Prostate Health Index and multiparametric magnetic resonance imaging to predict prostate cancer grade reclassification in active surveillance

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

Different diagnostic strategies combining prostate health index and magnetic resonance imaging for predicting prostate cancer: A multicentre study

OBJECTIVE

To explore how prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) should be used in concert to improve diagnostic capacity for clinically significant prostate cancers (CsCaP) in patients with prostate-specific antigen (PSA) between 4 and 20 ng/ml.

METHODS

About 426 patients fulfilling the inclusion criteria were included in this study. Univariable and multivariable logistic analyses were performed to analyze the association between the clinical indicators and CaP/CsCaP. We used the Delong test to compare the differences in the area under the curve (AUC) values of four models for CaP and CsCaP. Decision curve analysis (DCA) and calibration plots were used to assess predictive performance. We compared clinical outcomes of different diagnostic strategies constructed using different combinations of the models by the chi-square test and the McNemar test.

RESULTS

The AUC of PHI-MRI (a risk prediction model based on PHI and mpMRI) was 0.859, which was significantly higher than those of PHI (AUC = 0.792, P < 0.001) and mpMRI (AUC = 0.797, P < 0.001). PHI-MRI had a higher net benefit on DCA for predicting CaP and CsCaP in comparison to PHI and mpMRI. Adding the PHI-MRI in diagnostic strategies for CsCaP, such as use PHI-MRI alone or sequential use of PHI followed by PHI-MRI, could reduce the number of biopsies by approximately 20% compared to use PHI followed by mpMRI (256 vs 316, 257 vs 316, respectively).

CONCLUSIONS

The PHI-MRI model was superior to PHI and MRI alone. It may reduce the number of biopsies and ensure the detection rate of CsCaP under an appropriate sensitivity at the cost of an increased number of MRI scans.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

Prospective comparison of restriction spectrum imaging and non-invasive biomarkers to predict upgrading on active surveillance prostate biopsy

BACKGROUND

Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy.

METHODS

We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC.

RESULTS

The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS.

CONCLUSIONS

RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.

Baseline prostate health index risk category and risk category changes during active surveillance predict grade reclassification

BACKGROUND

It is not known whether baseline prostate health index (PHI) at the initiation of active surveillance (AS) or repeated PHI testing during AS is of clinical value after confirmatory biopsy in AS men followed with multiparametric magnetic resonance imaging (mpMRI).

METHODS

We identified 382 AS patients with no greater than Grade Group 1 (GG1) prostate cancer on diagnostic and confirmatory biopsy, at least one mpMRI and PHI test, of which 241 had at least 2 PHI tests. Grade reclassification (GR) was defined as ≥GG2 on surveillance biopsy. PHI risk categories 1 to 4 were as defined by the manufacturer. Associations between baseline PHI risk category or baseline PSA density (PSAD), change in PHI risk categories over time or PSAD changes over time and GR were evaluated with multivariable Cox proportional hazard regression models adjusted for age, Prostate Imaging-Reporting and Data System score and number of positive cores.

RESULTS

Men with baseline PHI scores in the highest risk categories had lower rates of GR-free survival (log-rank P < 0.001), as did those who increased in PHI risk category or remained in a high PHI risk category during surveillance (log-rank P = 0.032). On multivariable regression, baseline PHI risk category was a predictor of GR (risk category 4 [vs. 1] hazard ratio [HR] 2.74, 95% confidence interval [CI] 1.32-5.66, P = 0.002, model C-index 0.764, Akaike Information Criterion [AIC] 797), as were PHI risk category changes over time (risk category 4 [vs. 1] HR 4.20, 95% CI 1.76-10.05, P = 0.002, C-index 0.759, AIC 489). Separate models with baseline PSAD and PSAD changes over time yielded C-indices of 0.709 (AIC 809) and 0.733 (AIC 495) respectively.

CONCLUSIONS

Baseline PHI risk category and PHI changes over time were both independent predictors of GR after confirmatory biopsy, but the added benefit over PSAD seemed modest. However, baseline PHI and PHI risk category changes provided clinically useful risk stratification for time to GR, so further evaluation of PHI's ability to help reduce the frequency of mpMRI and/or surveillance biopsies with more PHI data points over time may be warranted.

Is Active Surveillance Too Active?

PURPOSE OF REVIEW

Many prostate cancer active surveillance protocols mandate serial monitoring at defined intervals, including but certainly not limited to serum PSA (often every 6 months), clinic visits, prostate multiparametric MRI, and repeat prostate biopsies. The purpose of this article is to evaluate whether current protocols result in excessive testing of patients on active surveillance.

RECENT FINDINGS

Multiple studies have been published in the past several years evaluating the utility of multiparametric MRI, serum biomarkers, and serial prostate biopsy for men on active surveillance. While MRI and serum biomarkers have promise with risk stratification, no studies have demonstrated that periodic prostate biopsy can be safely omitted in active surveillance. Active surveillance for prostate cancer is too active for some men with seemingly low-risk cancer. The use of multiple prostate MRIs or additional biomarkers do not always add to the prediction of higher-grade disease on surveillance biopsy.

Aggressive Prostate Cancer in Patients Treated with Active Surveillance

Active surveillance has emerged as a promising approach for managing low-risk and favorable intermediate-risk prostate cancer (PC), with the aim of minimizing overtreatment and maintaining the quality of life. However, concerns remain about identifying "aggressive prostate cancer" within the active surveillance cohort, which refers to cancers with a higher potential for progression. Previous studies are predictors of aggressive PC during active surveillance. To address this, a personalized risk-based follow-up approach that integrates clinical data, biomarkers, and genetic factors using risk calculators was proposed. This approach enables an efficient risk assessment and the early detection of disease progression, minimizes unnecessary interventions, and improves patient management and outcomes. As active surveillance indications expand, the importance of identifying aggressive PC through a personalized risk-based follow-up is expected to increase.

Prostate health index (PHI) as an accurate prostate cancer predictor

PURPOSE

This study aims to compare the ability of the PHI versus tPSA test to predict the presence of PCa in our population.

METHODS

A prospective observational study was performed. We included patients with tPSA ≥ 2.5 ng/ml, biopsy naïve or previous negative biopsy, undergoing a blood test, which includes tPSA, fPSA, and p2PSA, and a prostate biopsy between March 2019 and March 2022. Patients with PCa found in the biopsy-Group A-were compared with patients with a negative biopsy result-Group B. Diagnostic accuracy of tPSA and PHI was assessed by receiver operating characteristic [ROC] curves and logistic regression.

RESULTS

140 men were included. Fifty-seven (40.7%) had a positive prostate biopsy result (Group A), and 83 (59.3%) had a negative biopsy result (Group B). The mean age was similar in both groups (mean ± standard deviation), 66.86 ± 6.61 years. No difference was found in the tPSA value between the groups (Group A PSA: 6.11 ng/ml (3.56-17.01); Group B: 6.42 ng/ml (2.46-19.45), p = 0.41). The mean value of PHI was statistically different between groups (Group A 65.50 (29-146) vs. Group B 48 (16-233), p = 0.0001). The area under the curve 0.44 for tPSA and 0.77 for PHI. The multivariate logistic regression model applied to PHI showed a significant increase in its predictive accuracy: 72.14% in the model without PHI, 76.09% with PHI.

CONCLUSION

The PHI test improves PCa detection compared to tPSA in our population.

Nomograms Combining PHI and PI-RADS in Detecting Prostate Cancer: A Multicenter Prospective Study

(1) Background: The study aimed to construct nomograms to improve the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in the Asian population. (2) Methods: This multicenter prospective study included a group of 293 patients from three hospitals. Univariable and multivariable logistic regression analysis was performed to identify potential risk factors and construct nomograms. Discrimination, calibration, and clinical utility were used to assess the performance of the nomogram. The web-based dynamic nomograms were subsequently built based on multivariable logistic analysis. (3) Results: A total of 293 patients were included in our study with 201 negative and 92 positive results in PCa. Four independent predictive factors (age, prostate health index (PHI), prostate volume, and prostate imaging reporting and data system score (PI-RADS)) for PCa were included, and four factors (age, PHI, PI-RADS, and Log PSA Density) for CSPCa were included. The area under the ROC curve (AUC) for PCa was 0.902 in the training cohort and 0.869 in the validation cohort. The AUC for CSPCa was 0.896 in the training cohort and 0.890 in the validation cohort. (4) Conclusions: The combined diagnosis of PHI and PI-RADS can avoid more unnecessary biopsies and improve the detection rate of PCa and CSPCa. The nomogram with the combination of age, PHI, PV, and PI-RADS could improve the detection of PCa, and the nomogram with the combination of age, PHI, PI-RADS, and Log PSAD could improve the detection of CSPCa.

Development of a novel nomogram for predicting clinically significant prostate cancer with the prostate health index and multiparametric MRI

INTRODUCTION

On prostate biopsy, multiparametric magnetic resonance imaging (mpMRI) and the Prostate Health Index (PHI) have allowed prediction of clinically significant prostate cancer (csPCa).

METHODS

To predict the likelihood of csPCa, we created a nomogram based on a multivariate model that included PHI and mpMRI. We assessed 315 males who were scheduled for prostate biopsies.

RESULTS

We used the Prostate Imaging Reporting and Data System version 2 (PI-RADS V2) to assess mpMRI and optimize PHI testing prior to biopsy. Univariate analysis showed that csPCa may be identified by PHI with a cut-off value of 77.77, PHID with 2.36, and PI-RADS with 3 as the best threshold. Multivariable logistic models for predicting csPCa were developed using PI-RADS, free PSA (fPSA), PHI, and prostate volume. A multivariate model that included PI-RADS, fPSA, PHI, and prostate volume had the best accuracy (AUC: 0.882). Decision curve analysis (DCA), which was carried out to verify the nomogram's clinical applicability, showed an ideal advantage (13.35% higher than the model that include PI-RADS only).

DISCUSSION

In conclusion, the nomogram based on PHI and mpMRI is a valuable tool for predicting csPCa while avoiding unnecessary biopsy as much as possible.

Value of PHI and PHID in the detection of intermediate- and high-risk prostate cancer

BACKGROUND AND AIMS

PSA testing practice results in a large number of unnecessary prostate biopsies and the overdiagnosis of clinically insignificant prostate cancer (PCa). The aim of our study was to evaluate the value of PHI and PHID for the detection of PCa.

MATERIALS AND METHODS

We measured tPSA, fPSA and p2PSA in 455 patients scheduled for biopsy, including 243 patients with PCa. D'Amico criteria were used to classify these patients in three groups related to risk of progression. Intermediate- and high-risk PCa were considered as aggressive PCa.

RESULTS

The best area under the curve (AUC) value obtained in the detection of aggressive PCa was achieved for PHI and PHID (0.766 and 0.760, respectively). We found a relationship of the performance of by these tests with the calculated prostate volume or the estimated prostate size by digital rectal exam, obtaining the higher AUC in patients with a small prostate. Thus, the AUC for PHI was 0,843 for patients with small calculated prostate volume and 0,817 for patients with small estimated prostate size.

CONCLUSIONS

Our results underline that PHI and PHID outperforms the efficacy obtained with tPSA and %fPSA. Substantial differences in their value in relation to prostate volume were found.

Modified Prostate Health Index Density Significantly Improves Clinically Significant Prostate Cancer (csPCa) Detection

Background

Early screening of clinically significant prostate cancer (csPCa) may offer opportunities in revolutionizing the survival benefits of this lethal disease. We sought to introduce a modified prostate health index density (mPHI) model using imaging indicators and to compare its diagnostic performance for early detection of occult onset csPCa within the prostate-specific antigen (PSA) gray zone with that of PHI and PHID.

Methods and Participation

Between August 2020 and January 2022, a training cohort of 278 patients (total PSA 4.0-10.0 ng/ml) who were scheduled for a prostate biopsy were prospectively recruited. PHI and PHID were compared with mPHI ( LD TRD × APD × TPV × PHI ) for the diagnosis performance in identifying csPCa. Pathology outcomes from systematic prostate biopsies were considered the gold standard.

Results

This model was tested in a training cohort consisting of 73 csPCa, 14 non-clinically significant prostate cancer(non-csPCa), and 191 benign prostatic hyperplasia (BPH) samples. In the univariate analysis for the PSA gray zone cohort, for overall PCa, the AUC of mPHI (0.856) was higher than PHI (0.774) and PHID (0.835). For csPCa, the AUC of mPHI (0.859) also surpassed PHI (0.787) and PHID (0.825). For detection of csPCa, compared with lower specificities from PHI and PHID, mPHI performed the highest specificity (76.5%), by sparing 60.0% of unnecessary biopsies at the cost of missing 11 cases of csPCa. The mPHI outperformed PHI and PHID for overall PCa detection. In terms of csPCa, mPHI exceeds diagnostic performance with a better net benefit in decision curve analysis (DCA) compared with PHI or PHID.

Conclusions

We have developed a modified PHI density (mPHI) model that can sensitively distinguish early-stage csPCa patients within the PSA gray zone.

Clinical Trial Registration

ClinicalTrials.gov, NCT04251546.

The modified prostate health index (PHI) outperforms PHI density in the detection of clinical prostate cancer within the PSA grey zone

OBJECTIVES

To compare the accuracy of several volume and diameters modified prostate health index (mPHI) models with PHI density (PHID), PHI, and other prostate-specific antigen (PSA) derivatives in detecting PSA grey zone prostate cancer (PCa).

MATERIALS AND METHODS

Between August 2020 and September 2021, a consecutive cohort of 214 suspected PCa patients with elevated total PSA values ranged from 4.0 to 10.0 ng/mL were prospectively recruited and received PHI detections and transrectal ultrasonography (TRUS) measurements, followed by systematic prostate biopsies confirmation.

RESULTS

Among the 214 patients enrolled in the project, a total of 80 were diagnosed with PCa. In univariate analysis for the training cohort, the area under curve (AUC) of mPHI-2 [Formula: see text] was 0.8310, which outperformed PHID in identifying PSA grey zone PCa (P ≤ 0.0001) and showed the best net benefit in decision curve analysis (DCA). By a threshold of 0.2835, the sensitivity and specificity in the prediction of PCa were 78.9% and 90.3%, while the positive predictive value (PPV) and negative predictive value (NPV) were 78.3% and 78.6%, respectively.

CONCLUSIONS

According to our present single-center experience, the mPHI-2 risk predictor outperformed PHID or other classical parameters alone in the PCa detection with a grey zone PSA level in Asian males.

Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer

Simple Summary

In the last decades, the widespread use of PSA as the standard tool for prostate cancer diagnosis led to a high rate of overdiagnosis and overtreatment. More recently, multiparametric magnetic resonance imaging (mpMRI) became part of the diagnostic pathway, and several next-generation PSA-based tests (PHI, PHI density, 4Kscore, STHLM3) have been proposed. The multivariable approach promises to help with a better stratification of PCa patients at initial diagnosis. In this study, we evaluated the performance of the prostate health index (PHI) and mpMRI for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). Our findings suggested that PHI had a better ability than mpMRI to predict positive biopsy, whereas a comparable performance in the identification of pathological aggressive PCa was pointed out. Notably, PHI and PHI density might represent useful biomarkers to recognize high-grade PCa in patients with low or uncertain PI-RADS scores on mpMRI.

Abstract

Widespread use of PSA as the standard tool for prostate cancer (PCa) diagnosis led to a high rate of overdiagnosis and overtreatment. In this study, we evaluated the performance of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). To this end, we prospectively enrolled 196 biopsy-naïve patients who underwent mpMRI. A subgroup of 116 subjects with biopsy-proven PCa underwent surgery. We found that PHI significantly outperformed both PI-RADS score (difference in AUC: 0.14; p < 0.001) and PHI density (difference in AUC: 0.08; p = 0.002) in the ability to predict positive biopsy with a cut-off value of 42.7 as the best threshold. Conversely, comparing the performance in the identification of clinically significant prostate cancer (csPCa) at RP, we found that PHI ≥ 61.68 and PI-RADS score ≥ 4 were able to identify csPCa (Gleason score ≥ 7 (3 + 4)) both alone and added to a base model including age, PSA, fPSA-to-tPSA ratio and prostate volume. In conclusion, PHI had a better ability than PI-RADS score to predict positive biopsy, whereas it had a comparable performance in the identification of pathological csPCa.

Biomarker in Active Surveillance for Prostate Cancer: A Systematic Review

Active surveillance (AS) in prostate cancer (PCa) represents a curative alternative for men with localised low-risk PCa. Continuous improvement of AS patient's selection and surveillance modalities aims at reducing misclassification, simplifying modalities of surveillance and decreasing need for invasive procedures such repeated biopsies. Biomarkers represent interesting tools to evaluate PCa diagnosis and prognosis, of which many are readily available or under evaluation. The aim of this review is to investigate the biomarker performance for AS selection and patient outcome prediction. Blood, urinary and tissue biomarkers were studied and a brief description of use was proposed along with a summary of major findings. Biomarkers represent promising tools which could be part of a more tailored risk AS strategy aiming to offer personalized medicine and to individualize the treatment and monitoring of each patient. The usefulness of biomarkers has mainly been suggested for AS selection, whereas few studies have investigated their role during the monitoring phase. Randomized prospective studies dealing with imaging are needed as well as larger prospective studies with long-term follow-up and strong oncologic endpoints.

Does active surveillance avoid overtreatment in prostate cancer? Lessons learned from salvage radical prostatectomies

OBJECTIVE

Determine whether our institution´s active surveillance (AS) protocol is a suitable strategy to minimise prostate cancer overtreatment.

MATERIAL AND METHODS

Retrospective analysis of 516 patients on AS after prostate cancer diagnosis. Population divided into "per-protocol" vs "induced" AS depending on fulfilment of protocol´s inclusion criteria. Radical prostatectomies after AS were selected and stratified based on: reclassification, progression or patient anxiety. Clinicopathological features and biochemical relapse-free survival were studied. Primary endpoint was overtreatment ratio based on the presence of insignificant prostate cancer and adverse pathological features in the surgical specimen. Kaplan-Meier curves were used to estimate the biochemical relapse-free survival and compared with log-rank test.

RESULTS

304 patients fulfilled inclusion criteria; 100 proceeded to radical prostatectomy (31% "induced", 69% "per-protocol" AS). Surgery indications were reclassification, progression and anxiety in 66%, 18% and 16% of patients respectively. Rate of positive lymph nodes was higher in the progression group (11%) compared to reclassification and anxiety (5% and 0% respectively, P = .002). Positive surgical margins were more frequently reported in the progression cohort compared to reclassification (28% vs 20%). Median follow-up from diagnosis until last radical prostatectomy was 48.3 months (32.4-70). 3 year biochemical relapse-free survival in the salvage radical prostatectomy was 85.4% (95 CI 78.3-93.2). Insignificant cancer was noticed in 7% of patients (Epstein´s vs 24% Wolters´ criteria). Rate of patients with adverse pathological features was 36%.

CONCLUSIONS

The majority of patients who underwent salvage surgery after AS were not overtreated. Radical prostatectomy should be considered a safe rescue treatment.

Prediction of Grade Reclassification of Prostate Cancer Patients on Active Surveillance through the Combination of a Three-miRNA Signature and Selected Clinical Variables

Simple Summary

Active surveillance (AS) has evolved as an alternative to radical treatment for potentially indolent prostate cancer. However, current selection criteria for entering AS are suboptimal, and a significant percentage of patients discontinue AS because of disease reclassification. Hence, there is an unmet need for novel biomarkers for the accurate identification of high-risk PCa and the unequivocal classification of indolent disease. Circulating biomarkers, including microRNAs identified through liquid biopsies, represent a valuable approach to improve on currently available clinicopathological risk-stratification tools. In an attempt to identify specific microRNA signatures as potential circulating biomarkers, the authors performed an unprecedented analysis of the global microRNA profile in plasma samples from AS patients and identified and validated a three-microRNA signature able to predict patient reclassification. The addition of the three-microRNA signature was able to improve the performance of currently available clinicopathological variables, thus showing potential for the refinement of AS patients’ selection.

Abstract

Active surveillance (AS) has evolved as a strategy alternative to radical treatments for very low risk and low-risk prostate cancer (PCa). However, current criteria for selecting AS patients are still suboptimal. Here, we performed an unprecedented analysis of the circulating miRNome to investigate whether specific miRNAs associated with disease reclassification can provide risk refinement to standard clinicopathological features for improving patient selection. The global miRNA expression profiles were assessed in plasma samples prospectively collected at baseline from 386 patients on AS included in three independent mono-institutional cohorts (training, testing and validation sets). A three-miRNA signature (miR-511-5p, miR-598-3p and miR-199a-5p) was found to predict reclassification in all patient cohorts (training set: AUC 0.74, 95% CI 0.60–0.87, testing set: AUC 0.65, 95% CI 0.51–0.80, validation set: AUC 0.68, 95% CI 0.56–0.80). Importantly, the addition of the three-miRNA signature improved the performance of the clinical model including clinicopathological variables only (AUC 0.70, 95% CI 0.61–0.78 vs. 0.76, 95% CI 0.68–0.84). Overall, we trained, tested and validated a three-miRNA signature which, combined with selected clinicopathological variables, may represent a promising biomarker to improve on currently available clinicopathological risk stratification tools for a better selection of truly indolent PCa patients suitable for AS.

Biomarkers in Prostate Cancer Diagnosis: From Current Knowledge to the Role of Metabolomics and Exosomes

Early detection of prostate cancer (PC) is largely carried out using assessment of prostate-specific antigen (PSA) level; yet it cannot reliably discriminate between benign pathologies and clinically significant forms of PC. To overcome the current limitations of PSA, new urinary and serum biomarkers have been developed in recent years. Although several biomarkers have been explored in various scenarios and patient settings, to date, specific guidelines with a high level of evidence on the use of these markers are lacking. Recent advances in metabolomic, genomics, and proteomics have made new potential biomarkers available. A number of studies focused on the characterization of the specific PC metabolic phenotype using different experimental approaches has been recently reported; yet, to date, research on metabolomic application for PC has focused on a small group of metabolites that have been known to be related to the prostate gland. Exosomes are extracellular vesicles that are secreted from all mammalian cells and virtually detected in all bio-fluids, thus allowing their use as tumor biomarkers. Thanks to a general improvement of the technical equipment to analyze exosomes, we are able to obtain reliable quantitative and qualitative information useful for clinical application. Although some pilot clinical investigations have proposed potential PC biomarkers, data are still preliminary and non-conclusive.

Advances in the selection of patients with prostate cancer for active surveillance

Early identification and management of prostate cancer completely changed with the discovery of prostate-specific antigen. However, improved detection has also led to overdiagnosis and consequently overtreatment of patients with low-risk disease. Strategies for the management of patients using active surveillance - the monitoring of clinically insignificant disease until intervention is warranted - were developed in response to this issue. The success of this approach is critically dependent on the accurate selection of patients who are predicted to be at the lowest risk of prostate cancer mortality. The Epstein criteria for clinically insignificant prostate cancer were first published in 1994 and have been repeatedly validated for risk-stratification and selection for active surveillance over the past few decades. Current active surveillance programmes use modified criteria with 30-50% of patients receiving treatment at 10 years. Nonetheless, tools for prostate cancer diagnosis have continued to evolve with improvements in biopsy format and targeting, advances in imaging technologies such as multiparametric MRI, and the identification of serum-, tissue- and urine-based biomarkers. These advances have the potential to further improve the identification of men with low-risk disease who can be appropriately managed using active surveillance.

Does active surveillance avoid overtreatment in prostate cancer? Lessons learned from salvage radical prostatectomies

OBJECTIVE

Determine whether our institution's active surveillance (AS) protocol is a suitable strategy to minimise prostate cancer overtreatment.

MATERIAL AND METHODS

Retrospective analysis of 516 patients on AS after prostate cancer diagnosis. Population divided into «per-protocol» vs «induced» AS depending on fulfilment of protocol's inclusion criteria. Radical prostatectomies after AS were selected and stratified based on reclassification, progression or patient anxiety. Clinicopathological features and biochemical relapse-free survival were studied. Primary endpoint was overtreatment ratio based on the presence of insignificant prostate cancer and adverse pathological features in the surgical specimen. Kaplan-Meier curves were used to estimate the biochemical relapse-free survival and compared with log-rank test.

RESULTS

304 patients fulfilled inclusion criteria; 100 proceeded to radical prostatectomy (31% «induced», 69% «per-protocol» AS). Surgery indications were reclassification, progression and anxiety in 66%, 18% and 16% of patients, respectively. Rate of positive lymph nodes was higher in the progression group (11%) compared to reclassification and anxiety (5% and 0%, respectively; P=.002). Positive surgical margins were more frequently reported in the progression cohort compared to reclassification (28% vs 20%). Median follow-up from diagnosis until last radical prostatectomy was 48.3months (32.4-70). Three year biochemical relapse-free survival in the salvage radical prostatectomy was 85.4% (95%CI: 78.3-93.2). Insignificant cancer was noticed in 7% of patients (Epstein's vs 24% Wolters' criteria). Rate of patients with adverse pathological features was 36%.

CONCLUSIONS

The majority of patients who underwent salvage surgery after AS were not overtreated. Radical prostatectomy should be considered a safe rescue treatment.

Prostate cancer biomarkers and multiparametric MRI: is there a role for both in prostate cancer management?

Several advancements have been made in recent years with regards to the detection and evaluation of prostate cancer (PCa). The low specificity of prostate specific antigen (PSA) has left much to be desired in a test, but a boom in novel biomarkers has made screening and surveillance more complicated. Several attempts at identifying a niche for these tests has helped somewhat, but much is still undetermined about the benefit that each test provides. In addition to laboratory tests, advancements in multiparametric magnetic resonance imaging (mpMRI) and PIRADSv.2 scoring have provided significant benefit to the evaluation of PCa. With the widespread use of prostate imaging, it is important to re-evaluate the impact of novel biomarkers in the context of furthering PCa screening and management. In this review, we aim to assess the influence mpMRI has on the role of nine different novel biomarkers in the detection and evaluation of PCa. We performed a review of current peer-reviewed literature to assess this question. Much data has been published on the role of these tests, allowing for their placement into one of three best-fit categories: tests for biopsy-naïve men (Prostate Health Index, Mi Prostate Score, 4K Score); tests for men with prior negative biopsies (ConfirmMDx, Progensa PCA3); and men on active surveillance (OncotypeDx, Prolaris, Decipher). Data on the role of these tests with the use of mpMRI have not been comprehensive and excludes several of the markers. More research is needed to determine the combined impact mpMRI and the novel biomarkers on the evaluation and management of PCa.

PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer

Prostate cancer represents the most encountered urinary malignancy in males over 50 years old, and the second most diagnosed after lung cancer globally. Digital rectal examination and prostatic specific antigen were the long-time standard tools for diagnosis but with a significant risk of overdiagnosis and overtreatment. Magnetic resonance imaging recently entered the diagnosis process, but to this date, there is no specific biomarker that accurately indicates whether to proceed with the prostate biopsy. Research in this area has gone towards this direction, and recently, serum, urine, imagistic, tissue biomarkers, and Risk Calculators promise to help better diagnose and stratify prostate cancer. In order to eliminate the comorbidities that appear along with the diagnosis and treatment of this disease, there is a constant need to implement new diagnostic strategies. Important uro-oncology associations recommend the use of novel biomarkers in the grey area of prostate cancer, to better distinguish the next step in the diagnostic process. Although it is not that simple, they should be integrated according to the clinical policies, and it should be considered that statistical significance does not always equal clinical significance. In this review, we analyzed the contribution of prostate-specific antigen (PSA)-based biomarkers (PHI, PHID, 4Kscore, STHLM3), imagistic techniques (mp-MRI and mp-US), and combined tests in the early diagnosis process of localized prostate cancer.

Incorporating Magnetic Resonance Imaging and Biomarkers in Active Surveillance Protocols - Results From the Prospective Stockholm3 Active Surveillance Trial (STHLM3AS)

BACKGROUND

Active surveillance (AS) for men with low-risk prostate cancer (PC) can lead to patient morbidity and healthcare overutilization. The aim of this study was to evaluate an AS protocol using the Stockholm3 test and magnetic resonance imaging (MRI) to reduce biopsy intensity.

METHODS

We conducted a prospective multicenter study of 280 invited men from a contemporary screening study (STHLM3), with Gleason Score (GS) 3 + 3 PC on a current AS protocol. Patients underwent prostate-MRI and blood sampling for analysis of the Stockholm3 test including protein biomarkers, genetic variants, and clinical variables to predict risk of GS ≥3 + 4 PC followed by systematic biopsies and targeted biopsies (for Prostate Imaging Reporting and Data System version 2 ≥3 lesions) in all men. Primary outcomes were reclassification to GS ≥3 + 4 PC and clinically significant PC (csPCa), including unfavorable intermediate risk PC or higher based on National Comprehensive Cancer Network guidelines.

RESULTS

Adding MRI-targeted biopsies to systematic biopsies increased sensitivity of GS ≥3 + 4 PC compared with systematic biopsies alone (relative sensitivity [RS] = 1.52, 95% confidence interval [CI] = 1.28 to 1.85). Performing biopsies in only MRI positive increased sensitivity of GS ≥3 + 4 PC (RS = 1.30, 95% CI = 1.04 to 1.67) and reduced number of biopsy procedures by 49.3% while missing 7.2% GS ≥3 + 4 PC and 1.4% csPCa. Excluding men with negative Stockholm3 test reduced the number of MRI investigations at follow-up by 22.5% and biopsies by 56.8% while missing 6.9% GS ≥3 + 4 PC and 1.3% csPCa.

CONCLUSION

Including MRI and targeted/systematic biopsies in the follow-up for men on AS increased sensitivity of PC reclassification. Incorporation of risk prediction models including biomarkers may reduce the need for MRI use in men with low-risk PC.