Grade Prostate Cancer Research Articles

4D pathology: translating dynamic epithelial tubulogenesis to prostate cancer pathology.

The Gleason score is the gold standard for grading of prostate cancer (PCa) and is assessed by assigning specific grades to different microscopical growth patterns. Aside from the Gleason grades, individual growth patterns such as cribriform architecture were recently shown to have independent prognostic value for disease outcome. PCa grading is performed on static tissue samples collected at one point in time, whereas in vivo epithelial tumour structures are dynamically invading, branching and expanding into the surrounding stroma. Due to the lack of models that are able to track human PCa microscopical developments over time, our understanding of underlying tissue dynamics is sparse. We postulate that human PCa expansion utilizes embryonic and developmental tubulogenetic pathways. The aim of this study is to provide a comprehensive overview of developmental pathways of normal epithelial tubule formation, elongation, and branching, and relate those to the static microscopical PCa growth patterns observed in daily clinical practise. This study could provide a rationale for the discerned pathological interobserver variability and the clinical outcome differences between PCa growth patterns.

Abstract A031: Methylomic Biomarkers Associated with Prostate Cancer in Black Men

Abstract Introduction: The prostate cancer (PCa) disparity remains the highest cancer disparity in the US, with Black men facing an estimated 78% higher incidence and 230% higher mortality than White men. However, current understanding of risk factors for PCa such as socioeconomic status (SES), diet, and genetics cannot fully explain these disparities. Our objective is to examine DNA methylation (DNAm) markers in a cohort of Black men and assess their associations with PCa status. Methods: We randomly selected 100 Black men for DNAm profiling (50 cases diagnosed with clinically significant (Gleason Grade 3+) prostate cancer and 50 age-matched controls) from two larger cohorts. The cohorts recruited 400 Black men who were newly diagnosed with sporadic PCa as well as 400 controls matched on age and race. All men were 40-79 at recruitment and were enrolled from urology clinics at five Chicago-area medical institutions. All PCa cases underwent a transrectal prostate biopsy for abnormal prostate-specific antigen (PSA) or digital rectal exam and had a histologically confirmed prostate cancer and were not previously diagnosed with PCa. All men in the control group had been recruited from community prostate cancer screening events or were referred to participating urology clinics and had a negative prostate biopsy for a PSA <10ng/ml and were not diagnosed with PCa for 2 years of follow-up. Men with prior PCa diagnosis or conditions known to affect PSA levels were excluded. After enrollment and informed consent, all men provided demographic and lifestyle data via three questionnaires, followed by venipuncture for DNA collection. These specimens were obtained using buffy coat extraction after red blood cell lysis, centrifugation, and pipetting off the overlying plasma and buffy coat for freezing followed by Illumina EPIC arrays for DNAm assays. We examined DNAm markers in gene promoter regions associated with PCa using logistic regression and adjusted for multiple testing using Bonferroni correction. All models controlled for age, BMI, smoking status, first degree family history of PCa, blood cell types, and technical control variables. Results: We found 2,180 CpG sites in gene promoter regions associated with any PCa in this population. The top ten strongest associations identified in our models include loci on genes that have already been associated with prostate cancer progression (MAFG, FAM65B, OSM, PRKAG2), risk (SLC11A1, LAT), and metastasis (S1PR4) as well as immune system functioning (RAP1GAP2). Conclusions: Our initial findings suggest that DNAm markers may be useful predictors of clinically significant PCa status among Black men. Additional research is currently ongoing to expand the sample size of the DNAm profiling and to explore links between these loci and social determinants of health, as well as associations with more advanced (i.e., higher Gleason Grade) PCa. Citation Format: Brian T. Joyce, Yishu Qu, Jun Wang, Kai Zhang, Zequn Sun, Lifang Hou, Adam B. Murphy. Methylomic Biomarkers Associated with Prostate Cancer in Black Men [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A031.

Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort.

Localized prostate tumors show significant spatial heterogeneity, with regions of high-grade disease adjacent to lower-grade disease. Consequently, prostate cancer biopsies are prone to sampling bias, potentially leading to underestimation of tumor grade. To study the clinical, epidemiologic and molecular hallmarks of this phenomenon, we conducted a prospective study of grade upgrading: differences in detected prostate cancer grade between biopsy and surgery. We established a prospective, multi-institutional cohort of men with Grade Group 1 (GG1) prostate cancer on biopsy who underwent radical prostatectomy. Upgrading was defined as detection of GG2+ in the resected tumor. Germline DNA from 192 subjects was subjected to whole-genome sequencing to quantify ancestry, pathogenic variants in DNA damage response genes and polygenic risk. Of 285 men, 67% upgraded at surgery. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. No assessed genetic risk factor was predictive of upgrading, including polygenic risk scores for prostate cancer diagnosis. In a cohort of low-grade prostate cancer patients, a majority upgraded at radical prostatectomy. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores portended the presence of higher-grade disease, while germline genetics was not informative in this setting. Patients with low-risk prostate cancer, but elevated PSA density or percent cancer in positive biopsy cores, may benefit from repeat biopsy, additional imaging or other approaches to complement active surveillance. Further risk stratification of patients with low-risk prostate cancer may provide useful context for active surveillance decision-making.

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

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

Consistency between Gleason score of prostate biopsy and radical prostatectomy: predictors of increased tumour grading

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.

Analysis of the Performance and Accuracy of a PSA and PSA Ratio-Based Nomogram to Predict the Probability of Prostate Cancer in a Cohort of Patients with PIRADS 3 Findings at Multiparametric Magnetic Resonance Imaging.

PIRADS score 3 represents a challenge in prostate cancer (PCa) detection with MRI. Our study aimed to evaluate the application of a nomogram on a cohort of patients with PIRADS 3. We analyzed 286 patients undergoing fusion prostate biopsy from January 2020 to February 2024. Only PIRADS 3 patients were included. Two nomograms, previously developed and based on clinical variables such as age, total PSA (specifically 2-10 ng/mL) and PSA ratio were applied to estimate the probability (Nomograms A and B) for PCa Grade Group (GG) > 3 and GG < 3. Out of the 70 patients available for analysis, 14/70 patients (20%) had PCa, 4/14 were GG 1 (28.6%), 1/14 were GG 2 (7.1%), 5/14 were GG 3 (35.8%), 2/14 were GG 4 (14.3%) and 2/14 were GG 5 (14.3%). The median probability of PCa GG > 3 and GG < 3 was 5% and 33%, respectively. A significant difference (p = 0.033) was found between patients with negative versus positive biopsy for Nomogram B. There was a significant difference (p = 0.029) for Nomogram B comparing patients with GG < 3 and GG > 3. Using a cut-off of 40% for Nomogram B, sensitivity and specificity were 70% and 80%, respectively. This cohort has a low probability of harboring PCa especially ISUP > 3. Nomogram B has good accuracy for discriminating patients with PCa from those with negative biopsy.

Patients ask and pathologists answer: ten questions around prostate cancer grading.

Pathologists have closely collaborated with clinicians, mainly urologists, to update the Gleason grading system to reflect the current practice and approach in prostate cancer diagnosis, prognosis, and treatment. This has led to the development of what is called patient advocacy and patient information. Ten common questions asked by patients to pathologists concerning PCa grading and the answers given by the latter are reported.

Reinterpretation of prostate cancer pathology by Appl1, Sortilin and Syndecan-1 biomarkers

The diagnosis of prostate cancer using histopathology is reliant on the accurate interpretation of prostate tissue sections. Current standards rely on the assessment of Haematoxylin and Eosin (H&E) staining, which can be difficult to interpret and introduce inter-observer variability. Here, we present a digital pathology atlas and online resource of prostate cancer tissue micrographs for both H&E and the reinterpretation of samples using a novel set of three biomarkers as an interactive tool, where clinicians and scientists can explore high resolution histopathology from various case studies. The digital pathology prostate cancer atlas when used in conjunction with the biomarkers, will assist pathologists to accurately grade prostate cancer tissue samples.

NAT10 Promotes Prostate Cancer Growth and Metastasis by Acetylating mRNAs of HMGA1 and KRT8.

N4-acetylcytidine (ac4C) is essential for the development and migration of tumor cells. According to earlier research, N-acetyltransferase 10 (NAT10) can increase messenger RNAs (mRNAs) stability by catalyzing the synthesis of ac4C. However, little is known about NAT10 expression and its role in the acetylation modifications in prostate cancer (PCa). Thus, the biological function of NAT10 in PCa is investigated in this study. Compared to paraneoplastic tissues, the expression of NAT10 is significantly higher in PCa. The NAT10 expression is strongly correlated with the pathological grade, clinical stage, Gleason score, T-stage, and N-stage of PCa. NAT10 has the ability to advance the cell cycle and the epithelial-mesenchymal transition (EMT), both of which raise the malignancy of tumor cells. Mechanistically, NAT10 enhance the stability of high mobility group AT-hook 1 (HMGA1) by acetylating its mRNA, thereby promoting cell cycle progression to improve cell proliferation. In addition, NAT10 improve the stability of Keratin 8 (KRT8) by acetylating its mRNA, which promotes the progression of EMT to improve cell migration. This findings provide a potential prognostic or therapeutic target for PCa.

Do Prostate Imaging-Reporting and Data System (PIRADS) lesions predict holmium laser enucleation of prostate outcomes?

Prostate magnetic resonance imaging (MRI) is used for prostate cancer (PCa) screening and risk stratification and is helpful for surgical planning for patients undergoing holmium laser enucleation of the prostate (HoLEP). There are few studies investigating the correlation between MRI Prostate Imaging-Reporting and Data System (PIRADS) lesion characteristics and HoLEP pathology and outcomes. We performed retrospective review of patients who underwent HoLEP between January 2021 and August 2023 by a single surgeon. Preoperative, intraoperative, and postoperative characteristics and outcomes were analyzed for all patients who had a documented preoperative prostate MRI. There were 334 patients without a pre-existing diagnosis of PCa and with a preoperative prostate MRI, of which 140 (42%) had at least one PIRADS lesion. There was a total of 203 PIRADS lesions: 91 (45%) in the peripheral zone (PZ), 106 (52%) in the transition zone (TZ), and 6 (2%) not specified. Incidental PCa was noted in 44 (13%) patients at time of HoLEP. Presence or location of lesion was not significantly associated with rate or grade of incidental PCa on pathology. Greater number of lesions and lesion size correlated with longer procedure times. Lesion number, size, or grade were not found to correlate with cancer grade or rate of cancer. Grade, presence, location, size, and number of PIRADS lesions on preoperative prostate MRI for patients with an appropriate prior PCa workup were not significantly associated with incidental PCa or higher PCa grade on HoLEP pathology.

Evaluation of Artificial Intelligence-Based Gleason Grading Algorithms “in the Wild”

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.

External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.

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.

Robust, credible, and interpretable AI-based histopathological prostate cancer grading.

Prostate cancer (PCa) is among the most common cancers in men and its diagnosis requires the histopathological evaluation of biopsies by human experts. While several recent artificial intelligence-based (AI) approaches have reached human expert-level PCa grading, they often display significantly reduced performance on external datasets. This reduced performance can be caused by variations in sample preparation, for instance the staining protocol, section thickness, or scanner used. Another limiting factor of contemporary AI-based PCa grading is the prediction of ISUP grades, which leads to the perpetuation of human annotation errors. We developed the prostate cancer aggressiveness index (PCAI), an AI-based PCa detection and grading framework that is trained on objective patient outcome, rather than subjective ISUP grades. We designed PCAI as a clinical application, containing algorithmic modules that offer robustness to data variation, medical interpretability, and a measure of prediction confidence. To train and evaluate PCAI, we generated a multicentric, retrospective, observational trial consisting of six cohorts with 25,591 patients, 83,864 images, and 5 years of median follow-up from 5 different centers and 3 countries. This includes a high-variance dataset of 8,157 patients and 28,236 images with variations in sample thickness, staining protocol, and scanner, allowing for the systematic evaluation and optimization of model robustness to data variation. The performance of PCAI was assessed on three external test cohorts from two countries, comprising 2,255 patients and 9,437 images. Using our high-variance datasets, we show how differences in sample processing, particularly slide thickness and staining time, significantly reduce the performance of AI-based PCa grading by up to 6.2 percentage points in the concordance index (C-index). We show how a select set of algorithmic improvements, including domain adversarial training, conferred robustness to data variation, interpretability, and a measure of credibility to PCAI. These changes lead to significant prediction improvement across two biopsy cohorts and one TMA cohort, systematically exceeding expert ISUP grading in C-index and AUROC by up to 22 percentage points. Data variation poses serious risks for AI-based histopathological PCa grading, even when models are trained on large datasets. Algorithmic improvements for model robustness, interpretability, credibility, and training on high-variance data as well as outcome-based severity prediction gives rise to robust models with above ISUP-level PCa grading performance.

Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis

Background: The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis. Materials and Methods: This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles. Results: Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model’s curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%–91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%–97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%–91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%–90.91%], perfusion-related diffusion [D*] 65% [48.47%–81.53%] and perfusion fraction [f] 80.36% [64.23%–96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92–0.97), for DKI parameters (DK 0.94 [0.89–0.99] and K 0.93 [0.90–0.96]) and for IVIM parameters (DIVIM 0.85 [0.80–0.91], D* 0.60 [0.43–0.77] and f 0.73 [0.63–0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM–kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%–93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%–79.48%), respectively. Conclusion: Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.

Prostate Cancer Among Black Men in Canada

Prostate cancer is a prevalent disease among men worldwide, exhibiting substantial heterogeneity in presentation and outcomes influenced by various factors, including race and ethnicity. Disparities in incidence, stage at diagnosis, and survival rates have been observed between Black men and those of other races and ethnicities. To compare prostate cancer outcomes between Black men and men with other race (Asian, Hispanic, Indigenous, Middle Eastern, White, Multiracial, and Other) in a universal health care system, with race and ethnicity self-reported. This was a prospective, observational cohort study of men diagnosed with prostate cancer between June 1, 2014, and August 28, 2023, who self-identified race and ethnicity. Participants included men who had been prospectively enrolled in the Alberta Prostate Cancer Research Initiative from the 2 major urology referral centers in Alberta (University of Alberta and University of Calgary). All men with prostate cancer enrolled in the initiative were included. Race and ethnicity. The primary outcome was the stage and grade of prostate cancer at diagnosis. Further outcomes included age and prostate-specific antigen level at diagnosis, initial treatment modality, time from diagnosis to initial treatment, and prostate cancer-specific, metastasis-free, and overall survivals. A total of 6534 men were included; 177 (2.7%) were Black, and 6357 (97.3%) had another race or ethnicity. Men who identified as Black were diagnosed with prostate cancer at an earlier age (mean [SD], 62.0 [8.2] compared with 64.6 [7.7] years; P < .001) and had a lower Charlson Comorbidity Index rating (14% compared with 7% ≤ 1; P < .001) compared with men of other races. Men who identified as Black had similar prostate-specific antigen levels at diagnosis, TNM category (74% vs 74% with T1-T2; P = .83) and Gleason Grade Group (34% compared with 35% Gleason Grade Group 1; P = .63). Black men had similar rates of prostate cancer-specific (hazard ratio [HR], 1.10; 95% CI, 0.41-2.97; P = .85), metastasis-free (HR, 0.88; 95% CI, 0.42-1.46; P = .44), and overall (HR, 0.55; 95% CI, 0.25-1.24; P = .15) survival. The findings of this cohort study suggest that Black men, despite being diagnosed at a younger age, experience comparable prostate cancer outcomes compared with men of other races.

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.3, P = .04), whereas upgrading of the initial biopsy by contemporary uropathologist reviews was not associated with this outcome. In a contemporary validation cohort that underwent prostate magnetic resonance imaging before initial biopsy (n = 169), upgrading of the initial biopsy (all contemporary GG1 by uropathologist grading) by AIRAProstate was associated with grade reclassification on AS (hazard ratio = 1.7, P = .03). These results demonstrate the utility of a DL-based grading algorithm in PCa risk stratification for AS.

Multi-Scale Digital Pathology Patch-Level Prostate Cancer Grading Using Deep Learning: Use Case Evaluation of DiagSet Dataset.

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care.

Differential Diagnosis of Prostate Cancer Grade to Augment Clinical Diagnosis Based on Classifier Models with Tuned Hyperparameters.

We developed a novel machine-learning algorithm to augment the clinical diagnosis of prostate cancer utilizing first and second-order texture analysis metrics in a novel application of machine-learning radiomics analysis. We successfully discriminated between significant prostate cancers versus non-tumor regions and provided accurate prediction between Gleason score cohorts with statistical sensitivity of 0.82, 0.81 and 0.91 in three separate pathology classifications. Tumor heterogeneity and prediction of the Gleason score were quantified using two feature selection approaches and two separate classifiers with tuned hyperparameters. There was a total of 71 patients analyzed in this study. Multiparametric MRI, incorporating T2WI and ADC maps, were used to derive radiomics features. Recursive feature elimination (RFE), the least absolute shrinkage and selection operator (LASSO), and two classification approaches, incorporating a support vector machine (SVM) (with randomized search) and random forest (RF) (with grid search), were utilized to differentiate between non-tumor regions and significant cancer while also predicting the Gleason score. In T2WI images, the RFE feature selection approach combined with RF and SVM classifiers outperformed LASSO with SVM and RF classifiers. The best performance was achieved by combining LASSO and SVM into a model that used both T2WI and ADC images. This model had an area under the curve (AUC) of 0.91. Radiomic features computed from ADC and T2WI images were used to predict three groups of Gleason score using two kinds of feature selection methods (RFE and LASSO), RF and SVM classifier models with tuned hyperparameters. Using combined sequences (T2WI and ADC map images) and combined radiomics (1st and GLCM features), LASSO, with a feature selection method with RF, was able to predict G3 with the highest sensitivity at a level AUC of 0.92. To predict G3 for single sequence (T2WI images) using GLCM features, LASSO with SVM achieved the highest sensitivity with an AUC of 0.92.

Proposal for an optimised definition of adverse pathology (unfavourable histology) that predicts metastatic risk in prostatic adenocarcinoma independent of grade group and pathological stage.

Histological grading of prostate cancer is a powerful prognostic tool, but current criteria for grade assignment are not fully optimised. Our goal was to develop and test a simplified histological grading model, based heavily on large cribriform/intraductal carcinoma, with optimised sensitivity for predicting metastatic potential. Two separate non-overlapping cohorts were identified: a 419-patient post-radical prostatectomy cohort with long term clinical follow-up and a 209-patient post-radical prostatectomy cohort in which all patients had pathologically confirmed metastatic disease. All prostatectomies were re-reviewed for high-risk histological patterns of carcinoma termed 'unfavourable histology'. Unfavourable histology is defined by any classic Gleason pattern 5 component, any large cribriform morphology (> 0.25 mm) or intraductal carcinoma, complex intraluminal papillary architecture, grade 3 stromogenic carcinoma and complex anastomosing cord-like growth. For the outcome cohort, Kaplan-Meier analysis compared biochemical recurrence, metastasis and death between subjects with favourable and unfavourable histology, stratified by pathological stage and grade group. Multivariable Cox proportional hazards models evaluated adding unfavourable histology to the Memorial Sloan Kettering Cancer Center (MSKCC) post-prostatectomy nomogram and stratification by percentage of unfavourable histology. At 15 years unfavourable histology predicted biochemical recurrence, with sensitivity of 93% and specificity of 88%, metastatic disease at 100 and 48% and death at 100 and 46%. Grade group 2 prostate cancers with unfavourable histology were associated with metastasis independent of pathological stage, while those without had no risk. Histological models for prediction of metastasis based on only large cribriform/intraductal carcinoma or increasing diameter of cribriform size improved specificity, but with lower sensitivity. Multivariable Cox proportional hazards models demonstrated that unfavourable histology significantly improved discriminatory power of the MSKCC post-prostatectomy nomogram for biochemical failure (likelihood ratio test P < 0.001). In the retrospective review of a separate RP cohort in which all patients had confirmed metastatic disease, none had unequivocal favourable histology. Unfavourable histology at radical prostatectomy is associated with metastatic risk, predicted adverse outcomes better than current grading and staging systems and improved the MSKCC post-prostatectomy nomogram. Most importantly, unfavourable histology stratified grade group 2 prostate cancers into those with and without metastatic potential, independent of stage. While unfavourable histology is driven predominantly by large cribriform/intraductal carcinoma, the recognition and inclusion of other specific architectural patterns add to the sensitivity for predicting metastatic disease. Moreover, a simplified dichotomous model improves communication and could increase implementation.

Multinational, Multicenter Evaluation of Prostate Cancer Tissue in Sub-Saharan Africa: Challenges and Opportunities.

Prostate cancer disproportionately affects men of African descent, yet their representation in tissue-based studies is limited. This multinational, multicenter pilot study aims to establish the groundwork for collaborative research on prostate cancer in sub-Saharan Africa. The Men of African Descent and Carcinoma of the Prostate network formed a pathologist working group representing eight institutions in five African countries. Formalin-fixed paraffin-embedded prostate tissue specimens were collected from Senegal, Nigeria, and Ghana. Histology slides were produced and digitally scanned. A central genitourinary pathologist (P.L.) and eight African general pathologists reviewed anonymized digital whole-slide images for International Society of Urological Pathology grade groups and other pathologic parameters. Discrepancies were re-evaluated, and consensus grading was assigned. A virtual training seminar on prostate cancer grading was followed by a second assessment on a subcohort of the same tissue set. Of 134 tissue blocks, 133 had evaluable tissue; 13 lacked cancer evidence, and four were of insufficient quality. Post-training, interobserver agreement for grade groups improved to 56%, with a median Cohen's quadratic weighted kappa of 0.83 (mean, 0.74), compared with an initial 46% agreement and a quadratic weighted kappa of 0.77. Interobserver agreement between African pathologist groups was 40%, with a quadratic weighted kappa of 0.66 (95% CI, 0.51 to 0.76). African pathologists tended to overgrade (36%) more frequently than undergrade (18%) compared with the reference genitourinary pathologist. Interobserver variability tended to worsen with a decrease in tissue quality. Tissue-based studies on prostate cancer in men of African descent are essential for a better understanding of this common disease. Standardized tissue handling protocols are crucial to ensure good tissue quality and data. The use of digital slide imaging can enhance collaboration among pathologists in multinational, multicenter studies.