearly distinguishes them from noncancerous benign tissue. We characterized the molecular activity of the androgen receptor protein, which drives prostate cancer disease, in low-grade tumors. Our results show that these tumors are true cancers and are clearly separate from benign prostate tissue despite their low clinical aggressiveness. [ABSTRACT FROM AUTHOR]

BackgroundThe presence of invasive cribriform adenocarcinoma (ICC), an expanse of cells containing punched-out lumina uninterrupted by stroma, in radical prostatectomy (RP) specimens has been associated with biochemical recurrence (BCR). However, ICC identification has only moderate inter-reviewer agreement.ObjectiveTo investigate quantitative machine-based assessment of the extent and prognostic utility of ICC, especially within individual Gleason grade groups.Design, setting, and participantsA machine learning approach was developed for ICC segmentation using 70 RP patients and validated in a cohort of 749 patients from four sites whose median year of surgery was 2007 and with median follow-up of 28 mo. ICC was segmented on one representative hematoxylin and eosin RP slide per patient and the fraction of tumor area composed of ICC, the cribriform area index (CAI), was measured.Outcome measurements and statistical analysisThe association between CAI and BCR was measured in terms of the concordance index (c index) and hazard ratio (HR).Results and limitationsCAI was correlated with BCR (c index 0.62) in the validation set of 411 patients with ICC morphology, especially those with Gleason grade group 2 cancer (n = 192; c index 0.66), and was less prognostic when patients without ICC were included (c index 0.54). A doubling of CAI in the group with ICC morphology was prognostic after controlling for Gleason grade, surgical margin positivity, preoperative prostate-specific antigen level, pathological T stage, and age (HR 1.19, 95% confidence interval 1.03-1.38; p = 0.018).ConclusionsAutomated image analysis and machine learning could provide an objective, quantitative, reproducible, and high-throughput method of quantifying ICC area. The performance of CAI for grade group 2 cancer suggests that for patients with little Gleason 4 pattern, the ICC fraction has a strong prognostic role.Patient summaryMachine-based measurement of a specific cell pattern (cribriform; sieve-like, with lots of spaces) in images of prostate specimens could improve risk stratification for patients with prostate cancer. In the future, this could help in expanding the criteria for active surveillance.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661. [ABSTRACT FROM AUTHOR]

Introduction: Prostate cancer (CaP) is a leading cancer diagnosis and cause of cancer-related deaths among Nigerian men, and the accuracy of the pathologic diagnosis is critical for optimal patient care. This study aims to define the relative proportional accuracy of the histological diagnosis of prostatic cancer with hematoxylin and eosin (H and E) slides at Federal Medical Centre (FMC) Owerri, Nigeria. It also seeks to determine Gleason's grading and grade group pattern of CaP in our hospital. Materials and Methods: This study is a 5-year retrospective study reviewing all the H and E slides of prostatic Tru-Cut biopsies that were already diagnosed as cancer at FMC Owerri. These diagnoses were read and confirmed using their morphological appearance on light microscopy. Those that were suspicious, especially those that were diagnosed with well-differentiated adenocarcinoma (Gleason 3 + 3) and poorly differentiated carcinoma (Gleason 5 + 5), were sent for immunohistochemical studies. The well-differentiated carcinomas were stained for basal cells using P63, whereas poorly differentiated carcinomas were stained with prostate-specific antigens to confirm tissue of origin. These are to differentiate them from benign mimickers and metastatic carcinomas, respectively. Results: The age range of the patients was between 50 and 99 years, with a mean age of 72.84 years and peak age in the 70–79 years of age group. Using immunohistochemistry on the specimens which fitted into the criteria for immunohistochemistry, this study showed the relative proportional accuracy of the histological diagnosis of prostatic cancer using H and E to be 95.55% and the positive predictive value of H and E to be 0.83. With respect to International Society of Urological Pathology (ISUP) group grading, high-grade cancers predominated, and the most predominant grade is 5. Conclusion: H and E stain has high diagnostic accuracy (95.55%), but a 4.45% diagnostic error using H and E is quite significant. Immunohistochemistry, therefore, should act as a most valuable adjunctive, which significantly increases the diagnostic accuracy of prostatic carcinoma diagnosis.

Semantic Segmentation aims to partition an image into separate regions where each region conveys certain valuable information. In recent years, deep learning models have achieved high performance in this task. However, when several ground truth segmentations are available, aggregating the information of these segmentations into a single ground truth becomes a crucial pre-processing step. This task becomes challenging when the segmentations are contradictory and the existing classes in the segmentations are imbalanced. An elegant example is the grading of Prostate Cancer in the Gleason 2019 Challenge dataset. This dataset provides six annotations of relatively high contrast from expert pathologists for each image. Additionally, the low number of images for Gleason grade 5 has also resulted in an imbalanced dataset. The Majority Voting and the Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm are the most popular algorithms for combining annotations. In this paper, we visually show that the outputs of these algorithms discard the semantics of the image in regions of high inconsistency and point out that they demote low-frequency patterns, making the final segmentations even more imbalanced. We claim these drawbacks highly decrease the performance of deep learning models trained on these ground truths. To solve this problem, we propose a dynamic score function that selects one of the six annotations for each image while balancing the Gleason grading among the annotations in terms of variability and quantity. Finally, we train and evaluate a Pyramid Scene Parsing network on the final ground truths to validate our claims.

Background: The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer’s aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA). Methods: Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label. Results: Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98. Conclusion: Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.

This survey paper is motivated by the imperative need for advanced and precise diagnostic tools in the realm of prostate cancer, where Gleason grading plays a pivotal role in determining the severity and treatment strategy. The aim of this comprehensive review is to explore and assess the diverse spectrum of deep learning approaches applied to prostate cancer Gleason grading, with a specific focus on convolutional neural networks (CNNs), transfer learning, ensemble methods, and emerging techniques. The primary contribution lies in offering a consolidated understanding of the current state-of-the-art methodologies, their architectures, and training strategies, while also addressing challenges and advancements in the integration of deep learning into clinical workflows. Furthermore, the survey discusses recent developments such as the incorporation of multimodal data and explainable AI methods, shedding light on their potential to enhance the interpretability and adoption of deep learning models in the critical domain of prostate cancer diagnosis. Through this, the paper aims to provide a valuable resource for researchers, clinicians, and practitioners, guiding future endeavors toward more accurate and efficient Gleason grading using deep learning techniques.

Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network framework for multi-task prediction using an epithelial network head and a grading network head. Compared with a single-task model, our multi-task model can provide complementary contextual information, which contributes to better performance. Our model is achieved a state-of-the-art performance in epithelial cells detection and Gleason grading tasks simultaneously. Using fivefold cross-validation, our model is achieved an epithelial cells detection accuracy of 99.07% with an average area under the curve of 0.998. As for Gleason grading, our model is obtained a mean intersection over union of 79.56% and an overall pixel accuracy of 89.40%.

Introduction: Prostate cancer (CaP) is a leading cancer diagnosis and cause of cancer-related deaths among Nigerian men, and the accuracy of the pathologic diagnosis is critical for optimal patient care. This study aims to define the relative proportional accuracy of the histological diagnosis of prostatic cancer with hematoxylin and eosin (H and E) slides at Federal Medical Centre (FMC) Owerri, Nigeria. It also seeks to determine Gleason's grading and grade group pattern of CaP in our hospital. Materials and Methods: This study is a 5-year retrospective study reviewing all the H and E slides of prostatic Tru-Cut biopsies that were already diagnosed as cancer at FMC Owerri. These diagnoses were read and confirmed using their morphological appearance on light microscopy. Those that were suspicious, especially those that were diagnosed with well-differentiated adenocarcinoma (Gleason 3 + 3) and poorly differentiated carcinoma (Gleason 5 + 5), were sent for immunohistochemical studies. The well-differentiated carcinomas were stained for basal cells using P63, whereas poorly differentiated carcinomas were stained with prostate-specific antigens to confirm tissue of origin. These are to differentiate them from benign mimickers and metastatic carcinomas, respectively. Results: The age range of the patients was between 50 and 99 years, with a mean age of 72.84 years and peak age in the 70–79 years of age group. Using immunohistochemistry on the specimens which fitted into the criteria for immunohistochemistry, this study showed the relative proportional accuracy of the histological diagnosis of prostatic cancer using H and E to be 95.55% and the positive predictive value of H and E to be 0.83. With respect to International Society of Urological Pathology (ISUP) group grading, high-grade cancers predominated, and the most predominant grade is 5. Conclusion: H and E stain has high diagnostic accuracy (95.55%), but a 4.45% diagnostic error using H and E is quite significant. Immunohistochemistry, therefore, should act as a most valuable adjunctive, which significantly increases the diagnostic accuracy of prostatic carcinoma diagnosis. [ABSTRACT FROM AUTHOR]

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Gleason grading system is dependable for quantifying prostate cancer. This paper introduces a fast multiphoton microscopic imaging method via deep learning for automatic Gleason grading. Due to the contradiction between multiphoton microscopy (MPM) imaging speed and quality, a deep learning architecture (SwinIR) is used for image super-resolution to address this issue. The quality of low-resolution image is improved, which increased the acquisition speed from 7.55 s per frame to 0.24 s per frame. A classification network (Swin Transformer) was introduced for automated Gleason grading. The classification accuracy and Macro-F1 achieved by training on high-resolution images are respectively 90.9% and 90.9%. For training on super-resolution images, the classification accuracy and Macro-F1 are respectively 89.9% and 89.9%. It shows that super-resolution image can provide a comparable performance to high-resolution image. Our results suggested that MPM joint image super-resolution and automatic classification methods hold the potential to be a real-time clinical diagnostic tool for prostate cancer diagnosis., (© 2024 Wiley‐VCH GmbH.)

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology. Article research was performed using PubMed and Embase to collect relevant articles. A Risk of Bias (RoB) was assessed with an adaptation of the QUADAS-2 tool. Out of the 77 included studies, eight focused on pre-processing tasks such as quality assessment or staining normalization. Most articles (n = 53) focused on diagnosis tasks like cancer detection or Gleason grading. Fifteen articles focused on prediction tasks, such as recurrence prediction or genomic correlations. Best performances were reached for cancer detection with an Area Under the Curve (AUC) up to 0.99 with algorithms already available for routine diagnosis. A few biases outlined by the RoB analysis are often found in these articles, such as the lack of external validation. This review was registered on PROSPERO under CRD42023418661.

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000–2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility. [ABSTRACT FROM AUTHOR]

The identification of benign prostatic tissue within ovarian and testicular mature teratomas is an unusual occurrence. While a few documented reports exist in the literature regarding the emergence of benign prostatic tissue within teratomas, the occurrence of prostatic-type adenocarcinoma in a mature ovarian teratoma is an exceptionally rare phenomenon. To date, only two prior reports have documented such instances, and no tumors have been previously reported with prostate-type tissue with morphologically two different malignancies. We outline our experience with two tumors involving prostatic-type carcinoma, both arising in ovarian mature teratomas. Microscopic examination of the first tumor revealed small areas of infiltrative atypical glandular proliferation within the mature teratoma. In the second tumor, prostate-type tissue exhibited a low-grade basal cell carcinoma. Additionally, adjacent minute foci of adenocarcinoma of the prostate (Gleason score 3 + 4 = 7, <5% pattern 4) were identified. Goblet cell adenocarcinoma of appendiceal type was also evident in the latter tumor. In both tumors, immunostains (NKX3.1, PSA) were performed to establish the prostatic origin of these atypical glands and PIN4 was performed to document the absence of basal cell in the atypical glands. On clinical follow-up, both patients have no signs of recurrence at 14 and 11 months after the surgery. Further reports on such neoplasms would contribute to a better understanding of the prognosis and management of such occurrences. [ABSTRACT FROM AUTHOR]

Purpose: Prostate biopsy histopathology and immunohistochemistry are important in the differential diagnosis of the disease and can be used to assess the degree of prostate cancer differentiation. Today, prostate biopsy is increasing the demand for experienced uropathologists, which puts a lot of pressure on pathologists. In addition, the grades of different observations had an indicating effect on the treatment of the patients with cancer, but the grades were highly changeable, and excessive treatment and insufficient treatment often occurred. To alleviate these problems, an artificial intelligence system with clinically acceptable prostate cancer detection and Gleason grade accuracy was developed. Methods: Deep learning algorithms have been proved to outperform other algorithms in the analysis of large data and show great potential with respect to the analysis of pathological sections. Inspired by the classical semantic segmentation network, we propose a pyramid semantic parsing network (PSPNet) for automatic prostate Gleason grading. To boost the segmentation performance, we get an auxiliary prediction output, which is mainly the optimization of auxiliary objective function in the process of network training. The network not only includes effective global prior representations but also achieves good results in tissue micro-array (TMA) image segmentation. Results: Our method is validated using 321 biopsies from the Vancouver Prostate Centre and ranks the first on the MICCAI 2019 prostate segmentation and classification benchmark and the Vancouver Prostate Centre data. To prove the reliability of the proposed method, we also conduct an experiment to test the consistency with the diagnosis of pathologists. It demonstrates that the well-designed method in our study can achieve good results. The experiment also focused on the distinction between high-risk cancer (Gleason pattern 4, 5) and low-risk cancer (Gleason pattern 3). Our proposed method also achieves the best performance with respect to various evaluation metrics for distinguishing benign from malignant. Availability: The Python source code of the proposed method is publicly available at https://github.com/hubutui/Gleason. All implementation details are presented in this paper. Conclusion: These works prove that the Gleason grading results obtained from our method are effective and accurate. [ABSTRACT FROM AUTHOR]

PurposeProstate biopsy histopathology and immunohistochemistry are important in the differential diagnosis of the disease and can be used to assess the degree of prostate cancer differentiation. Today, prostate biopsy is increasing the demand for experienced uropathologists, which puts a lot of pressure on pathologists. In addition, the grades of different observations had an indicating effect on the treatment of the patients with cancer, but the grades were highly changeable, and excessive treatment and insufficient treatment often occurred. To alleviate these problems, an artificial intelligence system with clinically acceptable prostate cancer detection and Gleason grade accuracy was developed.MethodsDeep learning algorithms have been proved to outperform other algorithms in the analysis of large data and show great potential with respect to the analysis of pathological sections. Inspired by the classical semantic segmentation network, we propose a pyramid semantic parsing network (PSPNet) for automatic prostate Gleason grading. To boost the segmentation performance, we get an auxiliary prediction output, which is mainly the optimization of auxiliary objective function in the process of network training. The network not only includes effective global prior representations but also achieves good results in tissue micro-array (TMA) image segmentation.ResultsOur method is validated using 321 biopsies from the Vancouver Prostate Centre and ranks the first on the MICCAI 2019 prostate segmentation and classification benchmark and the Vancouver Prostate Centre data. To prove the reliability of the proposed method, we also conduct an experiment to test the consistency with the diagnosis of pathologists. It demonstrates that the well-designed method in our study can achieve good results. The experiment also focused on the distinction between high-risk cancer (Gleason pattern 4, 5) and low-risk cancer (Gleason pattern 3). Our proposed method also achieves the best performance with respect to various evaluation metrics for distinguishing benign from malignant.AvailabilityThe Python source code of the proposed method is publicly available at https://github.com/hubutui/Gleason. All implementation details are presented in this paper.ConclusionThese works prove that the Gleason grading results obtained from our method are effective and accurate.

Introduction: ISUP Grade Group 1 prostate cancer is the lowest histologic grade of prostate cancer with a clinically indolent course. Removal of the term 'cancer' has been proposed and has historical precedent both in urothelial and thyroid carcinoma. Methods: Evidence-based review identifying arguments for and against Grade Group 1 being referred to as cancer. Results: Grade Group 1 has histologic evidence of tissue microinvasion and 0.3–3% rate of extraprostatic extension. Genomic evaluation suggests overlap of a minority of Grade Group 1 cancers with those of Grade Group 2. Conversely, Grade Group 1 tumors appear to have distinct genetic and genomic profiles from Grade Group 3 or higher tumors. Grade Group 1 has no documented ability for regional or distant metastasis and long-term follow up after treatment or active surveillance is safe with excellent oncologic outcomes. Discussion: Grade Group 1 prostate cancer, while showing evidence of neoplasia on histology has a remarkably indolent natural history more akin to non-neoplastic precursor lesions. Consideration should be given to renaming Grade Group 1 prostate cancer, which has the potential to minimize overtreatment, treatment-related side effects, patient anxiety, and financial burden on the healthcare system. [ABSTRACT FROM AUTHOR]

Prostate cancer is the second-deadliest cancer in men in the United States, seriously affecting people's life and health. The Gleason grading system is one of the most reliable methods to quantify the invasiveness of prostate cancer, which is of great significance for risk assessment and treatment planning for patients. However, the task of automating Gleason grading is difficult because of the complexity of pathological images of prostate cancer. This paper presents an automated Gleason grading and Gleason pattern region segmentation method based on deep learning for pathological images of prostate cancer. An architecture combining the atrous spatial pyramid pooling and the multiscale standard convolution is proposed for the segmentation of the Gleason pattern region to get accurate Gleason grading. In addition, the postprocessing procedure based on conditional random fields is applied to the prediction. The quantitative experiments on 1211 prostate cancer tissue microarrays demonstrate that our results have a high correlation with the manual segmentations. The mean intersection over union and the overall pixel accuracy for the Gleason pattern region are 77.29% and 89.51%, respectively. Furthermore, the results of the automatic Gleason grading were comparable to the results of experienced pathologists. The inter-annotator agreements between the model and the pathologists, quantified via Cohen's quadratic kappa statistic, was 0.77 on average. Our study shows that the method of combining different deep neural network architectures is suitable for more objective and reproducible Gleason grading of prostate cancer.

Purpose : This study aims to assess whole-mount Gleason grading (GG) in prostate cancer (PCa) accurately using a multiomics machine learning (ML) model and to compare its performance with biopsy-proven GG (bxGG) assessment. Materials and Methods : A total of 146 patients with PCa recruited in a pilot study of a prospective clinical trial (NCT02659527) were retrospectively included in the side study, all of whom underwent 68 Ga-PSMA-11 integrated positron emission tomography (PET) / magnetic resonance (MR) before radical prostatectomy (RP) between May 2014 and April 2020. To establish a multiomics ML model, we quantified PET radiomics features, pathway-level genomics features from whole exome sequencing, and pathomics features derived from immunohistochemical staining of 11 biomarkers. Based on the multiomics dataset, five ML models were established and validated using 100-fold Monte Carlo cross-validation. Results : Among five ML models, the random forest (RF) model performed best in terms of the area under the curve (AUC). Compared to bxGG assessment alone, the RF model was superior in terms of AUC (0.87 vs 0.75), specificity (0.72 vs 0.61), positive predictive value (0.79 vs 0.75), and accuracy (0.78 vs 0.77) and showed slightly decreased sensitivity (0.83 vs 0.89) and negative predictive value (0.80 vs 0.81). Among the feature categories, bxGG was identified as the most important feature, followed by pathomics, clinical, radiomics and genomics features. The three important individual features were bxGG, PSA staining and one intensity-related radiomics feature. Conclusion : The findings demonstrate a superior assessment of the developed multiomics-based ML model in whole-mount GG compared to the current clinical baseline of bxGG. This enables personalized patient management by identifying high-risk PCa patients for RP., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology., (Copyright © 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights reserved.)

The biopsy Gleason score is an important prognostic marker for prostate cancer patients. It is, however, subject to substantial variability among pathologists. Artificial intelligence (AI)-based algorithms employing deep learning have shown their ability to match pathologists' performance in assigning Gleason scores, with the potential to enhance pathologists' grading accuracy. The performance of Gleason AI algorithms in research is mostly reported on common benchmark data sets or within public challenges. In contrast, many commercial algorithms are evaluated in clinical studies, for which data are not publicly released. As commercial AI vendors typically do not publish performance on public benchmarks, comparison between research and commercial AI is difficult. The aims of this study are to evaluate and compare the performance of top-ranked public and commercial algorithms using real-world data. We curated a diverse data set of whole-slide prostate biopsy images through crowdsourcing containing images with a range of Gleason scores and from diverse sources. Predictions were obtained from 5 top-ranked public algorithms from the Prostate cANcer graDe Assessment (PANDA) challenge and 2 commercial Gleason grading algorithms. Additionally, 10 pathologists (A.C., C.R., J.v.I., K.R.M.L., P.R., P.G.S., R.G., S.F.K.J., T.v.d.K., X.F.) evaluated the data set in a reader study. Overall, the pairwise quadratic weighted kappa among pathologists ranged from 0.777 to 0.916. Both public and commercial algorithms showed high agreement with pathologists, with quadratic kappa ranging from 0.617 to 0.900. Commercial algorithms performed on par or outperformed top public algorithms., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

International Society of Urological Pathology grade group 1 (GG 1) prostate cancer (PCa) is generally considered insignificant, with recent suggestions that it should even be considered as "noncancerous". We evaluated outcomes for patients with GG 1 PCa on biopsy (bGG 1) and high-risk features (prostate-specific antigen [PSA] >20 ng/ml and/or cT3-4 stage) to challenge the hypothesis that every case of bGG 1 PCa has a benign disease course. We used the multi-institutional EMPaCT database, which includes data for 9508 patients with high-risk PCa undergoing surgery. We included patients with bGG 1 PCa ( n  = 848) in our analysis and divided them into three groups according to PSA >20 ng/ml, cT3-4 stage, or both. The estimated 10-yr cancer-specific survival (CSS) rate was 96% in the overall population, 88% in the group with both PSA >20 ng/ml and cT3-4 stage, 97% in the group with PSA >20 ng/ml alone, and 98% in the group with cT3-4 stage alone. Similar CSS outcomes were found in subgroups with GG 1 PCa on pathology ( n  = 502) and with GG 1 on biopsy diagnosed after 2005 ( n  = 253). Study limitations include the lack of magnetic resonance imaging (MRI) staging and MRI-targeted biopsies. In conclusion, patients with GG 1 and either PSA >20 ng/ml or cT3-4 stage have a low risk of dying from their cancer after surgery. However, patients with GG 1 PCa and both PSA >20 ng/ml and cT3-4 stage are at higher risk of cancer-specific mortality and active treatment should be discussed for this subgroup., Patient Summary: We assessed outcomes for patients diagnosed with low-grade prostate cancer on biopsy who also had one or two factors associated with high risk disease. Men with both of those risk factors had a higher risk of dying from their prostate cancer. Active treatment should be discussed for this subgroup of patients., (© 2024 The Author(s).)

Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Furthermore, despite the promising results achieved on global scoring the location of cancerous patterns in the tissue is only qualitatively addressed. These heatmaps of tumor regions, however, are crucial to the reliability of CAD systems as they provide explainability to the system's output and give confidence to pathologists that the model is focusing on medical relevant features. Motivated by this, we propose a novel weakly-supervised deep-learning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin, as well as state-of-the-art methods on global biopsy-level scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (κ) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task. This suggests that the absence of the annotator's bias in our approach and the capability of using large weakly labeled datasets during training leads to higher performing and more robust models. Furthermore, raw features obtained from the patch-level classifier showed to generalize better than previous approaches in the literature to the subjective global biopsy-level scoring. [ABSTRACT FROM AUTHOR]

Prostate biopsy data are critical for patient management. Guidelines focus of detailed criteria to ensure that these are precisely and reproducibly reported. However, conflicting recommendations by different expert groups have led to significant reporting variation that could negatively impact patient care. We describe a different approach focusing on optimising communication of histopathological parameters such as tumour extent and grade to the clinical team. Communication is crucial because clinicians usually do not view histological material and are dependent on the information included in the histopathology report. Precision could become less critical if the biopsy findings are effectively conveyed by the pathologist and correctly interpreted by the clinician. Strategies to effectively communicate the message in contentious scenarios such when a prostate needle biopsy set contains prostate cancer that is discontinuous, of borderline grade, of disparate grades or associated with intraductal carcinoma of the prostate are outlined. [ABSTRACT FROM AUTHOR]

Purpose: While multi‐parametric magnetic resonance imaging (MRI) shows great promise in assisting with prostate cancer diagnosis and localization, subtle differences in appearance between cancer and normal tissue lead to many false positive and false negative interpretations by radiologists. We sought to automatically detect aggressive cancer (Gleason pattern ≥ 4) and indolent cancer (Gleason pattern 3) on a per‐pixel basis on MRI to facilitate the targeting of aggressive cancer during biopsy. Methods: We created the Stanford Prostate Cancer Network (SPCNet), a convolutional neural network model, trained to distinguish between aggressive cancer, indolent cancer, and normal tissue on MRI. Ground truth cancer labels were obtained by registering MRI with whole‐mount digital histopathology images from patients who underwent radical prostatectomy. Before registration, these histopathology images were automatically annotated to show Gleason patterns on a per‐pixel basis. The model was trained on data from 78 patients who underwent radical prostatectomy and 24 patients without prostate cancer. The model was evaluated on a pixel and lesion level in 322 patients, including six patients with normal MRI and no cancer, 23 patients who underwent radical prostatectomy, and 293 patients who underwent biopsy. Moreover, we assessed the ability of our model to detect clinically significant cancer (lesions with an aggressive component) and compared it to the performance of radiologists. Results: Our model detected clinically significant lesions with an area under the receiver operator characteristics curve of 0.75 for radical prostatectomy patients and 0.80 for biopsy patients. Moreover, the model detected up to 18% of lesions missed by radiologists, and overall had a sensitivity and specificity that approached that of radiologists in detecting clinically significant cancer. Conclusions: Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment. [ABSTRACT FROM AUTHOR]

The Gleason grading system for prostate cancer (PCa) was developed in the 1960s by DF Gleason. Due to changes in PCa detection and treatment, the application of the Gleason grading system has changed considerably in pathology routine practice. Two consensus conferences were held in 2005 and in 2014 to update PCa Gleason grading. This review provides a summary of the changes in the grading of PCa from the original Gleason grading system to the prognostic grade grouping, as well as a discussion of the clinical significance of the percentage of Gleason patterns 4 and 5.

Among American men, prostate cancer is the cause of the second-highest death by any cancer. It is also the most common cancer in men worldwide, and the annual numbers are quite alarming. The most prognostic marker for prostate cancer is the Gleason grading system on histopathology images. Pathologists determine the Gleason grade on stained tissue specimens of Hematoxylin and Eosin (H&E) based on tumor structural growth patterns from whole slide images. Recent advances in Computer-Aided Detection (CAD) using deep learning have brought the immense scope of automatic detection and recognition at very high accuracy in prostate cancer like other medical diagnoses and prognoses. Automated deep learning systems have delivered promising results from histopathological images to accurate grading of prostate cancer. Many studies have shown that deep learning strategies can achieve better outcomes than simpler systems that make use of pathology samples. This article aims to provide an insight into the gradual evolution of deep learning in detecting prostate cancer and Gleason grading. This article also evaluates a comprehensive, synthesized overview of the current state and existing methodological approaches as well as unique insights in prostate cancer detection using deep learning. We have also described research findings, current limitations, and future avenues for research. We have tried to make this paper applicable to deep learning communities and hope it will encourage new collaborations to create dedicated applications and improvements for prostate cancer detection and Gleason grading.

The reporting of intraductal carcinoma of the prostate (IDCP) is controversial, with conflicting recommendations having recently been published by the International Society of Urological Pathology (ISUP) and the Genitourinary Pathology Society (GUPS). Both recommend that isolated (pure) IDCP should not be graded. However, the ISUP recommends incorporating the IDCP component of invasive prostate cancer in the Gleason score, whereas the GUPS recommends reporting IDCP as a comment, independently of the Gleason score. The arguments for and against incorporating the IDCP component of invasive prostate cancer in the Gleason score are discussed in detail. [ABSTRACT FROM AUTHOR]

The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Background: Prostate cancer ranks as the most frequently diagnosed cancer in men in the USA, with significant mortality rates. Early detection is pivotal for optimal patient outcomes, providing increased treatment options and potentially less invasive interventions. There remain significant challenges in prostate cancer histopathology, including the potential for missed diagnoses due to pathologist variability and subjective interpretations., Methods: To address these challenges, this study investigates the ability of artificial intelligence (AI) to enhance diagnostic accuracy. The Galen™ Prostate AI algorithm was validated on a cohort of Puerto Rican men to demonstrate its efficacy in cancer detection and Gleason grading. Subsequently, the AI algorithm was integrated into routine clinical practice during a 3-year period at a CLIA certified precision pathology laboratory., Results: The Galen™ Prostate AI algorithm showed a 96.7% (95% CI 95.6-97.8) specificity and a 96.6% (95% CI 93.3-98.8) sensitivity for prostate cancer detection and 82.1% specificity (95% CI 73.9-88.5) and 81.1% sensitivity (95% CI 73.7-87.2) for distinction of Gleason Grade Group 1 from Grade Group 2+. The subsequent AI integration into routine clinical use examined prostate cancer diagnoses on >122,000 slides and 9200 cases over 3 years and had an overall AI Impact ™ factor of 1.8%., Conclusions: The potential of AI to be a powerful, reliable, and effective diagnostic tool for pathologists is highlighted, while the AI Impact™ in a real-world setting demonstrates the ability of AI to standardize prostate cancer diagnosis at a high level of performance across pathologists., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mariano de Socarraz reports a relationship with Ibex Medical Analytics that includes: equity or stocks. Juan C. Santa Rosario reports a relationship with Ibex Medical Analytics that includes: equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Grading prostate biopsies has an important role in determining treatment strategy. Histopathological evaluations suffer from interobserver variability and therefore biopsies may be re-evaluated. To provide insight into the extent of, characteristics associated with and clinical implications of prostate biopsy re-evaluations in daily clinical practice. Patients diagnosed with prostate cancer (PCa) by biopsy between October 2015 and April 2016 identified through the Netherlands Cancer Registry were included. The proportion of re-evaluations was assessed and characteristics were compared between patients with and without biopsy re-evaluation. Interobserver concordance of ISUP grade and EAU prognostic risk classification was determined by calculating Cohen's kappa. Biopsy re-evaluation was performed in 172 (3.3%) of 5214 patients. Primary reason for re-evaluation in patients treated with curative intent was referral to another hospital. Most referred patients treated with curative intent (n = 1856) had no re-evaluation (93.0%, n = 1727). Patients with biopsy re-evaluation were younger and underwent more often prostatectomy compared to patients without re-evaluation. The disagreement rate for ISUP grade was 26.1% and interobserver concordance was substantial (κ-weighted = 0.74). Re-evaluation resulted in 21.1% (n = 14) of patients with localised PCa in a different prognostic risk group. More tumours were downgraded (57.1%) than upgraded (42.9%). Interobserver concordance was very good (κweighted = 0.85). Pathology review of prostate biopsies is infrequently requested by clinicians in the Netherlands but in a non-negligible minority of patients with localised PCa the pathology review led to a change in prognostic risk group which might impact their treatment. [ABSTRACT FROM AUTHOR]

Visual inspection of histopathology images of stained biopsy tissue by expert pathologists is the standard method for grading of prostate cancer (PCa). However, this process is time-consuming and subject to high inter-observer variability. Machine learning-based methods have the potential to improve efficient throughput of large volumes of slides while decreasing variability, but they are not easy to develop because they require substantial amounts of labeled training data. In this paper, we propose a deep learning-based classification technique and data augmentation methods for accurate grading of PCa in histopathology images in the presence of limited data. Our method combines the predictions of three separate convolutional neural networks (CNNs) that work with different patch sizes. This enables our method to take advantage of the greater amount of contextual information in larger patches as well as greater quantity of smaller patches in the labeled training data. The predictions produced by the three CNNs are combined using a logistic regression model, which is trained separately after the CNN training. To effectively train our models, we propose new data augmentation methods and empirically study their effects on the classification accuracy. The proposed method achieves an accuracy of $\text{92}\%$ in classifying cancerous patches versus benign patches and an accuracy of $\text{86}\%$ in classifying low-grade (i.e., Gleason grade 3) from high-grade (i.e., Gleason grades 4 and 5) patches. The agreement level of our automatic grading method with expert pathologists is within the range of agreement between pathologists. Our experiments indicate that data augmentation is necessary for achieving expert-level performance with deep learning-based methods. A combination of image-space augmentation and feature-space augmentation leads to the best results. Our study shows that well-designed and properly trained deep learning models can achieve PCa Gleason grading accuracy that is comparable to an expert pathologist. [ABSTRACT FROM AUTHOR]

Introduction: Gleason scoring (GS) categorised prostatic adenocarcinoma into five prognostic grade groups (PGGs); associated with different prognosis and treatment. This study aims to correlate between Gleason scores of needle biopsies with the corresponding total prostatectomy specimens, and to assess the relationship between the percentage of Gleason 4 tumour pattern (GP4) within Gleason score 7 (GS7) needle biopsy groups with the pathological staging. Materials and Methods: Seventy-eight specimens of needle prostate biopsy and its subsequent radical prostatectomy were retrospectively studied. The GSs of the needle biopsy were compared with the corresponding prostatectomy specimens. The percentage of GP4 in GS7 needle biopsy groups was calculated and correlated with the pathological staging. Results: More than half (60%) of GS 6 needle biopsy cases (PGG 1) were upgraded in the prostatectomy specimen, while the majority (80%) of the GS7 needle biopsy groups (PGG 2 and 3) remain unchanged. Cohen's Kappa shows fair agreement in the Gleason scoring between needle biopsies and prostatectomy specimens, K = 0.324 (95% CI, 6.94 to 7.29), p <0.0005 and in the percentage of GP4 in GS7 needle biopsy groups and their corresponding radical prostatectomy specimens, K = 0.399 (95% CI 34.2 -- 49.2), p<0.0005. A significant relationship was seen between the percentage of GP4 in GS7 needle biopsy with the pT and pN stage of its radical prostatectomy (p = 0.008 and p=0.001 respectively). Conclusion: A higher percentage of GP4 in GS7 tumour is associated with worse tumour behaviour, therefore it is crucial for clinicians to realise this in deciding the optimal treatment. [ABSTRACT FROM AUTHOR]