Comparison of Two Prostate Cancer Risk Calculators that Include the Prostate Health Index

A 2-year prospective evaluation of the Prostate Health Index in guiding biopsy decisions in a large cohort

OBJECTIVES

To prospectively evaluate how the Prostate Health Index (PHI) impacts on clinical decision in a real-life setting for men with a prostate-specific antigen (PSA) level between 4 and 10 ng/mL and normal digital rectal examination.

PATIENTS AND METHODS

Since 2016, the PHI has been available at no cost to eligible men in all Hong Kong public hospitals. All eligible patients who received PHI testing in all public Urology units (n = 16) in Hong Kong between May 2016 and August 2017 were prospectively included and followed up. All included men had a PHI test, with its result and implications explained; the subsequent follow-up plan was then decided via shared decision-making with urologists. Patients were followed up for 2 years, with outcomes including prostate biopsy rates and biopsy findings analysed in relation to the initial PHI measurements.

RESULTS

A total of 2828 patients were followed up for 2 years. The majority (82%) had PHI results in the lower risk range (score <35). Knowing the PHI findings, 83% of the patients with elevated PSA decided not to undergo biopsy. In all, 11% and 45% opted for biopsy in the PHI score <35 and ≥35 groups, respectively. The initial detection rate of International Society of Urological Pathology (ISUP) Grade Group (GG) ≥2 cancer was higher in the PHI score ≥35 group (23%) than in the PHI score <35 group (7.9%). Amongst patients with no initial positive biopsy findings, the subsequent positive biopsy rate for ISUP GG ≥2 cancer was higher in the PHI score ≥35 group (34%) than the PHI score <35 group (13%) with a median follow-up of 2.4 years.

CONCLUSION

In a real-life setting, with the PHI incorporated into the routine clinical pathway, 83% of the patients with elevated PSA level decided not to undergo prostate biopsy. The PHI pathway also improved the high-grade prostate cancer detection rate when compared to PSA-driven strategies. Higher baseline PHI predicted subsequent biopsy outcome at 2 years. The PHI can serve as a tool to individualise biopsy decisions and frequency of follow-up visits.

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

Reducing the demand for magnetic resonance imaging scans and prostate biopsies during the early detection of clinically significant prostate cancer: Applying the Barcelona risk-stratified pathway in Catalonia

PURPOSE

To analyze the reduction in multiparametric magnetic resonance imaging (mpMRI) demand and prostate biopsies after the hypothetical implementation of the Barcelona risk-stratified pathway (BCN-RSP) in a population of the clinically significant prostate cancer (csCaP) early detection program in Catalonia.

MATERIALS AND METHODS

A retrospective comparation between the hypothetical application of the BCN-RSP and the current pathway, which relied on pre-biopsy mpMRI and targeted and/or systematic biopsies, was conducted. The BCN-RSP stratify men with suspected CaP based on a prostate specific antigen (PSA) level >10 ng/ml and a suspicious rectal examination (DRE), and the Barcelona-risk calculator 1 (BCN-RC1) to avoid mpMRI scans. Subsequently, candidates for prostate biopsy following mpMRI are selected based on the BCN-RC2. This comparison involved 3,557 men with serum PSA levels > 3.0 ng/ml and/or suspicious DRE. The population was recruited prospectively in 10 centers from January 2021 and December 2022. CsCaP was defined when grade group ≥ 2.

RESULTS

CsCaP was detected in 1,249 men (35.1%) and insignificant CaP was overdeteced in 498 (14%). The BCN-RSP would have avoid 705 mpMRI scans (19.8%), and 697 prostate biopsies (19.6%), while 61 csCaP (4.9%) would have been undetected. The overdetection of insignificant CaP would have decrease in 130 cases (26.1%), and the performance of prostate biopsy for csCaP detection would have increase to 41.5%.

CONCLUSION

The application of the BCN-RSP would reduce the demand for mpMRI scans and prostate biopsies by one fifth while less than 5% of csCaP would remain undetected. The overdetection of insignificant CaP would decrease by more than one quarter and the performance of prostate biopsy for csCaP detection would increase to higher than 40%.

Investigating Efficient Risk-Stratified Pathways for the Early Detection of Clinically Significant Prostate Cancer

Risk-stratified pathways (RSPs) are recommended by the European Association of Uro-logy (EAU) to improve the early detection of clinically significant prostate cancer (csPCa). RSPs can reduce magnetic resonance imaging (MRI) demand, prostate biopsies, and the over-detection of insignificant PCa (iPCa). Our goal is to analyze the efficacy and cost-effectiveness of several RSPs by using sequential stratifications from the serum prostate-specific antigen level and digital rectal examination, the Barcelona risk calculators (BCN-RCs), MRI, and Proclarix™. In a cohort of 567 men with a serum PSA level above 3.0 ng/mL who underwent multiparametric MRI (mpMRI) and targeted and/or systematic biopsies, the risk of csPCa was retrospectively assessed using Proclarix™ and BCN-RCs 1 and 2. Six RSPs were compared with those recommended by the EAU that, stratifying men from MRI, avoided 16.7% of prostate biopsies with a prostate imaging-reporting and data system score of <3, with 2.6% of csPCa cases remaining undetected. The most effective RSP avoided mpMRI exams in men with a serum PSA level of >10 ng/mL and suspicious DRE, following stratifications from BCN-RC 1, mpMRI, and Proclarix™. The demand for mpMRI decreased by 19.9%, prostate biopsies by 19.8%, and over-detection of iPCa by 22.7%, while 2.6% of csPCa remained undetected as in the recommended RSP. Cost-effectiveness remained when the Proclarix™ price was assumed to be below EUR 200.

Improving the Early Detection of Clinically Significant Prostate Cancer in Men in the Challenging Prostate Imaging-Reporting and Data System 3 Category

BACKGROUND

Prostate Imaging-Reporting and Data System (PI-RADS) category 3 is a challenging scenario for detection of clinically significant prostate cancer (csPCa) and some tools can improve the selection of appropriate candidates for prostate biopsy.

OBJECTIVE

To assess the performance of the European Randomized Study of Screening for Prostate Cancer (ERSPC) magnetic resonance imaging (MRI) model, the new Proclarix test, and prostate-specific antigen density (PSAD) in selecting candidates for prostate biopsy among men in the PI-RADS 3 category.

DESIGN SETTING AND PARTICIPANTS

We conducted a head-to-head prospective analysis of 567 men suspected of having PCa for whom guided and systematic biopsies were scheduled between January 2018 and March 2020 in a single academic institution. A PI-RADS v.2 category 3 lesion was identified in 169 men (29.8%).

OUTCOME MEASUREMENT AND STATISTICAL ANALYSIS

csPCa, insignificant PCa (iPCa), and unnecessary biopsy rates were analysed. csPCa was defined as grade group ≥2. Receiver operating characteristic (ROC) curves, decision curve analysis curves, and clinical utility curves were plotted.

RESULTS AND LIMITATIONS

PCa was detected in 53/169 men (31.4%) with a PI-RADS 3 lesion, identified as csPCa in 25 (14.8%) and iPCa in 28 (16.6%). The area under the ROC curve for csPCa detection was 0.703 (95% confidence interval [CI] 0.621-0.768) for Proclarix, 0.657 (95% CI 0.547-0.766) for the ERSPC MRI model, and 0.612 (95% CI 0.497-0.727) for PSAD (p = 0.027). The threshold with the highest sensitivity was 10% for Proclarix, 1.5% for the ERSPC MRI model, and 0.07 ng/ml/cm3 for PSAD, which yielded sensitivity of 100%, 91%, and 84%, respectively. Some 21.3%, 26.2%, and 7.1% of biopsies would be avoided with Proclarix, PSAD, and the ERSPC MRI model, respectively. Proclarix showed a net benefit over PSAD and the ERSPC MRI model. Both Proclarix and PSAD reduced iPCa overdetection from 16.6% to 11.3%, while the ERSPC MRI model reduced iPCa overdetection to 15.4%.

CONCLUSIONS

Proclarix was more accurate in selecting appropriate candidates for prostate biopsy among men in the PI-RADS 3 category when compared to PSAD and the ERSPC MRI model. Proclarix detected 100% of csPCa cases and would reduce prostate biopsies by 21.3% and iPCa overdetection by 5.3%.

PATIENT SUMMARY

We compared three methods and found that the Proclarix test can optimise the detection of clinically significant prostate cancer in men with a score of 3 on the Prostate Imaging-Reporting and Data System for magnetic resonance imaging scans.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

Background

Screening for prostate cancer has long been a debated, complex topic. The use of risk calculators for prostate cancer is recommended for determining patients’ individual risk of cancer and the subsequent need for a prostate biopsy. These tools could lead to better discrimination of patients in need of invasive diagnostic procedures and optimized allocation of health care resources

Objective

The goal of the research was to systematically review available literature on the performance of current prostate cancer risk calculators in healthy populations by comparing the relative impact of individual items on different cohorts and on the models’ overall performance.

Methods

We performed a systematic review of available prostate cancer risk calculators targeted at healthy populations. We included studies published from January 2000 to March 2021 in English, Spanish, French, Portuguese, or German. Two reviewers independently decided for or against inclusion based on abstracts. A third reviewer intervened in case of disagreements. From the selected titles, we extracted information regarding the purpose of the manuscript, analyzed calculators, population for which it was calibrated, included risk factors, and the model’s overall accuracy.

Results

We included a total of 18 calculators from 53 different manuscripts. The most commonly analyzed ones were the Prostate Cancer Prevention Trial (PCPT) and European Randomized Study on Prostate Cancer (ERSPC) risk calculators developed from North American and European cohorts, respectively. Both calculators provided high diagnostic ability of aggressive prostate cancer (AUC as high as 0.798 for PCPT and 0.91 for ERSPC). We found 9 calculators developed from scratch for specific populations that reached a diagnostic ability as high as 0.938. The most commonly included risk factors in the calculators were age, prostate specific antigen levels, and digital rectal examination findings. Additional calculators included race and detailed personal and family history.

Conclusions

Both the PCPR and ERSPC risk calculators have been successfully adapted for cohorts other than the ones they were originally created for with no loss of diagnostic ability. Furthermore, designing calculators from scratch considering each population’s sociocultural differences has resulted in risk tools that can be well adapted to be valid in more patients. The best risk calculator for prostate cancer will be that which has been calibrated for its intended population and can be easily reproduced and implemented.

Trial Registration

PROSPERO CRD42021242110; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=242110

Phi-based risk calculators performed better in the prediction of prostate cancer in the Chinese population

Risk prediction models including the Prostate Health Index (phi) for prostate cancer have been well established and evaluated in the Western population. The aim of this study is to build phi-based risk calculators in a prostate biopsy population and evaluate their performance in predicting prostate cancer (PCa) and high-grade PCa (Gleason score ≥7) in the Chinese population. We developed risk calculators based on 635 men who underwent initial prostate biopsy. Then, we validated the performance of prostate-specific antigen (PSA), phi, and the risk calculators in an additional observational cohort of 1045 men. We observed that the phi-based risk calculators (risk calculators 2 and 4) outperformed the PSA-based risk calculator for predicting PCa and high-grade PCa in the training cohort. In the validation study, the area under the receiver operating characteristic curve (AUC) for risk calculators 2 and 4 reached 0.91 and 0.92, respectively, for predicting PCa and high-grade PCa, respectively; the AUC values were better than those for risk calculator 1 (PSA-based model with an AUC of 0.81 and 0.82, respectively) (all P < 0.001). Such superiority was also observed in the stratified population with PSA ranging from 2.0 ng ml^-1to 10.0 ng ml^-1. Decision curves confirmed that a considerable proportion of unnecessary biopsies could be avoided while applying phi-based risk calculators. In this study, we showed that, compared to risk calculators without phi, phi-based risk calculators exhibited superior discrimination and calibration for PCa in the Chinese biopsy population. Applying these risk calculators also considerably reduced the number of unnecessary biopsies for PCa.

Personalized strategies in population screening for prostate cancer

This review discusses evidence for population-based screening with contemporary screening tools. In Europe, prostate-specific antigen (PSA)-based screening led to a relative reduction of prostate cancer (PCa) mortality, but also to a substantial amount of overdiagnosis and unnecessarily biopsies. Risk stratification based on a single variable (a clinical variable or based on the presence of a lesion on prostate imaging) or based on multivariable approaches can aid in reducing unnecessary prostate biopsies and overdiagnosis by selecting men who can benefit from further clinical assessment. Multivariable approaches include clinical variables, and biomarkers, often combined in risk calculators or nomograms. These risk calculators can also incorporate the result of MRI imaging. In general, as compared to a purely PSA based approach, the combination of relevant prebiopsy information results in superior selection of men at higher risk of harboring clinically significant prostate cancer. Currently, it is not possible to draw any conclusions on the superiority of these multivariable risk-based approaches since head-to-head comparisons are virtually lacking. Recently initiated large population-based screening studies in Finland, Germany and Sweden, incorporating various multivariable risk stratification approaches will hopefully give more insight in whether the harm-benefit ratio can be improved, that is, maintain (or improving) the ability to reduce metastatic disease and prostate cancer mortality while reducing harm caused by unnecessary testing and overdiagnosis including related overtreatment.

Comparing a new risk prediction model with prostate cancer risk calculator apps in a Taiwanese population

PURPOSE

To develop a novel Taiwanese prostate cancer (PCa) risk model for predicting PCa, comparing its predictive performance with that of two well-established PCa risk calculator apps.

METHODS

1545 men undergoing prostate biopsies in a Taiwanese tertiary medical center between 2012 and 2019 were identified retrospectively. A five-fold cross-validated logistic regression risk model was created to calculate the probabilities of PCa and high-grade PCa (Gleason score ≧ 7), to compare those of the Rotterdam and Coral apps. Discrimination was analyzed using the area under the receiver operator characteristic curve (AUC). Calibration was graphically evaluated with the goodness-of-fit test. Decision-curve analysis was performed for clinical utility. At different risk thresholds to biopsy, the proportion of biopsies saved versus low- and high-grade PCa missed were presented.

RESULTS

Overall, 278/1309 (21.2%) patients were diagnosed with PCa, and 181 out of 278 (65.1%) patients had high-grade PCa. Both our model and the Rotterdam app demonstrated better discriminative ability than the Coral app for detection of PCa (AUC: 0.795 vs 0.792 vs 0.697, DeLong's method: P < 0.001) and high-grade PCa (AUC: 0.869 vs 0.873 vs 0.767, P < 0.001). Using a ≥ 10% risk threshold for high-grade PCa to biopsy, our model could save 67.2% of total biopsies; among these saved biopsies, only 3.4% high-grade PCa would be missed.

CONCLUSION

Our new logistic regression model, similar to the Rotterdam app, outperformed the Coral app in the prediction of PCa and high-grade PCa. Additionally, our model could save unnecessary biopsies and avoid missing clinically significant PCa in the Taiwanese population.

Explication of the roles of prostate health index (PHI) and urokinase plasminogen activator (uPA) as diagnostic and predictor tools for prostate cancer in equivocal PSA range of 4-10 ng/mL

Background

Prostate cancer (PCa) is one of the most commonly encountered cancers and the leading cause of death worldwide. Currently used biomarkers accounts difficulties in discriminating benign from malignant cases or predicting outcome, so investigating new biomarkers performance is needed.

Objectives

Assessment of diagnostic and predictor roles of prostate health index (PHI) and urokinase plasminogen activator (uPA) in PCa.

Methods

194 males with initial tPSA of 4-10 ng/mL were categorized into three groups: PCa, benign prostatic hyperplasia (BPH) and healthy control. Serum levels of tPSA, fPSA, p2PSA, and uPA were performed by ELISA with calculation of PHI as (p2PSA/fPSA) × √PSA.

Results

PHI and uPA were significantly higher in PCa patients relevant to BPH and healthy control (p ≤ 0.001). Both markers outperformed all assessed biomarkers and showed the highest area under the curve (AUC) in ROC curve analysis. Both were significantly higher in PCa patients with {Gleason score ≥ 7, late stages (cT2b,c; T3), LN extension and distant metastasis}relative to their counterparts. Additionally, PHI and uPA and were independent predictors of distant metastasis and Gleason score ≥ 7, while PHI was predictor of LN invasion (β = 0.25, p = 0.004).

Conclusion

PHI and uPA would be of potential value in discriminating between PCa, BPH and healthy men in addition, both are promising as independent predictors of adverse pathological features.

Beyond PSA: The Role of Prostate Health Index (phi)

Background: Widespread use of prostate specific antigen (PSA) in screening procedures allowed early identification of an increasing number of prostate cancers (PCas), mainly including indolent cancer. Availability of different therapeutic strategies which have a very different impact on the patient’s quality of life suggested a strong need for tools able to identify clinically significant cancer at diagnosis. Multi-parametric magnetic resonance showed very good performance in pre-biopsy diagnosis. However, it is an expensive tool and requires an experienced radiologist. In this context, a simple blood-based test is worth investigating. In this context, researchers focused their attention on the development of a laboratory test able to minimize overdiagnosis without losing the identification of aggressive tumors. Results: Recent literature data on PCa biomarkers revealed a clear tendency towards the use of panels of biomarkers or a combination of biomarkers and clinical variables. Phi, the 4Kscore, and Stockholm3 as circulating biomarkers and the Mi-prostate score, Exo DX Prostate, and Select MD-X as urinary biomarker-based tests have been developed. In this scenario, phi is worthy of attention as a noninvasive test significantly associated with aggressive PCa. Conclusions: Literature data showed that phi had good diagnostic performance to identify clinically significant (cs) PCa, suggesting that it could be a useful tool for personalized treatment decision-making. In this review, phi potentialities, limitations, and comparisons with other blood- and urinary-based tests were explored.

Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis

This review discusses the most recent evidence for currently available risk stratification tools in the detection of clinically significant prostate cancer (csPCa), and evaluates diagnostic strategies that combine these tools. Novel blood biomarkers, such as the Prostate Health Index (PHI) and 4Kscore, show similar ability to predict csPCa. Prostate cancer antigen 3 (PCA3) is a urinary biomarker that has inferior prediction of csPCa compared to PHI, but may be combined with other markers like TMPRSS2-ERG to improve its performance. Original risk calculators (RCs) have the advantage of incorporating easy to retrieve clinical variables and being freely accessible as a web tool/mobile application. RCs perform similarly well as most novel biomarkers. New promising risk models including novel (genetic) markers are the SelectMDx and Stockholm-3 model (S3M). Prostate magnetic resonance imaging (MRI) has evolved as an appealing tool in the diagnostic arsenal with even stratifying abilities, including in the initial biopsy setting. Merging biomarkers, RCs and MRI results in higher performances than their use as standalone tests. In the current era of prostate MRI, the way forward seems to be multivariable risk assessment based on blood and clinical parameters, potentially extended with information from urine samples, as a triaging test for the selection of candidates for MRI and biopsy.

Smarter screening for prostate cancer

PURPOSE

Prostate cancer is the second commonest cancer among men. In the large European Randomized Study of Screening for Prostate Cancer (ERSPC) trial, prostate-specific antigen (PSA) screening has been shown to substantially reduce prostate cancer mortality. However, PSA screening is known to lead to more unnecessary prostate biopsies and over-diagnosis of clinically insignificant cancer. Therefore, it is imperative that smarter screening methods be developed to overcome the weaknesses of PSA screening. This review explores the novel screening tools that are available.

METHODS

A comprehensive literature search was performed using PubMed regarding newer biomarkers, imaging techniques and risk-predicting models that are used to screen for prostate cancer in mainly biopsy-naïve men.

RESULTS

Novel serum-based models like 4Kscore^® and prostate health index (PHI) are generally better than PSA alone in detecting clinically significant cancer. Similarly, urine-based biomarkers like prostate cancer antigen 3 (PCA3) and HOXC6/DLX1 have been shown to be more accurate than PSA screening. More recently, multiparametric magnetic resonance imaging (mpMRI) is gaining popularity for its ability to detect clinically significant cancer. There is also evidence that combining individual tests to develop prediction models can reliably predict high-risk prostate cancers while reducing the number of unnecessary biopsies. Combinations such as the Stockholm-3 model (STHLM3) and other novel combinations are presented in this review.

CONCLUSION

While we continue to find the smarter screening methods that are reliable, precise, and cost-effective, we continue to advocate shared decision-making in prostate cancer screening in order to work in our patients' best interests.

epiCaPture: A Urine DNA Methylation Test for Early Detection of Aggressive Prostate Cancer

Purpose

Liquid biopsies that noninvasively detect molecular correlates of aggressive prostate cancer (PCa) could be used to triage patients, reducing the burdens of unnecessary invasive prostate biopsy and enabling early detection of high-risk disease. DNA hypermethylation is among the earliest and most frequent aberrations in PCa. We investigated the accuracy of a six-gene DNA methylation panel (Epigenetic Cancer of the Prostate Test in Urine [epiCaPture]) at detecting PCa, high-grade (Gleason score greater than or equal to 8) and high-risk (D’Amico and Cancer of the Prostate Risk Assessment] PCa from urine.

Patients and Methods

Prognostic utility of epiCaPture genes was first validated in two independent prostate tissue cohorts. epiCaPture was assessed in a multicenter prospective study of 463 men undergoing prostate biopsy. epiCaPture was performed by quantitative methylation-specific polymerase chain reaction in DNA isolated from prebiopsy urine sediments and evaluated by receiver operating characteristic and decision curves (clinical benefit). The epiCaPture score was developed and validated on a two thirds training set to one third test set.

Results

Higher methylation of epiCaPture genes was significantly associated with increasing aggressiveness in PCa tissues. In urine, area under the receiver operating characteristic curve was 0.64, 0.86, and 0.83 for detecting PCa, high-grade PCa, and high-risk PCa, respectively. Decision curves revealed a net benefit across relevant threshold probabilities. Independent analysis of two epiCaPture genes in the same clinical cohort provided analytical validation. Parallel epiCaPture analysis in urine and matched biopsy cores showed added value of a liquid biopsy.

Conclusion

epiCaPture is a urine DNA methylation test for high-risk PCa. Its tumor specificity out-performs that of prostate-specific antigen (greater than 3 ng/mL). Used as an adjunct to prostate-specific antigen, epiCaPture could aid patient stratification to determine need for biopsy.

Head-to-head comparison of prostate cancer risk calculators predicting biopsy outcome

Background

Multivariable risk calculators (RCs) predicting prostate cancer (PCa) aim to reduce unnecessary workup (e.g., MRI and biopsy) by selectively identifying those men at risk for PCa or clinically significant PCa (csPCa) (Gleason ≥7). The lack of an adequate comparison makes choosing between RCs difficult for patients, clinicians and guideline developers. We aim to perform a head-to-head comparison of seven well known RCs predicting biopsy outcome.

Methods

Our study comprised 7,119 men from ten independent contemporary cohorts in Europe and Australia, who underwent prostate biopsy between 2007 and 2015. We evaluated the performance of the ERSPC RPCRC, Finne, Chun, ProstataClass, Karakiewicz, Sunnybrook, and PCPT 2.0 (HG) RCs in predicting the presence of any PCa and csPCa. Performance was assessed by discrimination, calibration and net benefit analyses.

Results

A total of 3,458 (48%) PCa were detected; 1,784 (25%) men had csPCa. No particular RC stood out predicting any PCa: pooled area under the ROC-curve (AUC) ranged between 0.64 and 0.72. The ERSPC RPCRC had the highest pooled AUC 0.77 (95% CI: 0.73–0.80) when predicting csPCa. Decision curve analysis (DCA) showed limited net benefit in the detection of csPCa, but that can be improved by a simple calibration step. The main limitation is the retrospective design of the study.

Conclusions

No particular RC stands out when predicting biopsy outcome on the presence of any PCa. The ERSPC RPCRC is superior in identifying those men at risk for csPCa. Net benefit analyses show that a multivariate approach before further workup is advisable.

Novel Biomarkers for Prostate Cancer Detection and Prognosis

Prostate cancer (PCa) remains as one of the most controversial issues in health care because of the dilemmas related to screening using Prostate Specific Antigen (PSA). A high number of false positive biopsies and an elevated rate of overdiagnosis are the main problems associated with PSA. New PCa biomarkers have been recently proposed to increase the predictive value of PSA. The published results showed that PCA3 score, Prostate Health Index and 4Kscore can reduce the number of unnecessary biopsies, outperforming better than PSA and the percentage of free PSA. Furthermore, 4Kscore provides with high accuracy an individual risk for high-grade PCa. High values of PHI are also associated with tumor aggressiveness. In contrast, the relationship of PCA3 score with aggressiveness remains controversial, with studies showing opposite conclusions. Finally, the development of molecular biology has opened the study of genes, among them TMPRSS2:ERG fusion gene and miRNAs, in PCa detection and prognosis.

Biomarkers for Prostate Biopsy and Risk Stratification of Newly Diagnosed Prostate Cancer Patients

INTRODUCTION

Many new markers are now available as an aid for decisions about prostate biopsy for men without prostate cancer, and/or to improve risk stratification for men with newly diagnosed prostate cancer.

METHODS

A literature review was performed on currently available markers for use in decisions about prostate biopsy and initial prostate cancer treatment.

RESULTS

Although total prostate-specific antigen cutoffs were traditionally used for biopsy decisions, PSA elevations are not specific. Repeating the PSA test, and adjusting for factors like age, prostate volume and changes over time can increase specificity for biopsy decisions. The Prostate Health Index (phi) and 4K Score are new PSA-based markers that can be offered as second-line tests to decide on initial or repeat prostate biopsy. The PCA3 urine test and ConfirmMDx tissue test are additional options for repeat biopsy decisions. For men with newly diagnosed prostate cancer, genomic tests are available to refine risk classification and may influence treatment decisions.

CONCLUSIONS

Numerous secondary testing options are now available that can be offered to patients deciding whether to undergo prostate biopsy and those with newly diagnosed prostate cancer.

Prostate Cancer Detection and Prognosis: From Prostate Specific Antigen (PSA) to Exosomal Biomarkers

Prostate specific antigen (PSA) remains the most used biomarker in the management of early prostate cancer (PCa), in spite of the problems related to false positive results and overdiagnosis. New biomarkers have been proposed in recent years with the aim of increasing specificity and distinguishing aggressive from non-aggressive PCa. The emerging role of the prostate health index and the 4Kscore is reviewed in this article. Both are blood-based tests related to the aggressiveness of the tumor, which provide the risk of suffering PCa and avoiding negative biopsies. Furthermore, the use of urine has emerged as a non-invasive way to identify new biomarkers in recent years, including the PCA3 and TMPRSS2:ERG fusion gene. Available results about the PCA3 score showed its usefulness to decide the repetition of biopsy in patients with a previous negative result, although its relationship with the aggressiveness of the tumor is controversial. More recently, aberrant microRNA expression in PCa has been reported by different authors. Preliminary results suggest the utility of circulating and urinary microRNAs in the detection and prognosis of PCa. Although several of these new biomarkers have been recommended by different guidelines, large prospective and comparative studies are necessary to establish their value in PCa detection and prognosis.

Prostate health index (PHI) and prostate-specific antigen (PSA) predictive models for prostate cancer in the Chinese population and the role of digital rectal examination-estimated prostate volume

PURPOSE

To investigate PSA- and PHI (prostate health index)-based models for prediction of prostate cancer (PCa) and the feasibility of using DRE-estimated prostate volume (DRE-PV) in the models.

METHODS

This study included 569 Chinese men with PSA 4-10 ng/mL and non-suspicious DRE with transrectal ultrasound (TRUS) 10-core prostate biopsies performed between April 2008 and July 2015. DRE-PV was estimated using 3 pre-defined classes: 25, 40, or 60 ml. The performance of PSA-based and PHI-based predictive models including age, DRE-PV, and TRUS prostate volume (TRUS-PV) was analyzed using logistic regression and area under the receiver operating curves (AUC), in both the whole cohort and the screening age group of 55-75.

RESULTS

PCa and high-grade PCa (HGPCa) was diagnosed in 10.9 % (62/569) and 2.8 % (16/569) men, respectively. The performance of DRE-PV-based models was similar to TRUS-PV-based models. In the age group 55-75, the AUCs for PCa of PSA alone, PSA with DRE-PV and age, PHI alone, PHI with DRE-PV and age, and PHI with TRUS-PV and age were 0.54, 0.71, 0.76, 0.78, and 0.78, respectively. The corresponding AUCs for HGPCa were higher (0.60, 0.70, 0.85, 0.83, and 0.83). At 10 and 20 % risk threshold for PCa, 38.4 and 55.4 % biopsies could be avoided in the PHI-based model, respectively.

CONCLUSIONS

PHI had better performance over PSA-based models and could reduce unnecessary biopsies. A DRE-assessed PV can replace TRUS-assessed PV in multivariate prediction models to facilitate clinical use.