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

Prognostic Testing for Prostate Cancer-A Cost-Effectiveness Analysis Comparing a Prostatype P-Score Biomarker Approach to Standard Clinical Practice

BACKGROUND

The Prostatype score (P-score) is a prognostic biomarker that integrates a three-gene (IGFBP3, F3, and VGLL3) signature derived from prostate biopsy samples, with key clinical parameters, including prostate-specific antigen (PSA) levels, Gleason grade, and tumor stage at diagnosis. The test has demonstrated superior predictive accuracy for prostate cancer outcomes compared with traditional risk categorization systems such as D'Amico. Notably, it reclassifies a higher proportion of patients into the low-risk category, making them eligible for active surveillance. This study assessed the cost-effectiveness of the P-score in comparison with D'Amico and the Swedish National Prostate Cancer Register (NPCR) risk categorization systems.

METHODS

A two-step decision analytic model was developed. The model consisted of a decision tree-informed Markov structure estimating the lifetime outcomes of 60-year-old men with diagnosed prostate cancer. Prostate cancer was classified as low-risk, intermediate-risk, or high-risk using either the P-score or D'Amico. Initial therapy was based on observed treatment patterns from the Swedish NPCR. Costs (SEK, year 2022) and quality-adjusted life years (QALYs) were estimated from a healthcare perspective and discounted at 3% per year; incremental cost-effectiveness ratio (ICER) was the primary outcome.

RESULTS

The P-score led to cost savings and generated an additional 0.19 QALYs compared with D'Amico. The added costs of the genetic test and higher costs of active surveillance and radiotherapy were counterbalanced by savings from reduced costs of surgery, treatment-related side-effects, and metastatic disease. The gain in QALYs was primarily due to the avoidance of metastatic disease and a reduction in treatment-related side-effects.

CONCLUSIONS

The results of this study suggest that the P-score is likely to be a cost-effective alternative to D'Amico for prognostic evaluation of newly diagnosed prostate cancer in Sweden and compared with NPCR when health-related quality of life was included.

Bi-parametric MRI-based quantification radiomics model for the noninvasive prediction of histopathology and biochemical recurrence after prostate cancer surgery: a multicenter study

RATIONALE AND OBJECTIVES

To develop and evaluate the performance of a noninvasive radiomics combined model based on preoperative bi-parametric MRI to assess biochemical recurrence (BCR) risk factors and to predict biochemical recurrence free survival in PCa patients.

MATERIALS AND METHODS

Pretreatment bp-MRI and clinicopathology data of 666 (discovery cohort, 545; test cohort, 121) PCa patients from four centers between January 2015 to March 2023 were retrospectively included. To predict BCR, extracapsular extension (ECE), pelvic lymph node metastasis (PLNM), and Gleason Grade group (GG), the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were developed, respectively. Subsequently, a logistic regression algorithm was used to combine one or more radiomics models and clinicopathology variables into radiomics-clinicopathology combined models (M1, M2) and radiomics-clinical combined model without pathology results (M3) for predicting BCR.

RESULTS

In the test cohort, the AUCs for the pred-BCR, pred-ECE, pred-PLNM, and pred-GG models were 0.841, 0.764, 0.896, and 0.698. Of the three combined models, M3 has the best prediction performance with an AUC of 0.884, M2 is the following with an AUC of 0.863, and M1 has the lowest performance with an AUC of 0.838 (95% CI 0.750-0.925) in the test cohort. Delong's test showed that the M3 was significantly higher (M1 vs. M3, p = 0.028; M2 vs. M3, p = 0.044).

CONCLUSION

The combined model developed in this study, which is not dependent on pathologic biopsies, can noninvasively predict postoperative histopathology and BCR after PCa, therefore may provide decision support for follow-up and treatment strategies for patients in the postoperative period.

Optimizing risk stratification for intermediate-risk prostate cancer - the prognostic value of baseline health-related quality of life

OBJECTIVE

To investigate the prognostic value of baseline health-related quality of life (HRQOL) for patients with intermediate-risk localized prostate cancer (IR-PCa) undergoing radical prostatectomy (RP).

METHODS

4780 patients with IR-PCa according to NCCN risk stratification were identified from a prospectively maintained database. All patients were treated with RP and had prospectively assessed baseline HRQOL. Main outcomes were oncologic endpoints metastasis-free survival (MFS); biochemical recurrence free survival (BRFS) and overall survival (OS). Multivariable Cox regression models assessed prognostic significance of baseline global health status (GHS) on survival outcomes. Harrell's discrimination C-index was applied to calculate the predictive accuracy of the model. Decision curve analysis (DCA) tested the clinical net benefit associated with adding the GHS domain to our multivariable model (p < 0.05).

RESULTS

Median follow-up was 51 months. Multivariable analysis confirmed baseline GHS as an independent predictor for increased MFS (HR 0.976, 95%CI 0.96-0.99; p < 0.001), increased BRFS (HR 0.993, 95%CI 0.99-1.00; p = 0.027) and increased OS (HR 0.969, 95%CI 0.95-0.99; p = 0.002), indicating a relative risk reduction of 2.4% for MFS, 0.7% for BRFS and 3.1% for OS per 1-point increase of baseline GHS. Baseline HRQOL improved discrimination in predicting MFS, BRFS and OS. DCA revealed a net benefit over all threshold probabilities.

CONCLUSIONS

We found baseline HRQOL to substantially improve risk stratification for the heterogeneous cohort of IR-PCa. Baseline HRQOL accurately predicts increased MFS, BRFS and OS. Our findings therefore support the role of preoperative HRQOL as an adjunct to established prognosticators for IR-PCa, potentially facilitating guidance of therapy.

Investigation of association between clinically significant prostate cancer, obesity and platelet to-lymphocyte ratio and neutrophil -to-lymphocyte ratio

INTRODUCTION

Several blood markers of inflammation are elevated in prostate cancer (PCa) and have prognostic value. Little is known about the relationship between these markers, PCa, and other factors associated with chronic inflammation, such as smoking and obesity. We analyzed the interaction between neutrophil and platelet counts indexed to lymphocyte count (NLR and PLR, resp.) and clinically significant PCa (csPCa), accounting for the potential confounding factors of systemic inflammation.

METHODS

NLR and PLR were evaluated in a multicenter prospective study in 443 patients. CsPCa was defined as a Gleason ≥ 4 + 3. Differences between patients with csPCa and non-csPCA were evaluated using the chi-square test, analysis of variance or the Kruskal-Wallis test. Multivariable logistic regression analysis adjusted for smoking, hypertension, diabetes, and cardiovascular disease, and in separate models, either body mass index or waist-to-hip ratio was used to characterize the relationship between inflammation and csPCa.

RESULTS

None of the factors such as plateletcrit, NLR, and PLR were significantly different between patients with csPCa or non-significant PCa. After adjustment, there was no association between PLR, NLR, plateletcrit or platelet count and csPCa. In an exploratory analysis, there was no association between markers of inflammation and PSA levels > 10 ng/mL. When testing different NLR cutoffs to predict csPCa in ROC analysis, none reached a clinically meaningful value.

CONCLUSION

In contrast to previous studies, we found no significant association between easily available blood markers of inflammation and indices of PCa aggressiveness. Further research is required to determine whether inflammation promotes PCa. (ClinicalTrials.gov: NCT03127631. Date of registration: April 25, 2017.

Direct three-dimensional segmentation of prostate glands with nnU-Net

Significance

In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance.

Aim

To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E).

Approach

For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue.

Results

nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs.

Conclusions

Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Development and validation of a nomogram to predict cancer-specific survival in nonsurgically treated elderly patients with prostate cancer

Prostate Cancer (PC) is the most common male nonskin tumour in the world, and most diagnosed patients are over 65 years old. The main treatment for PC includes surgical treatment and nonsurgical treatment. Currently, for nonsurgically treated elderly patients, few studies have evaluated their prognostic factors. Our aim was to construct a nomogram that could predict cancer-specific survival (CSS) in nonsurgically treated elderly PC patients to assess their prognosis-related independent risk factors. Patient information was obtained from the Surveillance, Epidemiology and End Results (SEER) database, and our target population was nonsurgically treated PC patients who were over 65 years old. Independent risk factors were determined using both univariate and multivariate Cox regression models. A nomogram was built using a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested using the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to examine the potential clinical value of this model. A total of 87,831 elderly PC patients with nonsurgical treatment in 2010-2018 were included in the study and were randomly assigned to the training set (N = 61,595) and the validation set (N = 26,236). Univariate and multivariate Cox regression model analyses showed that age, race, marital status, TNM stage, chemotherapy, radiotherapy modality, PSA and GS were independent risk factors for predicting CSS in nonsurgically treated elderly PC patients. The C-index of the training set and the validation set was 0.894 (95% CI 0.888-0.900) and 0.897 (95% CI 0.887-0.907), respectively, indicating the good discrimination ability of the nomogram. The AUC and the calibration curves also show good accuracy and discriminability. We developed a new nomogram to predict CSS in elderly PC patients with nonsurgical treatment. The model is internally validated with good accuracy and reliability, as well as potential clinical value, and can be used for clinical aid in decision-making.

Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.

New insights into molecular signaling pathways and current advancements in prostate cancer diagnostics & therapeutics

Prostate adenocarcinoma accounts for more than 20% of deaths among males due to cancer. It is the fifth-leading cancer diagnosed in males across the globe. The mortality rate is quite high due to prostate cancer. Despite the fact that advancements in diagnostics and therapeutics have been made, there is a lack of effective drugs. Metabolic pathways are altered due to the triggering of androgen receptor (AR) signaling pathways, and elevated levels of dihydrotestosterone are produced due to defects in AR signaling that accelerate the growth of prostate cancer cells. Further, PI3K/AKT/mTOR pathways interact with AR signaling pathway and act as precursors to promote prostate cancer. Prostate cancer therapy has been classified into luminal A, luminal B, and basal subtypes. Therapeutic drugs inhibiting dihydrotestosterone and PI3K have shown to give promising results to combat prostate cancer. Many second-generation Androgen receptor signaling antagonists are given either as single agent or with the combination of other drugs. In order to develop a cure for metastasized prostate cancer cells, Androgen deprivation therapy (ADT) is applied by using surgical or chemical methods. In many cases, Prostatectomy or local radiotherapy are used to control metastasized prostate cancer. However, it has been observed that after 1.5 years to 2 years of Prostatectomy or castration, there is reoccurrence of prostate cancer and high incidence of castration resistant prostate cancer is seen in population undergone ADT. It has been observed that Androgen derivation therapy combined with drugs like abiraterone acetate or docetaxel improve overall survival rate in metastatic hormone sensitive prostate cancer (mHSPC) patients. Scientific investigations have revealed that drugs inhibiting poly ADP Ribose polymerase (PARP) are showing promising results in clinical trials in the prostate cancer population with mCRPC and DNA repair abnormalities. Recently, RISUG adv (reversible inhibition of sperm under guidance) has shown significant results against prostate cancer cell lines and MTT assay has validated substantial effects of this drug against PC3 cell lines. Current review paper highlights the advancements in prostate cancer therapeutics and new drug molecules against prostate cancer. It will provide detailed insights on the signaling pathways which need to be targeted to combat metastasized prostate cancer and castration resistant prostate cancer.

Chronic hypoxia favours adoption to a castration-resistant cell state in prostate cancer

Predicting and treating recurrence in intermediate-risk prostate cancer patients remains a challenge despite having identified genomic instability [1] and hypoxia [2, 3] as risk factors. This underlies challenges in assigning the functional impact of these risk factors to mechanisms promoting prostate cancer progression. Here we show chronic hypoxia (CH), as observed in prostate tumours [4], leads to the adoption of an androgen-independent state in prostate cancer cells. Specifically, CH results in prostate cancer cells adopting transcriptional and metabolic alterations typical of castration-resistant prostate cancer cells. These changes include the increased expression of transmembrane transporters for the methionine cycle and related pathways leading to increased abundance of metabolites and expression of enzymes related to glycolysis. Targeting of the Glucose Transporter 1 (GLUT1) identified a dependency on glycolysis in androgen-independent cells. Overall, we identified a therapeutically targetable weakness in chronic hypoxia and androgen-independent prostate cancer. These findings may offer additional strategies for treatment development against hypoxic prostate cancer.

Convolutional Neural Network Quantification of Gleason Pattern 4 and Association with Biochemical Recurrence in Intermediate Grade Prostate Tumors

Differential classification of prostate cancer (CaP) grade group (GG) 2 and 3 tumors remains challenging, likely due to the subjective quantification of percentage of Gleason pattern 4 (%GP4). Artificial intelligence assessment of %GP4 may improve its accuracy and reproducibility and provide information for prognosis prediction. To investigate this potential, a convolutional neural network (CNN) model was trained to objectively identify and quantify Gleason pattern (GP) 3 and 4 areas, estimate %GP4, and assess whether CNN-assessed %GP4 is associated with biochemical recurrence (BCR) risk in intermediate risk GG 2 and 3 tumors. The study was conducted in a radical prostatectomy cohort (1999-2012) of African American men from the Henry Ford Health System (Detroit, Michigan). A CNN model that could discriminate four tissue types (stroma, benign glands, GP3 glands, and GP4 glands) was developed using histopathologic images containing GG 1 (n=45) and 4 (n=20) tumor foci. The CNN model was applied to GG 2 (n=153) and 3 (n=62) for %GP4 estimation, and Cox proportional hazard modeling was used to assess the association of %GP4 and BCR, accounting for other clinicopathologic features including GG. The CNN model achieved an overall accuracy of 86% in distinguishing the four tissue types. Further, CNN-assessed %GP4 was significantly higher in GG 3 compared with GG 2 tumors (p=7.2*10^-11). %GP4 was associated with an increased risk of BCR (adjusted HR=1.09 per 10% increase in %GP4, p=0.010) in GG 2 and 3 tumors. Within GG 2 tumors specifically, %GP4 was more strongly associated with BCR (adjusted HR=1.12, p=0.006). Our findings demonstrate the feasibility of CNN-assessed %GP4 estimation, which is associated with BCR risk. This objective approach could be added to the standard pathological assessment for patients with GG 2 and 3 tumors and act as a surrogate for specialist genitourinary pathologist evaluation when such consultation is not available.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Improving the stratification of intermediate risk prostate cancer

BACKGROUND

Intermediate risk prostate cancer (IR PCa) may exhibit a wide array of phenotypes, from favorable to unfavorable. NCCN criteria help distinguishing between favorable versus unfavorable subgroups. We studied and attempted to improve this classification.

METHODS

Within the SEER database 2010-2016, we identified 19,193 IR PCa patients treated with radical prostatectomy. A multivariable logistic regression model predicting unfavorable IR PCa was developed and externally validated, in addition to a head-to-head comparison with NCCN IR PCa stratification.

RESULTS

Model development (development cohort N.=13,436: 3585 unfavorable versus 9851 favorable) rested on age, PSA, clinical T stage, biopsy Gleason Grade Group (GGG) and percentage of positive cores. All were independent predictors of unfavorable IR PCa. In external validation cohort (N.=5757: 1652 unfavorable versus 4105 favorable), NCCN stratification was 61.8% accurate in discriminating between favorable versus unfavorable, compared to 67.6% for nomogram, which exhibited excellent calibration, less pronounced departures from ideal prediction and greater net-benefit in decision curve analyses (DCA) than NCCN stratification. The optimal nomogram cutoff misclassified 312 of 1976 patients (15.8%) versus 598 of 2877 (20.8%) for NCCN stratification. Of NCCN misclassified patients, 90.0% harbored pT3-4 stages versus 84.6% of nomogram.

CONCLUSIONS

The newly developed, externally validated nomogram discriminates better between favorable versus unfavorable IR PCa, according to overall accuracy, calibration, DCA, and actual numbers and stage distribution of misclassified patients.

Active Surveillance in Intermediate-Risk Prostate Cancer: A Review of the Current Data

Simple Summary

AS is an option for the initial management of selected patients with intermediate-risk PC. The proper way to predict which men will have an aggressive clinical course or indolent PC who would benefit from AS has not been unveiled. Genetics and MRI can help in the decision-making, but it remains unclear which men would benefit from which tests. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. Large series and a few RCTs are under investigation, and more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk PC. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Abstract

Active surveillance (AS) is a monitoring strategy to avoid or defer curative treatment, minimizing the side effects of radiotherapy and prostatectomy without compromising survival. AS in intermediate-risk prostate cancer (PC) has increasingly become used. There is heterogeneity in intermediate-risk PC patients. Some of them have an aggressive clinical course and require active treatment, while others have indolent disease and may benefit from AS. However, intermediate-risk patients have an increased risk of metastasis, and the proper way to select the best candidates for AS is unknown. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. A few large series and randomized trials are under investigation. Therefore, more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk disease. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Overexpression of Nucleolin and Associated Genes in Prostate Cancer

Prostate cancer (PCa) is the second most frequent cancer and the fifth leading cause of cancer death in men worldwide. If local PCa presents a favorable prognosis, available treatments for advanced PCa display limiting benefits due to therapeutic resistances. Nucleolin (NCL) is a ubiquitous protein involved in numerous cell processes, such as ribosome biogenesis, cell cycles, or angiogenesis. NCL is overexpressed in several tumor types in which it has been proposed as a diagnostic and prognostic biomarker. In PCa, NCL has mainly been studied as a target for new therapeutic agents. Nevertheless, little data are available concerning its expression in patient tissues. Here, we investigated the expression of NCL using a new cohort from Mondor Hospital and data from published cohorts. Results were then compared with NCL expression using in vitro models. NCL was overexpressed in PCa tissues compared to the normal tissues, but no prognostic values were demonstrated. Nine genes were highly co-expressed with NCL in patient tissues and tumor prostate cell lines. Our data demonstrate that NCL is an interesting diagnostic biomarker and propose a signature of genes co-expressed with NCL.

Effect of Clinical Parameters on Risk of Death from Cancer after Radical Prostatectomy in Men with Localized and Locally Advanced Prostate Cancer

Background: The study aimed to assess predictors and to identify patients at increased risk of prostate-cancer-specific mortality (CSM) after radical prostatectomy (RP). Methods: A total of 2421 men with localized and locally advanced PCa who underwent RP in 2001−2017 were included in the study. CSM predictors were assessed using multivariate competing risk analysis. Death from other causes was considered a competing event. Cumulative CSM and other-cause mortality (OCM) were calculated in various combinations of predictors. Results: During the median 8 years (interquartile range 4.4−11.7) follow-up, 56 (2.3%) of registered deaths were due to PCa. Cumulative 10 years CSM and OCM was 3.6% (95% CI 2.7−4.7) and 15.9% (95% CI 14.2−17.9), respectively. The strongest predictors of CSM were Grade Group 5 (GG5) (hazard ratio (HR) 19.9, p < 0.0001), lymph node invasion (HR 3.4, p = 0.001), stage pT3b-4 (HR 3.1, p = 0.009), and age (HR 1.1, p = 0.0007). In groups created regarding age, stage, and GG, cumulative 10 years CSM ranged from 0.4−84.9%, whereas OCM varied from 0−43.2%. Conclusions: CSM after RP is related to GGs, pathological stage, age, and combinations of these factors, whereas other-cause mortality is only associated with age. Created CSM and OCM plots can help clinicians identify patients with the most aggressive PCa who could benefit from more intensive or novel multimodal treatment strategies.

MRI-detectability of clinically significant prostate cancer relates to oncologic outcomes after prostatectomy

INTRODUCTION/BACKGROUND

Magnetic resonance imaging (MRI) misses a proportion of "clinically significant" prostate cancers (csPC) as defined by histopathology criteria. The aim of this study was to analyze whether long-term oncologic outcomes differ between MRI-detectable and MRI-occult csPC.

PATIENTS AND METHODS

Retrospective analysis of 1449 patients with pre-prostatectomy MRI and csPC on prostatectomy specimens (ie, Grade group ≥2 or extraprostatic spread) between 2001-2006. T2-weighted MRIs were classified according to the Prostate Imaging Reporting and Data System into MRI-occult (categories 1, 2), MRI-equivocal (category 3), and MRI-detectable (categories 4, 5). Cumulative incidence of biochemical recurrence (BCR), metastatic disease, and cancer-specific mortality, estimated with competing risk models. The median follow-up in survivors was 11.0 years (IQR: 8.9-13.1).

RESULTS

In 188 (13%) cases, csPC was MRI-occult, 435 (30%) MRIs were equivocal, and 826 (57%) csPC were MRI-detectable. The 15-year cumulative incidence [95% CI] of BCR was 8.3% [2.2, 19.5] for MRI-occult cases, 17.4% [11.1, 24.8] for MRI-equivocal cases, and 43.3% [38.7, 47.8] for MRI-detectable cases (P < .001). The cumulative incidences of metastases were 0.61% [0.06, 3.1], 3.5% [1.5, 6.9], and 19.6% [15.4, 24.2] for MRI-occult, MRI-equivocal, and MRI-detectable cases, respectively (P < .001). There were no deaths from prostate cancer observed in patients with MRI-occult csPC, compared to an estimated 1.9% [0.54, 4.9], and 7.1 % [4.5, 10.6] for patients with MRI-equivocal and MRI-detectable cancer, respectively (P < .001).

CONCLUSION

Oncologic outcomes after prostatectomy for csPC differ between MRI-occult and MRI-detectable lesions. Judging the clinical significance of a negative prostate MRI based on histopathologic surrogates alone might be misleading.

MICROABSTRACT

Among 1449 patients with pre-prostatectomy MRI and clinically significant prostate cancer on prostatectomy histopathology, MRI-occult cancers (n = 188, 13%) were less likely to recur biochemically (8% vs. 43%, P < .001), metastasize (0.6% vs. 20%, P < .001), or lead to prostate cancer mortality (0% vs. 7%, P < .001) than MRI-detectable cancers (n = 826, 57%). MRI-occult cancers constitute a prognostically distinct subgroup among higher-grade prostate cancers.

MiR-378a inhibits glucose metabolism by suppressing GLUT1 in prostate cancer

Prostate cancer (PCa) is the fifth leading cause of cancer related deaths worldwide, in part due to a lack of molecular stratification tools that can distinguish primary tumours that will remain indolent from those that will metastasise. Amongst potential molecular biomarkers, microRNAs (miRs) have attracted particular interest because of their high stability in body fluids and fixed tissues. These small non-coding RNAs modulate several physiological and pathological processes, including cancer progression. Herein we explore the prognostic potential and the functional role of miRs in localised PCa and their relation to nodal metastasis. We define a 7-miR signature that is associated with poor survival independently of age, Gleason score, pathological T state, N stage and surgical margin status and that is also prognostic for disease-free survival in patients with intermediate-risk localised disease. Within our 7-miR signature, we show that miR-378a-3p (hereafter miR-378a) levels are low in primary tumours compared to benign prostate tissue, and also lower in Gleason score 8-9 compared to Gleason 6-7 PCa. We demonstrate that miR-378a impairs glucose metabolism and reduces proliferation in PCa cells through independent mechanisms, and we identify glucose transporter 1 (GLUT1) messenger RNA as a direct target of miR-378a. We show that GLUT1 inhibition hampers glycolysis, leading to cell death. Our data provides a rational for a new PCa stratification strategy based on miR expression, and it reveals that miR-378a and GLUT1 are potential therapeutic targets in highly aggressive glycolytic PCa.

Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning-Assisted Gland Analysis

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted three-dimensional (3D) glandular structures via visual inspection of a limited number of two-dimensional (2D) histology sections is often unreliable, which contributes to the under- and overtreatment of patients. To improve risk assessment and treatment decisions, we have developed a workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analogue of standard hematoxylin and eosin (H&E) staining. This analysis is based on interpretable glandular features and is facilitated by the development of image translation-assisted segmentation in 3D (ITAS3D). ITAS3D is a generalizable deep learning-based strategy that enables tissue microstructures to be volumetrically segmented in an annotation-free and objective (biomarker-based) manner without requiring immunolabeling. As a preliminary demonstration of the translational value of a computational 3D versus a computational 2D pathology approach, we imaged 300 ex vivo biopsies extracted from 50 archived radical prostatectomy specimens, of which, 118 biopsies contained cancer. The 3D glandular features in cancer biopsies were superior to corresponding 2D features for risk stratification of patients with low- to intermediate-risk prostate cancer based on their clinical biochemical recurrence outcomes. The results of this study support the use of computational 3D pathology for guiding the clinical management of prostate cancer. SIGNIFICANCE: An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of patients with prostate cancer.

Hidden clues in prostate cancer - Lessons learned from clinical and pre-clinical approaches on diagnosis and risk stratification

The heterogeneity of prostate cancer is evident at clinical, morphological and molecular levels. To aid clinical decision making, a three-tiered system for risk stratification is used to designate low-, intermediate-, and high-risk of disease progression. Intermediate-risk prostate cancers are the most frequently diagnosed, and even with common diagnostic features, can exhibit vastly different clinical progression. Thus, improved risk stratification methods are needed to better predict patient outcomes. Here, we provide an overview of the improvements in diagnosis/prognosis arising from advances in pathology reporting of prostate cancer, which can improve risk stratification, especially for patients with intermediate-risk disease. This review discusses updates to pathology reporting of morphological growth patterns, and proposes the utility of integrating prognostic biomarkers or innovative imaging techniques to enhance clinical decision-making. To complement clinical studies, experimental approaches using patient-derived tumors have highlighted important cellular and morphological features associated with aggressive disease that may impact treatment response. The intersection of urology, pathology and scientific disciplines is required to work towards a common goal of understanding disease pathogenesis, improving the stratification of patients with intermediate-risk disease and subsequently defining optimal treatment strategies using precision-based approaches.

Up- and downgrading in single intermediate-risk positive biopsy core prostate cancer

Background

Up- and/or downgrading rates in single intermediate-risk positive biopsy core are unknown.

Methods

We identified single intermediate-risk (Gleason grade group (GGG) 2/GGG3) positive biopsy core prostate cancer patients (≤ cT2c and PSA ≤ 20 ng/mL) within the Surveillance, Epidemiology, and End Results (SEER) database (2010–2015). Subsequently, separate uni- and multivariable logistic regression models tested for independent predictors of up- and downgrading.

Results

Of 1,328 assessable patients with single core positive intermediate-risk prostate cancer at biopsy, 972 (73%) harbored GGG2 versus 356 (27%) harbored GGG3. Median PSA (5.5 vs 5.7; p = 0.3), median age (62 vs 63 years; p = 0.07) and cT1-stage (77 vs 75%; p = 0.3) did not differ between GGG2 and GGG3 patients. Of individuals with single GGG2 positive biopsy core, 191 (20%) showed downgrading to GGG1 versus 35 (4%) upgrading to GGG4 or GGG5 at RP. Of individuals with single GGG3 positive biopsy core, 36 (10%) showed downgrading to GGG1 versus 42 (12%) significant upgrading to GGG4 or GGG5 at RP. In multivariable logistic regression models, elevated PSA (10–20 ng/mL) was an independent predictor of upgrading to GGG4/GGG5 in single GGG3 positive biopsy core patients (OR:2.89; 95%-CI: 1.31–6.11; p = 0.007).

Conclusion

In single GGG2 positive biopsy core patients, downgrading was four times more often recorded compared to upgrading. Conversely, in single GGG3 positive biopsy core patients, up- and downgrading rates were comparable and should be expected in one out of ten patients.

Influence of Biopsy Gleason Score on the Risk of Lymph Node Invasion in Patients With Intermediate-Risk Prostate Cancer Undergoing Radical Prostatectomy

Objective: To analyze the influence of biopsy Gleason score on the risk for lymph node invasion (LNI) during pelvic lymph node dissection (PLND) in patients undergoing radical prostatectomy (RP) for intermediate-risk prostate cancer (PCa).

Materials and Methods: We retrospectively analyzed 684 patients, who underwent RP between 2014 and June 2020 due to PCa. Univariable and multivariable logistic regression, as well as binary regression tree models were used to assess the risk of positive LNI and evaluate the need of PLND in men with intermediate-risk PCa.

Results: Of the 672 eligible patients with RP, 80 (11.9%) men harbored low-risk, 32 (4.8%) intermediate-risk with international society of urologic pathologists grade (ISUP) 1 (IR-ISUP1), 215 (32.0%) intermediate-risk with ISUP 2 (IR-ISUP2), 99 (14.7%) intermediate-risk with ISUP 3 (IR-ISUP3), and 246 (36.6%) high-risk PCa. Proportions of LNI were 0, 3.1, 3.7, 5.1, and 24.0% for low-risk, IR-ISUP1, IR-ISUP 2, IR-ISUP-3, and high-risk PCa, respectively (p < 0.001). In multivariable analyses, after adjustment for patient and surgical characteristics, IR-ISUP1 [hazard ratio (HR) 0.10, p = 0.03], IR-ISUP2 (HR 0.09, p < 0.001), and IR-ISUP3 (HR 0.18, p < 0.001) were independent predictors for lower risk of LNI, compared with men with high-risk PCa disease.

Conclusions: The international society of urologic pathologists grade significantly influence the risk of LNI in patients with intermediate- risk PCa. The risk of LNI only exceeds 5% in men with IR-ISUP3 PCa. In consequence, the need for PLND in selected patients with IR-ISUP 1 or IR-ISUP2 PCa should be critically discussed.

The Urine Biomarker PUR-4 Is Positively Associated with the Amount of Gleason 4 in Human Prostate Cancers

The Prostate Urine Risk (PUR) biomarker is a four-group classifier for predicting outcome in patients prior to biopsy and for men on active surveillance. The four categories correspond to the probabilities of the presence of normal tissue (PUR-1), D’Amico low-risk (PUR-2), intermediate-risk (PUR-3), and high-risk (PUR-4) prostate cancer. In the current study we investigate how the PUR-4 status is linked to Gleason grade, prostate volume, and tumor volume as assessed from biopsy (n = 215) and prostatectomy (n = 9) samples. For biopsy data PUR-4 status alone was linked to Gleason Grade group (GG) (Spearman’s, ρ = 0.58, p < 0.001 trend). To assess the impact of tumor volume each GG was dichotomized into Small and Large volume cancers relative to median volume. For GG1 (Gleason Pattern 3 + 3) cancers volume had no impact on PUR-4 status. In contrast for GG2 (3 + 4) and GG3 (4 + 3) cancers PUR-4 levels increased in large volume cancers with statistical significance observed for GG2 (p = 0.005; Games-Howell). These data indicated that PUR-4 status is linked to the presence of Gleason Pattern 4. To test this observation tumor burden and Gleason Pattern were assessed in nine surgically removed and sectioned prostates allowing reconstruction of 3D maps. PUR-4 was not correlated with Gleason Pattern 3 amount, total tumor volume or prostate size. A strong correlation was observed between amount of Gleason Pattern 4 tumor and PUR-4 signature (r = 0.71, p = 0.034, Pearson’s). These observations shed light on the biological significance of the PUR biomarker and support its use as a non-invasive means of assessing the presence of clinically significant prostate cancer.

The impact of race/ethnicity on upstaging and/or upgrading rates among intermediate risk prostate cancer patients treated with radical prostatectomy

BACKGROUND

Race/ethnicity may predispose to less favorable prostate cancer characteristics in intermediate risk prostate cancer (IR PCa) patients. We tested this hypothesis in a subgroup of IR PCa patients treated with radical prostatectomy (RP).

METHODS

We relied on the Surveillance, Epidemiology and End Results 2004-2016. The effect of race/ethnicity was tested in univariable and multivariable logistic regression analyses predicting upstaging (pT3+/pN1) and/or upgrading (Gleason Grade Group [GGG] 4-5) at RP.

RESULTS

Of 20,391 IR PCa patients, 15,050 (73.8%) were Caucasian, 2857 (14.0%) African-American, 1632 (8.0%) Hispanic/Latino and 852 (4.2%) Asian. Asian patients exhibited highest age (64 year), highest PSA (6.8 ng/ml) and highest rate of GGG3 (31.9%). African-Americans exhibited the highest percentage of positive cores at biopsy (41.7%) and the highest proportion of NCCN unfavorable risk group membership (54.6%). Conversely, Caucasians exhibited the highest proportion of cT2 stage (35.6%). In univariable analyses, Hispanic/Latinos exhibited the highest rates of upstaging/upgrading among all race/ethnicities, in both favorable and unfavorable groups, followed by Asians, Caucasians and African-Americans in that order. In multivariable analyses, Hispanic/Latino race/ethnicity represented an independent predictor of higher upstaging and/or upgrading in favorable IR PCa (odds ratio [OR] 1.27, p < 0.01), while African-American race/ethnicity represented an independent predictor of lower upstaging and/or upgrading in unfavorable IR PCa (OR 0.79, p < 0.001).

CONCLUSION

Race/ethnicity predisposes to differences in clinical, as well as in pathological characteristics in IR PCa patients. Specifically, even after full statistical adjustment, Hispanic/Latinos are at higher and African-Americans are at lower risk of upstaging and/or upgrading.

The utility of magnetic resonance imaging in prostate cancer diagnosis in the Australian setting

Objectives

To investigate the utility of Magnetic Resonance Imaging (MRI) for prostate cancer diagnosis in the Australian setting.

Patients and methods

All consecutive men who underwent a prostate biopsy (transperineal or transrectal) at Royal Melbourne Hospital between July 2017 to June 2019 were included, totalling 332 patients. Data were retrospectively collected from patient records. For each individual patient, the risk of prostate cancer diagnosis at biopsy based on clinical findings was determined using the European Randomized study of Screening for Prostate Cancer (ERSPC) risk calculator, with and without incorporation of MRI findings.

Results

MRI has good diagnostic accuracy for clinically significant prostate cancer. A PI‐RADS 2 or lower finding has a negative predictive value of 96% for clinically significant cancer, and a PI‐RADS 3, 4 or 5 MRI scan has a sensitivity of 93%. However, MRI has a false negative rate of 6.5% overall for clinically significant prostate cancers. Pre‐ biopsy MRI may reduce the number of unnecessary biopsies, as up to 50.0% of negative or ISUP1 biopsies have MRI PI‐RADS 2 or lower. Incorporation of MRI findings into the ERSPC calculator improved predictive performance for all prostate cancer diagnoses (AUC 0.77 vs 0.71, P = .04), but not for clinically significant cancer (AUC 0.89 vs 0.87, P = .37).

Conclusion

MRI has good sensitivity and negative predictive value for clinically significant prostate cancers. It is useful as a pre‐biopsy tool and can be used to significantly reduce the number of unnecessary prostate biopsies. However, MRI does not significantly improve risk predictions for clinically significant cancers when incorporated into the ERSPC risk calculator.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Aberrant Hypermethylation-Mediated Suppression of PYCARD Is Extremely Frequent in Prostate Cancer with Gleason Score ≥ 7

Epigenetic gene silencing by aberrant DNA methylation leads to loss of key cellular pathways in tumorigenesis. In order to analyze the effects of DNA methylation on prostate cancer, we established LNCaP-derived human prostate cancer cells that can pharmacologically induce global reactivation of hypermethylated genes by the methyl-CpG targeted transcriptional activation (MeTA) method. The MeTA suppressed the growth of LNCaP-derived cells and induced apoptosis. Microarray analysis indicated that PYCARD (PYD and CARD domain containing) encoding an apoptosis-inducing factor was upregulated by 65-fold or more after treatment with MeTA. We analyzed DNA methylation statuses using 50 microdissected primary prostate cancer tissues and found an extremely high frequency of tumor-specific promoter hypermethylation of PYCARD (90%, 45/50). Moreover, DNA methylation status was significantly associated with Gleason score (P = 0.0063); the frequency of tumor-specific hypermethylation was 96% (44/46) in tumors with Gleason score ≥ 7, whereas that in tumors with Gleason score 6 was 25% (1/4). Immunohistochemical analyses using these 50 cases indicated that only 8% (4/50) of cancerous tissues expressed PYCARD, whereas 80% (40/50) of corresponding normal prostate epithelial and/or basal cells expressed PYCARD. In addition, there was no relationship between PYCARD immunostaining and the Gleason score in cancerous tissue and surrounding normal tissue. Inducible expression of PYCARD inhibited cell proliferation by induction of apoptosis. These results suggest that aberrant methylation of PYCARD is a distinctive feature of prostate cancers with Gleason score ≥ 7 and may play an important role in escaping from apoptosis in prostatic tumorigenesis.

Predictive role of T2WI and ADC-derived texture parameters in differentiating Gleason score 3 + 4 and 4 + 3 prostate cancer

BACKGROUND

Since Gleason score (GS) 4 + 3 prostate cancer (PCa) has a worse prognosis than GS 3 + 4 PCa, differentiating these two types of PCa is of clinical significance.

OBJECTIVE

To assess the predictive roles of using T2WI and ADC-derived image texture parameters in differentiating GS 3 + 4 from GS 4 + 3 PCa.

METHODS

Forty-eight PCa patients of GS 3 + 4 and 37 patients of GS 4 + 3 are retrieved and randomly divided into training (60%) and testing (40%) sets. Axial image showing the maximum tumor size is selected in the T2WI and ADC maps for further image texture feature analysis. Three hundred texture features are computed from each region of interest (ROI) using MaZda software. Feature reduction is implemented to obtain 30 optimal features, which are then used to generate the most discriminative features (MDF). Receiver operating characteristic (ROC) curve analysis is performed on MDF values in the training sets to achieve cutoff values for determining the correct rates of discrimination between two Gleason patterns in the testing sets.

RESULTS

ROC analysis on T2WI and ADC-derived MDF values in the training set (n = 51) results in a mean area under the curve (AUC) of 0.953±0.025 (with sensitivity 0.9274±0.0615 and specificity 0.897±0.069), and 0.985±0.013 (with sensitivity 0.9636±0.0446 and specificity 0.9726±0.0258), respectively. Using the corresponding MDF cutoffs, 95.3% (ranges from 76.5% to 100%) and 94.1% (ranged from 76.5% to 100%) of test cases (n = 34) are correctly discriminated using T2WI and ADC-derived MDF values, respectively.

CONCLUSIONS

The study demonstrates that using T2WI and ADC-derived image texture parameters has a potential predictive role in differentiating GS 3 + 4 and GS 4 + 3 PCa.

Long-term and pathological outcomes of low- and intermediate-risk prostate cancer after radical prostatectomy: implications for active surveillance

PURPOSE

The safety of active surveillance (AS) in favorable intermediate-risk (FIR) prostate cancer (PCa) remains uncertain. To provide guidance on clinical decision-making, we examined long-term and pathological outcomes of low-risk and intermediate-risk PCa patients after radical prostatectomy (RP).

METHODS

The study involved 5693 patients diagnosed between 1994 and 2019 with low-risk, FIR, and unfavorable intermediate-risk (UIR) PCa (stratification according to the AUA guidelines) who underwent RP. Pathological outcomes were compared, and Kaplan-Meier analysis determined biochemical recurrence-free survival (BRFS) and cancer-specific survival (CSS) at 5, 10, 15, and 20 years. Multiple Cox regression was used to simultaneously control for relevant confounders.

RESULTS

Those at FIR had higher rates of upgrading and upstaging (12.8% vs. 7.2%, p < 0.001; 19.8% vs. 12.0%, p < 0.001) as well as pathological tumor and node stage (≥ pT3a: 18.8% vs. 11.6%, p < 0.001; pN1: 2.7% vs. 0.8%, p > 0.001) compared to patients at low risk. The 20-year BRFS was 69%, 65%, and 44% and the 20-year CSS was 98%, 95%, and 89% in low-risk, FIR, and UIR patients. On multiple Cox regression, FIR was not associated with a worse BRFS (HR 1.07, CI 0.87-1.32), UIR was associated with a worse BRFS (HR 1.49, CI 1.20-1.85).

CONCLUSION

Patients at FIR had only slightly worse pathological and long-term outcomes compared to patients at low risk, whereas the difference compared to patients at UIR was large. This emphasizes AS in these patients as a possible treatment strategy in well-counseled patients.

Multiparametric MRI in the management of prostate cancer: an update-a narrative review

The growing interest in multiparametric MRI is leading to important changes in the diagnostic process of prostate cancer. MRI-targeted biopsy is likely to become a standard for the diagnosis of prostate cancer in the next years. Despite it is well known that MRI has no role as a staging technique, it is clear that multiparametric MRI may be of help in active surveillance protocols. Noteworthy, MRI in active surveillance is not recommended, but a proper understanding of its potential may be of help in achieving the goals of a delayed treatment strategy. Moreover, the development of minimally invasive techniques, like laparoscopic and robotic surgery, has led to greater expectations as regard to the functional outcomes of radical prostatectomy. Multiparametric MRI may play a role in planning surgical strategies, with the aim to provide the highest oncologic outcome with a minimal impact on the quality of life. We maintain that a proper anatomic knowledge of prostate lesions may allow the surgeon to achieve a better result in planning as well as in performing surgery and help the surgeon and the patient engage in a shared decision in planning a more effective strategy for prostate cancer control and treatment. This review highlights the advantages and the limitations of multiparametric MRI in prostate cancer diagnosis, in active surveillance and in planning surgery.

Improving the Stratification of Patients With Intermediate-risk Prostate Cancer

BACKGROUND

Intermediate-risk prostate cancer (IR PCa) phenotypes may vary from favorable to unfavorable. National Comprehensive Cancer Network (NCCN) criteria help distinguish between those groups. We studied and attempted to improve this stratification.

PATIENTS AND METHODS

A total of 4048 (NCCN favorable: 2015 [49.8%] vs. unfavorable 2033 [50.2%]) patients with IR PCa treated with radical prostatectomy were abstracted from an institutional database (2000-2018). Multivariable logistic regression models predicting upstaging and/or upgrading (Gleason Grade Group [GGG] IV-V and/or ≥ pT3 or pN1) in IR PCa were developed, validated, and directly compared with the NCCN IR PCa stratification.

RESULTS

All 4048 patients were randomly divided between development (n = 2024; 50.0%) and validation cohorts (n = 2024; 50.0%). The development cohort was used to fit basic (age, prostate-specific antigen, clinical T stage, biopsy GGG, and percentage of positive cores [all P < .001]) and extended models (age, prostate-specific antigen, clinical T stage, biopsy GGG, prostate volume, and percentage of tumor within all biopsy cores [all P < .001]). In the validation cohort, the basic and the extended models were, respectively, 71.4% and 74.7% accurate in predicting upstaging and/or upgrading versus 66.8% for the NCCN IR PCa stratification. Both models outperformed NCCN IR PCa stratification in calibration and decision curve analyses (DCA). Use of NCCN IR PCa stratification would have misclassified 20.1% of patients with ≥ pT3 or pN1 and/or GGG IV to V versus 18.3% and 16.4% who were misclassified using the basic or the extended model, respectively.

CONCLUSION

Both newly developed and validated models better discriminate upstaging and/or upgrading risk than the NCCN IR PCa stratification.

EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer-2020 Update. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent

OBJECTIVE

To present a summary of the 2020 version of the European Association of Urology (EAU)-European Association of Nuclear Medicine (EANM)-European Society for Radiotherapy and Oncology (ESTRO)-European Society of Urogenital Radiology (ESUR)-International Society of Geriatric Oncology (SIOG) guidelines on screening, diagnosis, and local treatment of clinically localised prostate cancer (PCa).

EVIDENCE ACQUISITION

The panel performed a literature review of new data, covering the time frame between 2016 and 2020. The guidelines were updated and a strength rating for each recommendation was added based on a systematic review of the evidence.

EVIDENCE SYNTHESIS

A risk-adapted strategy for identifying men who may develop PCa is advised, generally commencing at 50 yr of age and based on individualised life expectancy. Risk-adapted screening should be offered to men at increased risk from the age of 45 yr and to breast cancer susceptibility gene (BRCA) mutation carriers, who have been confirmed to be at risk of early and aggressive disease (mainly BRAC2), from around 40 yr of age. The use of multiparametric magnetic resonance imaging in order to avoid unnecessary biopsies is recommended. When a biopsy is performed, a combination of targeted and systematic biopsies must be offered. There is currently no place for the routine use of tissue-based biomarkers. Whilst prostate-specific membrane antigen positron emission tomography computed tomography is the most sensitive staging procedure, the lack of outcome benefit remains a major limitation. Active surveillance (AS) should always be discussed with low-risk patients, as well as with selected intermediate-risk patients with favourable International Society of Urological Pathology (ISUP) 2 lesions. Local therapies are addressed, as well as the AS journey and the management of persistent prostate-specific antigen after surgery. A strong recommendation to consider moderate hypofractionation in intermediate-risk patients is provided. Patients with cN1 PCa should be offered a local treatment combined with long-term hormonal treatment.

CONCLUSIONS

The evidence in the field of diagnosis, staging, and treatment of localised PCa is evolving rapidly. The 2020 EAU-EANM-ESTRO-ESUR-SIOG guidelines on PCa summarise the most recent findings and advice for their use in clinical practice. These PCa guidelines reflect the multidisciplinary nature of PCa management.

PATIENT SUMMARY

Updated prostate cancer guidelines are presented, addressing screening, diagnosis, and local treatment with curative intent. These guidelines rely on the available scientific evidence, and new insights will need to be considered and included on a regular basis. In some cases, the supporting evidence for new treatment options is not yet strong enough to provide a recommendation, which is why continuous updating is important. Patients must be fully informed of all relevant options and, together with their treating physicians, decide on the most optimal management for them.

Stratifying patients with intermediate-risk prostate cancer: Validation of a new model based on MRI parameters and targeted biopsy and comparison with NCCN and AUA subclassifications

OBJECTIVE

Intermediate-risk prostate cancer regroups heterogeneous patients with different oncologic outcomes. Aim of the study is to validate a novel intermediate-risk subclassification ("magnetic resonance imaging [MRI] subclassification") that defines favorable and unfavorable diseases based on multiparametric MRI parameters and compare it to NCCN and AUA intermediate-risk subclassifications.

METHODS

A total of 429 patients treated with radical prostatectomy for NCCN intermediate-risk prostate cancer were identified. Using MRI subclassification, a favorable disease was defined as an organ-confined disease on MRI and international society of urological pathology Grade Group 1 to 2 on targeted biopsy. Remaining was classified as unfavorable. Univariable and multivariable analysis tested MRI subclassification in predicting overall unfavorable disease (OUD: pT3-4 and/or pN1 and/or International Society of Urological Pathology Grade Group ≥ 3), the need for adjuvant therapy and early biochemical recurrence (eBCR). Performance of NCCN, AUA, and MRI models was compared in term of OUD proportion and eBCR prediction using Harrell's c-index, calibrations plots, and decision curve analysis.

RESULTS

Median (interquartile range) follow-up was 12 months (4-28). In multivariable analysis, MRI subclassification was an independent factor for OUD (odds ratio [OR]: 4.54 [2.85-7.22], P < 0.001), the need for adjuvant therapy (OR: 3.42 [1.36-8.57], P = 0.009), and eBCR (HR: 2.62 [1.18-5.83], P = 0.018). Using this model, the proportion of unfavorable disease decreased from 73.7% and 63.9% to 35.9% (P < 0.001) associated to an increasing proportion of OUD when compared to NCCN and AUA models (63.9% and 67.1%-77.9% respectively, P < 0.001). Performance of the 3 models for eBCR prediction tended to be similar with a poor accuracy ranged from 58.7% to 66.7% (P > 0.05), permanent miscalibration and a net benefit at decision curve analysis.

CONCLUSIONS

We validated an intermediate-risk subclassification based on MRI and targeted biopsy that potentially improves patient selection by reducing the number of patients considered at unfavorable risk while increasing proportion of patients harboring poor oncologic outcomes. Its performance for eBCR detection was comparable to NCCN and AUA models.

Urokinase-Type Plasminogen Activator Receptor (uPAR) PET/MRI of Prostate Cancer for Noninvasive Evaluation of Aggressiveness: Comparison with Gleason Score in a Prospective Phase 2 Clinical Trial

The aim of this study was to evaluate the correlation between uptake of the PET ligand ⁶⁸Ga-NOTA-AE105, targeting the urokinase-type plasminogen activator receptor (uPAR), and Gleason score in patients undergoing prostate biopsy. Methods: Patients with clinical suspicion of prostate cancer (PCa) or previously diagnosed with PCa were prospectively enrolled in this phase 2 trial. A combination of uPAR PET and multiparametric MRI (mpMRI) was performed, and the SUV in the primary tumor, as delineated by mpMRI, was measured by 2 independent readers. The correlation between the SUV and the Gleason score obtained by biopsy was assessed. Results: A total of 27 patients had histologically verified PCa visible on mpMRI and constituted the study population. There was a positive correlation between the SUV_max and the Gleason score (Spearman ρ = 0.55; P = 0.003). Receiver operating characteristic analysis showed an area under the curve of 0.88 (95% CI, 0.67-1.00) for discriminating a Gleason score of greater than or equal to 3 + 4 from a Gleason score of less than or equal to 3 + 3. A cutoff for the tumor SUV_max could be established with a sensitivity of 96% (79%-99%) and a specificity of 75% (30%-95%) for detecting a Gleason score of greater than or equal to 3 + 4. For discriminating a Gleason score of greater than or equal to 4 + 3 from a Gleason score of less than or equal to 3 + 4, a cutoff could be established for detecting a Gleason score of greater than or equal to 4 + 3 with a sensitivity of 93% (69%-99%) and a specificity of 62% (36%-82%). Conclusion: SUV measurements from uPAR PET in primary tumors, as delineated by mpMRI, showed a significant correlation with the Gleason score, and the tumor SUV_max was able to discriminate between low-risk Gleason score profiles and intermediate risk Gleason score profiles with a high diagnostic accuracy. Consequently, uPAR PET/MRI could be a promising method for the noninvasive evaluation of PCa and might reduce the need for repeated biopsies (e.g., in active surveillance).

Prostate specific membrane antigen positron emission tomography/computed tomography and staging high risk prostate cancer: a non-systematic review of high clinical impact literature

INTRODUCTION

Prostate specific membrane antigen (PSMA) positron emission tomography (PET) with computed tomography (CT) is a promising molecular imaging technique for prostate cancer (PCa). Although not yet included in international guidelines, PSMA PET/CT is commonly used in clinical practice to stage patients with newly diagnosed PCa. This review focuses on the most up-to-date literature on staging high-risk prostate cancer with PSMA PET/CT.

EVIDENCE ACQUISITION

An online based literature research encompassing original studies, reviews and meta-analysis was performed in the month of November of 2019. The most relevant and impactful research was then extracted based on the expertise of the authors, with the specific focus of highlighting the clinical impact and appropriateness of PSMA PET/CT in staging PCa.

EVIDENCE SYNTHESIS

The use of PSMA PET/CT is appropriate in all high-risk patients with newly diagnosed PCa as it will often have a significant clinical impact. Although preliminary findings are promising, there is still a scarcity of data regarding the performance of PSMA PET/CT vs. other modalities in defining disease within the prostate gland. There is good evidence suggesting that PSMA PET/CT may be superior to every other imaging modality in assessing loco-regional and distant metastatic disease.

CONCLUSIONS

PSMA PET/CT has the potential to become a gold standard in staging high risk prostate cancer, providing clinicians with accurate information on the extent of disease within the prostate and the presence of loco-regional and distant metastatic disease within a single scan.

[Reports of prostate needle biopsies-what pathologists provide and urologists want]

BACKGROUND

The prostate biopsy report is key for risk stratification of prostate cancer patients and subsequent therapeutic decision-making. However, due to the inclusion of a multitude of additional parameters its interpretation is becoming more challenging.

OBJECTIVES

We aimed to determine how urologists currently interpret prostate biopsy reports, in particular how they consider different histopathological parameters for therapy decision-making.

MATERIALS AND METHODS

A survey was sent to all urology practices in Germany with the help of the BDU (Berufsverband der Deutschen Urologen e. V.). In total, there were 106 complete responses that could be included for further analyses.

RESULTS

Most urologists consider the number of positive cores and relative tumor burden (%) per core as crucial for the assessment of tumor extension. In case of targeted biopsies, the majority of urologists prefers a separate statement of positive cores per random biopsy scheme and per region of interest, respectively. The core with the highest Gleason score is mostly the basis for therapy decision-making (versus the overall Gleason score). Proportion of Gleason 4 pattern also seems to be critical for prostate cancer management. Only half of the urologists demand reporting of the new ISUP/WHO (International Society of Urological Pathology/World Health Organization) grade groups. Additional parameters claimed are Ki67, prostate-specific membrane antigen status, presence of intraductal or neuroendocrine component of the tumor.

CONCLUSIONS

Our survey shows that there is no standardized reporting for prostate biopsies and that the interpretation of prostate biopsy reports varies among urologists. Further studies and guideline recommendations are necessary to establish a standardized reporting scheme for prostate biopsies.

PSA and PSA Kinetics Thresholds for the Presence of 68Ga-PSMA-11 PET/CT-Detectable Lesions in Patients With Biochemical Recurrent Prostate Cancer

⁶⁸Ga-PSMA-11 positron-emission tomography/computed tomography (PET/CT) is commonly used for restaging recurrent prostate cancer (PC) in European clinical practice. The goal of this study is to determine the optimum time for performing these PET/CT scans in a large cohort of patients by identifying the prostate-specific-antigen (PSA) and PSA kinetics thresholds for detecting and localizing recurrent PC. This retrospective analysis includes 581 patients with biochemical recurrence (BC) by definition. The performance of ⁶⁸Ga-PSMA-11 PET/CT in relation to the PSA value at the scan time as well as PSA kinetics was assessed by the receiver-operating-characteristic-curve (ROC) generated by plotting sensitivity versus 1-specificity. Malignant prostatic lesions were identified in 77%. For patients that were treated with radical prostatectomy (RP) a PSA value of 1.24 ng/mL was found to be the optimal cutoff level for predicting positive and negative scans, while for patients previously treated with radiotherapy (RT) it was 5.75 ng/mL. In RP-patients with PSA value <1.24 ng/mL, 52% scans were positive, whereas patients with PSA ≥1.24 ng/mL had positive scan results in 87%. RT-patients with PSA <5.75 ng/mL had positive scans in 86% and for those with PSA ≥5.75 ng/mL 94% had positive scans. This study identifies the PSA and PSA kinetics threshold levels for the presence of ⁶⁸Ga-PSMA-11 PET/CT-detectable PC-lesions in BC patients.

Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer

PURPOSE

To explore associations between magnetic resonance imaging (MRI) features of prostate cancer and expression levels of cell cycle genes, as assessed by the Prolaris^® test.

MATERIALS AND METHODS

Retrospective analysis of 118 PCa patients with genetic testing of biopsy specimen and prostate MRI from 08/2013 to 11/2015. Associations between the cell cycle risk (CCR) score and MRI features [i.e., PI-RADSv2 score, extracapsular extension (ECE), quantitative metrics] were analyzed with Fisher's exact test, nonparametric tests, and Spearman's correlation coefficient. In 41 patients (34.7%), test results were compared to unfavorable features on prostatectomy specimen (i.e., Gleason group ≥ 3, ECE, lymph node metastases).

RESULTS

Fifty-four (45.8%), 60 (50.8%), and 4 (3.4%) patients had low-, intermediate-, and high-risk cancers according to American Urological Association scoring system. Patients with ECE on MRI had significantly higher mean CCR scores (reader 1: 3.9 vs. 3.2, p = 0.015; reader 2: 3.6 vs. 3.2, p = 0.045). PI-RADSv2 scores and quantitative MRI features were not associated with CCR scores. In the prostatectomy subset, ECE on MRI (p = < 0.001-0.001) and CCR scores (p = 0.049) were significantly associated with unfavorable histopathologic features.

CONCLUSION

The phenotypic trait of ECE on MRI indicates a more aggressive genotype of prostate cancer.

Advancing Risk Assessment of Intermediate Risk Prostate Cancer Patients

The individual risk to progression is unclear for intermediate risk prostate cancer patients. To assess their risk to progression, we examined the level of genomic instability in circulating tumor cells (CTCs) using quantitative three-dimensional (3D) telomere analysis. Data of CTCs from 65 treatment-naïve patients with biopsy-confirmed D’Amico-defined intermediate risk prostate cancer were compared to radical prostatectomy pathology results, which provided a clinical endpoint to the study and confirmed pre-operative pathology or demonstrated upgrading. Hierarchical centroid cluster analysis of 3D pre-operative CTC telomere profiling placed the patients into three subgroups with different potential risk of aggressive disease. Logistic regression modeling of the risk of progression estimated odds ratios with 95% confidence interval (CI) and separated patients into “stable” vs. “risk of aggressive” disease. The receiver operating characteristic (ROC) curve showed an area under the curve (AUC) of 0.77, while prostate specific antigen (PSA) (AUC of 0.59) and Gleason 3 + 4 = 7 vs. 4 + 3 = 7 (p > 0.6) were unable to predict progressive or stable disease. The data suggest that quantitative 3D telomere profiling of CTCs may be a potential tool for assessing a patient’s prostate cancer pre-treatment risk.

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer

PURPOSE

To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.

METHODS

We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.

RESULTS

119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.

CONCLUSIONS

ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

Subtyping the Risk of Intermediate Risk Prostate Cancer for Active Surveillance Based on Adverse Pathology at Radical Prostatectomy

PURPOSE

Intermediate risk prostate cancer is a heterogenous classification with favorable proposed criteria based on men treated with radiation therapy. However, there is uncertain application to active surveillance. We quantified the rate of adverse surgical pathology and implications for survival in patients at favorable intermediate risk compared to those with low risk prostate cancer.

MATERIALS AND METHODS

We performed a comparative cohort study of men with prostate cancer from 2009 to 2013 in the National Cancer Database who underwent radical prostatectomy. The study primary end point was adverse pathology, defined as Grade Group 3 or greater/pT3b/pN1. Various favorable intermediate risk definitions were evaluated, including the Memorial Sloan Kettering Cancer Center definition of Grade Group 2 or less with only 1 intermediate risk factor (Grade Group 2/cT2b/prostate specific antigen 10 to 20 ng/ml), which we defined as type 1 intermediate risk. The remaining patients at intermediate risk were classified as type 2 intermediate risk. Log binomial, logistic and Cox proportional hazards regression models were applied.

RESULTS

Adverse pathological findings were noted in 3,519 of the 51,688 patients (6.8%) at low risk and 8,888 of the 42,720 Grade Group 2 patients (20.8%) at intermediate risk (RR 3.06, 95% CI 2.95-3.17, p <0.001). Stratification by prostate specific antigen and volume minimally impacted the absolute rate. Results were similar for the Memorial Sloan Kettering Cancer Center definition (type 1 intermediate risk). Type 2 intermediate risk led to a greater risk of adverse pathology (RR 8.52, 8.23-8.82, p <0.001) and Grade Group 1 intermediate risk led to lower risk (RR 2.00, 1.86-2.16, p <0.001). Patients at favorable intermediate risk had worse overall survival than patients at low risk in adjusted models due to adverse pathology.

CONCLUSIONS

Adverse pathology at radical prostatectomy was observed at a threefold higher rate in patients classified at favorable intermediate risk compared to low risk, leading to worse overall survival. Men at intermediate risk may be better classified as types 1 and 2 since none showed pathological outcomes similar to those of men at low risk.