Prospective multicenter evaluation of the Beckman Coulter Prostate Health Index using WHO calibration

Modern predictors and management of incidental prostate cancer at holmium enucleation of prostate

BACKGROUND

To evaluate contemporary preoperative risk factors and subsequent postoperative management of incidental prostate cancer (iPCa) and incidental clinically significant prostate cancer (icsPCa, Grade Group [GG] ≥ 2 PCa).

METHODS

A retrospective cohort of 811 men undergoing Holmium enucleation of the prostate (HoLEP) (January 2021-July 2022) were identified. Advanced preoperative testing was defined as prostate health index (PHI), prostate MRI, and/or negative preoperative biopsy. Descriptive statistics (Whitney-Mann U test, Chi-squared test) and multivariable logistic regression were performed.

RESULTS

iPCa and icsPCa detection rates were 12.8% (104/811) and 4.4% (36/811), respectively. Advanced preoperative testing (406/811, 50%) was associated with younger age and higher (prostate specific antigen) PSA, prostate volume, and PSA density. On multivariable analysis, PHI ≥ 55 was associated with iPCa (OR 6.91, 95% CI 1.85-26.3, p = 0.004), and % free PSA (%fPSA) was associated with icsPCa (OR 0.83, 95% CI 0.67, 0.94, p = 0.01). GG1 disease comprised the majority of iPCa (65%, 68/104) with median 1% involvement. iPCa patients were followed with active surveillance (median follow up 9.3 months), with higher risk patients receiving prostate MRI and confirmatory biopsy. Three patients proceeded to radical prostatectomy or radiation.

CONCLUSIONS

In the era of MRI and advanced biomarkers, the majority of iPCa following HoLEP is low volume GG1 suitable for active surveillance. A tentative follow-up strategy is proposed. Patients with PHI ≥ 55 or low %fPSA, even with negative prostate MRI, can consider preoperative prostate biopsy before HoLEP.

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.

Advancing Traditional Prostate-specific Antigen Kinetics in the Detection of Prostate Cancer: A Machine Learning Model

BACKGROUND

Prostate-specific antigen (PSA) kinetics, defined as the change in PSA over time, may be of use as a predictor of prostate cancer. PSA kinetics can be assessed as the PSA velocity, which is traditionally evaluated dichotomously and classified as abnormal if greater than either 0.35 or 0.75 ng/ml/yr. Machine learning models may provide additional benefit in assessing risk using PSA kinetics instead of PSA velocity.

OBJECTIVE

To improve the utility of PSA kinetics by constructing a generalizable, universal machine learning model.

DESIGN, SETTING, AND PARTICIPANTS

Data were obtained from the PLCO and PCPT trials and from a contemporary Australian cohort. PSA data were interpolated using a modified Gaussian process. A machine learning model based on a two-headed approach was designed, in which the multivariable input was fed into a one-dimensional ResNet18 model.

OUTCOME MEASURES AND STATISTICAL ANALYSIS

The model performance was assessed compared to PSA levels and PSA velocity in terms of area under the receiver operator characteristic curve (AUC).

RESULTS AND LIMITATIONS

A total of 10719 patients were included in the analysis. In tests on a validation set of the complete database to diagnose grade group ≥2, the AUC was 0.886 (95% confidence interval [CI] 0.870-0.902) for the machine learning model, compared to 0.807 (95% CI 0.796-0.819) for PSA and 0.627 (95% CI 0.607-0.648) for PSA velocity.

CONCLUSIONS

Machine learning models can be used to augment the diagnostic utility of PSA kinetics in the diagnosis of prostate cancer. We demonstrated significant improvements in accuracy compared to the traditional approaches of PSA velocity and PSA thresholds.

PATIENT SUMMARY

Prostate cancer diagnosis is limited by the diagnostic accuracy of the prostate-specific antigen (PSA) blood test. Advances in techniques such as machine learning algorithms can greatly improve the diagnostic accuracy of prostate cancer screening without additional costs or tests.

Biomarkers for detection of clinically significant prostate cancer: contemporary clinical data and future directions

Use of serum prostate-specific antigen (PSA) testing for early detection of prostate cancer appears to reduce cancer-specific mortality. Due to the limited specificity of PSA for clinically significant [Grade Group (GG) ≥2] cancer, however, screening carries substantial risks, including frequent unnecessary prostate biopsies and overdetection of non-aggressive cancers. To that end, serum and urine biomarkers with improved specificity for GG ≥2 cancer have been proposed for clinical use following PSA. In the current article, we present clinical validation data for five such biomarkers: PHI, 4Kscore, SelectMDx, ExoDx, and MPS. For all studies, we specify the study population (overall biopsy referral vs. pre-specified PSA ranges), previous biopsy status (biopsy-naïve vs. previous negative biopsy), and the proportion of subjects diagnosed with GG ≥2 cancer. Outcomes include test performance characteristics: sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Published data were used to compute the number of unnecessary biopsies avoided and number of GG ≥2 cancers missed if the biomarker had been used clinically to select for prostate biopsy. The evidence review is preceded by a primer on these and other clinically-relevant summary statistics.

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.

Diagnostic accuracy of [-2]proPSA versus Gleason score and Prostate Health Index versus Gleason score for the determination of aggressive prostate cancer: a systematic review

OBJECTIVE

The objective of this review was to determine the diagnostic accuracy of [-2]proPSA (p2PSA) and the Prostate Health Index compared to the Gleason score in determining the aggressiveness of prostate cancer.

INTRODUCTION

Prostate cancer is the most commonly diagnosed cancer in men. However, the utility of currently available biomarkers for determining the aggressive form of the disease remains unknown. This review sought to determine the diagnostic accuracy of two new biomarkers in determining the aggressive form of prostate cancer.

INCLUSION CRITERIA

Diagnostic accuracy studies that enrolled men of any age and any prostate specific antigen (PSA) level with histologically confirmed prostate cancer in which Prostate Health Index and p2PSA were assessed in comparison to Gleason score for the determination of aggressive prostate cancer were considered for inclusion. There was no time limitation on study inclusion.

METHODS

A three-step search strategy was utilized to identify both published and unpublished studies in the English language in the following sources: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science, Google Scholar, MedNar, and SIGLE. Databases were searched from inception to January 2019. Study selection, critical appraisal, data extraction, and data synthesis were done according to the approach recommended by JBI.

RESULTS

A total of 12 studies (n = 8462) that recruited men with aggressive prostate cancer were considered in this review. The majority of included subjects had a total PSA level of 2 to 10ng/mL. The sensitivity of the Prostate Health Index ranged from 67% to 97% while specificity ranged from 6% to 64%. At a Prostate Health Index threshold of 25 and below (three studies, n = 3222), pooled sensitivity was 97% (95% confidence interval [CI], 95% to 98%) and specificity was 10% (95% CI, 6% to 16%). At a Prostate Health Index threshold of between 26 and 35 (six studies, n = 6030), pooled sensitivity was 87% (95% CI, 8% to 91%) and specificity was 45% (95% CI, 39% to 50%). At a Prostate Health Index threshold of 36 and above (five studies, n = 1476), pooled sensitivity was 72% (95% CI, 64% to 79%) and specificity was 74% (95% CI, 68% to 80%). Only one study assessed p2PSA. Sensitivity ranged from 80% to 95%, and specificity ranged from 9.9% to 27.9% with increasing threshold values from 7.9 to 10.9ng/mL.

CONCLUSIONS

Overall, both Prostate Health Index and p2PSA have acceptable accuracy for the determination of the likelihood of aggressive prostate cancer. However, the inverse relationship between sensitivity and specificity makes it difficult to determine an optimum cut-off value for positivity. Further research is warranted to determine their utility in the management of prostate cancer.

PSA-based machine learning model improves prostate cancer risk stratification in a screening population

CONTEXT

The majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.

METHODS

Data were collated from a local institution and combined with patient data retrieved from the Prostate, Lung, Colorectal and Ovarian Cancer screening Trial (PLCO) database. Using a dataset of 4548 patients, a machine learning model was developed and trained using PSA, free-PSA, age and free-PSA to total PSA (FTR) ratio.

RESULTS

The model was trained on a dataset involving 3638 patients and was then tested on a separate set of 910 patients. The model improved prediction for prostate cancer (AUC 0.72) compared to PSA alone (AUC 0.63), age (AUC 0.52), free-PSA (AUC 0.50) and FTR alone (AUC 0.65). When an operating point is chosen such that the sensitivity of the model is 80% the specificity of the model is 45.3%. The benefit in AUC secondary to the model was related to sample size, with AUC of 0.64 observed when a subset of the cohort was assessed.

CONCLUSIONS

Development of a dense neural network model improved the diagnostic accuracy in screening for prostate cancer. These results demonstrate an additional utility of machine learning methods in prostate cancer risk stratification when using biochemical parameters.

Prediction models for prostate cancer to be used in the primary care setting: a systematic review

OBJECTIVE

To identify risk prediction models for prostate cancer (PCa) that can be used in the primary care and community health settings.

DESIGN

Systematic review.

DATA SOURCES

MEDLINE and Embase databases combined from inception and up to the end of January 2019.

ELIGIBILITY

Studies were included based on satisfying all the following criteria: (i) presenting an evaluation of PCa risk at initial biopsy in patients with no history of PCa, (ii) studies not incorporating an invasive clinical assessment or expensive biomarker/genetic tests, (iii) inclusion of at least two variables with prostate-specific antigen (PSA) being one of them, and (iv) studies reporting a measure of predictive performance. The quality of the studies and risk of bias was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

DATA EXTRACTION AND SYNTHESIS

Relevant information extracted for each model included: the year of publication, source of data, type of model, number of patients, country, age, PSA range, mean/median PSA, other variables included in the model, number of biopsy cores to assess outcomes, study endpoint(s), cancer detection, model validation and model performance.

RESULTS

An initial search yielded 109 potential studies, of which five met the set criteria. Four studies were cohort-based and one was a case-control study. PCa detection rate was between 20.6% and 55.8%. Area under the curve (AUC) was reported in four studies and ranged from 0.65 to 0.75. All models showed significant improvement in predicting PCa compared with being based on PSA alone. The difference in AUC between extended models and PSA alone was between 0.06 and 0.21.

CONCLUSION

Only a few PCa risk prediction models have the potential to be readily used in the primary healthcare or community health setting. Further studies are needed to investigate other potential variables that could be integrated into models to improve their clinical utility for PCa testing in a community setting.

New biomarkers for diagnosis and prognosis of localized prostate cancer

The diagnostics and management of localized prostate cancer is complicated because of cancer heterogeneity and differentiated progression in various subgroups of patients. As a prostate cancer biomarker, FDA-approved detection assay for serum prostate specific antigen (PSA) and its derivatives are not potent enough to diagnose prostate cancer, especially high-grade disease (Gleason ≥7). To date, a collection of new biomarkers was developed. Some of these markers are superior for primary screening while others are particularly helpful for cancer risk stratification, detection of high-grade cancer, and prediction of adverse events. Two of those markers such as proPSA (a part of the Prostate Health Index (PHI)) and prostate specific antigen 3 (PCA3) (a part of the PCA3 Progensa test) were recently approved by FDA for clinical use. Other markers are not PDA-approved yet but are available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories. In this review, we characterize diagnostic performance of these markers and their diagnostic and prognostic utility for prostate cancer.

A single mitochondrial DNA deletion accurately detects significant prostate cancer in men in the PSA 'grey zone'

Purpose

To determine the clinical performance of a blood-based test for clinically significant (CS) prostate cancer (PCa) (grade group ≥ 2) intended for use in men with prostate serum antigen levels in the ‘grey zone’ (PSA < 10 ng/ml). The test quantifies a previously described 3.4 kb mitochondrial DNA (mtDNA) deletion.

Methods

In a first prospective study of an MRI-guided re-biopsy population (n = 126), the 3.4 kb deletion and 18S rRNA gene were amplified from plasma. A diagnostic threshold was selected from the coordinates of the receiver operating characteristic curve and tested in a second population of men who were (n = 92) biopsy naïve when the mtDNA deletion was assayed and for whom those diagnosed with cancer on initial biopsy were treated with radical prostatectomy.

Results

The 3.4 kb deletion was a good predictor of CS PCa in the image-guided re-biopsy population [AUC 0.84, (95% CI 0.73–0.95)] and the selected threshold corresponded to a sensitivity of 87% [95% CI, 70–96%], specificity of 68% [95% CI, 47–85%] and negative predictive value (NPV) of 97%. Applying this threshold to the second population showed this deletion to be a strong predictor of CS cancer [AUC 0.98, (95% CI 0.94–1.02)], independent of PSA or age [sensitivity 100% (95% CI, 93–100%), specificity 90% (95%CI 73–98%) and NPV 100%].

Conclusion

The 3.4 kb deletion in plasma is an accurate predictor of CS cancer for men in the PSA ‘grey zone’. Used in advance of biopsy for improved patient selection, this deletion may reduce the number of biopsies needed to diagnose CS prostate cancers.

Electronic supplementary material

The online version of this article (10.1007/s00345-017-2152-z) contains supplementary material, which is available to authorized users.

The prostate health index PHI predicts oncological outcome and biochemical recurrence after radical prostatectomy - analysis in 437 patients

The purpose of this study was to investigate the Prostate-Health-Index (PHI) for pathological outcome prediction following radical prostatectomy and also for biochemical recurrence prediction in comparison to established parameters such as Gleason-score, pathological tumor stage, resection status (R0/1) and prostate-specific antigen (PSA).

Out of a cohort of 460 cases with preoperative PHI-measurements (World Health Organization calibration: Beckman Coulter Access-2-Immunoassay) between 2001 and 2014, 437 patients with complete follow up data were included. From these 437 patients, 87 (19.9%) developed a biochemical recurrence. Patient characteristics were compared by using chi-square test. Predictors were analyzed by multivariate adjusted logistic and Cox regression.

The median follow up for a biochemical recurrence was 65 (range 3-161) months. PHI, PSA, [-2]proPSA, PHI- and PSA-density performed as significant variables (p < 0.05) for cancer aggressiveness: Gleason-score <7 or ≥7 (ISUP grade 1 or ≥2) . Concerning pathological tumor stage discrimination and prediction, variables as PHI, PSA, %fPSA, [-2]proPSA, PHI- and PSA-density significantly discriminated between stages <pT3 and ≥pT3 with the highest AUC (0.7) for PHI. In biochemical recurrence prediction PHI, PSA, [-2]proPSA, PHI- and PSA-density were the strongest predictors.

In conclusion, due to heterogeneity of time spans to biochemical recurrence, longer follow up periods are crucial. This study with a median follow up of more than 5 years, confirmed a clinical value for PHI as an independent biomarker essential for biochemical recurrence prediction.

Performance of the Prostate Health Index in predicting prostate biopsy outcomes among men with a negative digital rectal examination and transrectal ultrasonography

The [-2]proPSA (p2PSA) and its derivatives, the p2PSA-to-free PSA ratio (%p2PSA), and the Prostate Health Index (PHI) have greatly improved discrimination between men with and without prostate cancer (PCa) in prostate biopsies. However, little is known about their performance in cases where a digital rectal examination (DRE) and transrectal ultrasonography (TRUS) are negative. A prospective cohort of 261 consecutive patients in China with negative DRE and TRUS were recruited and underwent prostate biopsies. A serum sample had collected before the biopsy was used to measure various PSA derivatives, including total prostate-specific antigen (tPSA), free PSA, and p2PSA. For each patient, the free-to-total PSA ratio (%fPSA), PSA density (PSAD), p2PSA-to-free PSA ratio (%p2PSA), and PHI were calculated. Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC) and the biopsy rate at 91% sensitivity. The AUC scores within the entire cohort with respect to age, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI were 0.598, 0.751, 0.646, 0.789, 0.814, 0.808, and 0.853, respectively. PHI was the best predictor of prostate biopsy results, especially in patients with a tPSA of 10.1-20 ng ml-1 . Compared with other markers, at a sensitivity of 91%, PHI was the most useful for determining which men did not need to undergo biopsy, thereby avoiding unnecessary procedures. The use of PHI could improve the accuracy of PCa detection by predicting prostate biopsy outcomes among men with a negative DRE and TRUS in China.

Screening and Detection of Prostate Cancer-Review of Literature and Current Perspective

Screening of prostatic cancer is a matter of debate among uro-oncologist. With many new screening modalities like prostatic health index (PHI), 4K testing the role of screening has increased as one is able to stratify patients with serum prostate specific antigen level in a grey zone of 4-10 ng/ml and normal digital rectal examination into various risk groups, thus avoiding unnecessary biopsy which was the pitfalls of routine screening practice. PHI is better at predicting malignancy while 4K is better at predicting high-grade disease. This in combination with multiparametric MRI especially with prostate imaging reporting and data system score has made screening less difficult and more meaningful for a practising uro-oncologist.

Clinical Utility of Biomarkers in Localized Prostate Cancer

A new generation of prostate cancer (PCa) biomarkers has emerged, including diagnostic serum and urine markers aimed at refining the identification high-grade tumors and tissue-based gene expression assays offering prognostic and predictive clinical information. Such tests seek to improve treatment-related decisions at multiple decision points, including initial diagnosis and following initial primary therapy. In this review, we aim to contextualize the body of evidence surrounding these emerging tests, with attention on studies addressing clinical utility.

What's new in screening in 2015?

PURPOSE OF REVIEW

The aim of this review was to highlight important articles in the field of prostate cancer screening published during 2015 and early 2016. Four major areas were identified for the purpose: screening strategies, post-United States Preventive Services Task Force (USPSTF) 2011-2012, screening trends/patterns, and shared decision making.

RECENT FINDINGS

Several studies furthered the evidence that screening reduces the risk of metastasis and death from prostate cancer. Multiplex screening strategies are of proven benefit; genetics and MRI need further evaluation. Prostate-specific antigen (PSA) screening rates declined in men above age of 50 years, as did the overall prostate cancer incidence following the USPSTF 2011-2012 recommendation against PSA. The consequences of declining screening rates will become apparent in the next few years. More research is needed to identify the most optimal approach to engage in, and implement, an effective shared decision-making in clinical practice.

SUMMARY

Data emerging in 2015 provided evidence on the question of how best to screen and brought more steps in the right direction of 'next-generation prostate cancer screening'. Screening is an ongoing process in all men regardless of whether or not they might benefit from early detection and treatment. After the USPSTF 2011-2012 recommendation, the rates of PSA testing are declining; however, this decline is observed in all men and not solely in those who will not benefit from the screening. The long-term effect of this recommendation might not be as anticipated. More studies are needed on how to implement the best available evidence on who, and when, to screen in clinical practice.

Role of biobanking in urology: a review

In the current era of individualized medicine, a biorepository of human samples is essential to support clinical and translational research. There have been limited efforts in this arena within the field of urology, as cost, logistical and ethical issues represent significant deterrents to biobanking. The Johns Hopkins Brady Urological Institute Biorepository was founded in 1994 as a resource to facilitate discovery. Since its inception, the biorepository has enabled numerous research endeavours including pivotal trials leading to the regulatory approval of four diagnostic tests for prostate cancer. In the present review, we discuss the current state of biobanking within urology, outline the specific ethical and financial challenges to biobanking as well as solutions, and describe the operations of a successful urological biorepository.

Personalized prostate cancer care: from screening to treatment

Unprecedented progress has been made in genomic personalized medicine in the last several years, allowing for more individualized healthcare assessments and recommendations than ever before. However, most of this progress in prostate cancer (PCa) care has focused on developing and selecting therapies for late-stage disease. To address this issue of limited focus, we propose a model for incorporating genomic-based personalized medicine into all levels of PCa care, from prevention and screening to diagnosis, and ultimately to the treatment of both early-stage and late-stage cancers. We have termed this strategy the "Pyramid Model" of personalized cancer care. In this perspective paper, our objective is to demonstrate the potential application of the Pyramid Model to PCa care. This proactive and comprehensive personalized cancer care approach has the potential to achieve three important medical goals: reducing mortality, improving quality of life and decreasing both individual and societal healthcare costs.

Immunochemical Assays and Nucleic-Acid Detection Techniques for Clinical Diagnosis of Prostate Cancer

Prostate cancer (PCa) is a significant cause of morbidity and mortality and the most common cancer in men in Europe, North America, and some parts of Africa. The established methods for detecting PCa are normally based on tests using Prostate Specific Antigen (PSA) in blood, Prostate cancer antigen 3 (PCA3) in urine and tissue Alpha-methylacyl-CoA racemase (AMACR) as tumour markers in patient samples. Prior to the introduction of PSA in clinics, prostatic acid phosphatase (PAP) was the most widely used biomarker. An early diagnosis of PCa through the detection of these biomarkers requires the availability of simple, reliable, cost-effective and robust techniques. Immunoassays and nucleic acid detection techniques have experienced unprecedented growth in recent years and seem to be the most promising analytical tools. This growth has been driven in part by the surge in demand for near-patient-testing systems in clinical diagnosis. This article reviews immunochemical assays, and nucleic-acid detection techniques that have been used to clinically diagnose PCa.

Comprehensive assessment for novel prostate cancer markers in the prostate-specific antigen era: focusing on Asians and Asian countries

We reviewed the current evidence for three novel prostate tumor markers (PCA3, TMPRSS2:ERG and proPSA) that have been recently reported predominantly in Western countries. We focus our attention on Asian men in both clinical and basic research studies. There have been no reports on the clinical usefulness of these three markers for Asians living in Western countries. In Asian countries, evidence for the clinical usefulness of PCA3 and proPSA-related indices including Prostate Health Index is being accumulated, mainly in Japan. The process for how a novel marker is approved in the clinical setting is also discussed.

Approaches for Initial Prostate Biopsy and Antibiotic Prophylaxis

CONTEXT

Debate on the optimal technique to use as an initial prostate biopsy (PB) strategy is continually evolving.

OBJECTIVE

To review recent advances and current recommendations regarding initial PB and antibiotic prophylaxis.

EVIDENCE ACQUISITION

A nonsystematic review of the literature was performed up to October 2014 using the PubMed and Embase databases. Articles were selected with preference for the highest level of evidence in publications within the past 5 yr.

EVIDENCE SYNTHESIS

The decision to perform PB is still based on an abnormal digital rectal examination or increased prostate0specific antigen (PSA) level without clear consensus about the absolute cutoff. Several biomarkers have been suggested to improve PSA-based PB decision-making and minimize overdiagnosis and overtreatment. The random 12-core transrectal (TR) ultrasound-guided approach remains the standard-of-care technique for PB. A >12-core scheme may be considered as an alternative in a single patient given his clinical features (large volume, low PSA levels). Transperineal biopsies may only be considered as an alternative to the TR route in special situations. Nevertheless, given the increase in antimicrobial resistance, the impact on the post-biopsy sepsis rate should be assessed in well-designed clinical trials. Imaging-guided targeted PB strategies, combined or not with random PBs, may represent the future of prostate cancer diagnosis by reducing the number of PBs and improving decision-making.

CONCLUSIONS

The 12-core TR scheme remains the standard of care for initial PB. The actual trend for PB strategy, with the aim of avoiding overdiagnosis of very low-risk cancers, could rapidly change our current indications and techniques through new biomarkers and imaging-guided targeted strategies. Nevertheless, the cost-benefit balance of these techniques should be closely assessed in the setting of initial PB strategy.

PATIENT SUMMARY

This review highlights current recommendations for prostate biopsy and possible advances in the near future.

The Xu's chart for prostate biopsy: a visual presentation of the added value of biomarkers to prostate-specific antigen for estimating detection rates of prostate cancer

Elevated serum prostate-specific antigen (PSA) level is the primary indication for prostate biopsy for detection of prostate cancer (PCa) in the modern era. The detection rate of PCa from biopsy is typically below 30%, especially among patients with PSA levels at 4–10 ng ml⁻¹. In the past several years, additional biomarkers, such as Prostate Health Index, PCA3 and genetic risk score (GRS) derived from multiple PCa risk-associated single nucleotide polymorphisms (SNPs) have been shown to provide added value to PSA in discriminating prostate biopsy outcomes. However, the adoption rate of these novel biomarkers in clinics is low, largely due to poor understanding of the added value of novel biomarkers. To address this matter, we developed a chart to visually present (i) expected detection rates of PCa from biopsy with respect to PSA levels, and more importantly, (ii) a range of PCa detection rates at the same PSA levels when novel biomarkers are considered. This chart, called the Xu's chart for prostate biopsy, is not a formal risk prediction model; rather, a simple visual tool for urologists to communicate with their patients an initial evaluation of PCa detection rate based on their PSA levels and a possible recommendation for additional biomarkers. A more comprehensive evaluation of PCa risk using existing risk assessment tools such as nomograms can be followed once additional biomarkers are measured. The current version of the chart is only a prototype and should be further developed to include the detection rate of aggressive PCa, and validated in larger studies.

Urinary biomarkers for prostate cancer

PURPOSE OF REVIEW

The field of urology has been beset by several major trends that have affected the early detection of prostate cancer. These stem primarily from a backlash against overdiagnosis due to prostate specific antigen-based screening efforts and are epitomized by the US Preventive Services Task Force giving prostate specific antigen-based prostate cancer screening a 'D' recommendation. Consequently, the active surveillance strategy for low-risk prostate cancer has become commonplace, leading many to ask how best to follow these patients. More importantly, this public outcry has shifted the focus of early detection from an effort to diagnose any and all prostate cancers to an effort to diagnose only 'high-risk' cancer. Along with a trend for minimally invasive procedures, these forces have challenged the early detection field to more efficiently identify clinically significant prostate cancers at an early stage while limiting the number of biopsies.

RECENT FINDINGS

With US Food and Drug Administration approval, prostate cancer antigen 3 has emerged as the first bona-fide urinary biomarker for prostate cancer. Using the same platform, investigators have developed a second urinary test based on TMPRSS2:erg fusion. Recent literature supports the use of these biomarkers as a combined panel that improves risk evaluation in the setting of prostate cancer detection. Early works for applying urinary biomarkers for active surveillance are underway. Other biomarkers in the pipeline will require further prevalidation and validation work.

SUMMARY

Recent literature would support that urinary biomarkers have a clear role to supplement risk evaluation for men undergoing prostate biopsy and for prognostication.

Cancer proteomics: developments in technology, clinical use and commercialization

In the last two decades, advances in genomic, transcriptomic and proteomic methods have enabled us to identify and classify cancers by their molecular profiles. Many anticipate that a molecular taxonomy of cancer will not only lead to more effective subtyping of cancers but also earlier diagnoses, more informative prognoses and more targeted treatments. This article reviews recent technological developments in the field of proteomics, recent discoveries in proteomic cancer biomarker research and trends in clinical use. Readers are also informed of examples of successful commercialization, and the future of proteomics in cancer diagnostics.

Prostate health index vs percent free prostate-specific antigen for prostate cancer detection in men with "gray" prostate-specific antigen levels at first biopsy: systematic review and meta-analysis

The most promising approach to improve the specificity of prostate-specific antigen (PSA) test relies on the measurement of different molecular isoforms of PSA in serum. Currently, in men with a total PSA (tPSA) level between 2 and 10 ng/mL, measurement of %fPSA (free to total PSA ratio ×100) is used as reflex testing to better distinguish between malignant and benign prostate disease. Recently, Beckman Coulter developed the prostate health index (PHI) and several studies suggested that this test may improve the diagnostic ability of %fPSA.We performed a meta-analysis to evaluate the usefulness of PHI compared with %fPSA in the detection of prostate cancer (PCa) at first biopsy in men with tPSA "gray" levels of 2-10 ng/mL. Data on sensitivity and specificity were extracted from 8 eligible studies. Only observational studies comparing the diagnostic ability of PHI and %fPSA in tPSA range of 2-10 ng/mL were included. A total of 8 studies involving 2969 patients with a tPSA range of 2-10 ng/mL undergoing first biopsy were included in this meta-analysis. Biopsy-confirmed PCa was detected in 1287 (43.3%) men. Selected studies determined both PHI and %fPSA as a reflex test. The areas under curve (AUCs) of PHI and %fPSA were 0.74 (95% confidence interval [CI], 0.70-0.77) and 0.63 (95% CI, 0.58-0.67), respectively. Meta-regression analysis confirmed the superiority of PHI which showed, compared with %fPSA, a relative diagnostic odds ratio of 2.81 (95% CI, 2.19-3.6; P < 0.0001). In conclusion, PHI instead of %fPSA as a reflex test in men with tPSA "gray" levels is a better predictor of positive first biopsy and can offer a reduction in unnecessary biopsies.

Biomarkers in prostate cancer: what's new?

PURPOSE OF REVIEW

This review is intended to provide an overview of the current state of biomarkers for prostate cancer (PCa), with a focus on biomarkers approved by the US Food and Drug Administration (FDA) as well as biomarkers available from Clinical Laboratory Improvement Amendment (CLIA)-certified clinical laboratories within the last 1-2 years.

RECENT FINDINGS

During the past 2 years, two biomarkers have been approved by the US FDA. These include proPSA as part of the Prostate Health Index (phi) by Beckman Coulter, Inc and PCA3 as Progensa by Gen Probe, Inc. With the advances in genomic and proteomic technologies, several new CLIA-based laboratory-developed tests have become available. Examples are Oncotype DX from Genomics Health, Inc, and Prolaris from Myriad Genetics, Inc. In most cases, these new tests are based on a combination of multiple genomic or proteomic biomarkers.

SUMMARY

Several new tests, as discussed in this review, have become available during the last 2 years. Although the intended use of most of these tests is to distinguish PCa from benign prostatic conditions with better sensitivity and specificity than prostate-specific antigen, studies have shown that some of them may also be useful in the differentiation of aggressive from nonaggressive forms of PCa.

Performance of serum prostate-specific antigen isoform [-2]proPSA (p2PSA) and the prostate health index (PHI) in a Chinese hospital-based biopsy population

BACKGROUND

The use of serum [-2]proPSA (p2PSA) and its derivative, the prostate health index (PHI), in detecting prostate cancer (PCa) have been consistently shown to have better performance than total prostate-specific antigen (tPSA) in discriminating biopsy outcomes in western countries. However, little is known about their performance in Chinese men. Our objective is to test the performance of p2PSA and PHI and their added value to tPSA in discriminating biopsy outcomes in Chinese men.

METHODS

Consecutive patients who underwent prostate biopsy in three tertiary hospitals in Shanghai, China during 2012-2013 were recruited. Serum tPSA, free PSA (fPSA), and p2PSA were measured centrally using Beckman Coulter's DxI 800 Immunoassay System. The primary outcome is PCa and the secondary outcome is high-grade PCa (Gleason Score of 4 + 3 or worse). Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC), detection rate and Decision Curve Analysis (DCA).

RESULTS

Among 636 patients who underwent prostate biopsy, PHI was a significant predictor of biopsy outcomes, independent of other clinical variables. The AUC in discriminating PCa from non-PCa was consistently higher for PHI than tPSA in the entire cohort (0.88 vs. 0.81) as well as in patients with tPSA at 2-10 ng/ml (0.73 vs. 0.53), at 10.1-20 ng/ml (0.81 vs. 0.58), and at tPSA >20 ng/ml (0.90 vs. 0.80). The differences were statistically significant in all comparisons, P < 0.01. To detect 90% of all PCa in the cohort, 362 and 457 patients would need to be biopsied based on PHI and tPSA cutoff, respectively, a 21% reduction for PHI. Similar results were found for discriminating high-grade PCa.

CONCLUSIONS

PHI provides added value over tPSA in discriminating PCa and high-grade PCa in patients who underwent prostate biopsy in China.

Clinical use of [-2]proPSA (p2PSA) and its derivatives (%p2PSA and Prostate Health Index) for the detection of prostate cancer: a review of the literature

Prostate-specific antigen (PSA) is recognized as an organ-specific marker with low specificity and sensitivity in discriminating prostate cancer (PCa) from other benign conditions, such as prostatic hyperplasia or chronic prostatitis. Thus, in the case of clinical suspicion, a PCa diagnosis cannot be made without a prostate biopsy. [-2]proPSA (p2PSA), a precursor of PSA, has been investigated as a new marker to accurately detect PCa. The aim of this systematic review was to discuss the available literature regarding the clinical validity and utility of p2PSA and its derivatives, p2PSA/fPSA (%p2PSA) and the Prostate Health Index (PHI). A systematic search of the PubMed and Scopus electronic databases was performed in accordance with the PRISMA statement (http://www.prisma-statement.org), considering the time period from January 1990 to January 2014 and using the following search terms: proprostate specific antigen, proenzyme PSA, proPSA, [-2]proPSA, p2PSA, Prostate Health Index, and PHI. To date, 115 studies have been published, but only 35 were considered for the qualitative analysis. These studies suggested that p2PSA is the most cancer-specific form of PSA, being preferentially expressed in PCa tissue and being significantly elevated in the serum of men with PCa. It is now evident that p2PSA, %p2PSA, and PHI measurements improve the specificity of the available tests (PSA and derivatives) in detecting PCa. Moreover, increasing PHI values seem to correlate with more aggressive disease. Some studies have compared p2PSA and its derivatives with other new biomarkers and found p2PSA to be significantly more accurate. Indeed, the implementation of these tests in clinical practice has the potential to significantly increase the physician's ability to detect PCa and avoid unnecessary biopsies, while also having an effective impact on costs. Further studies in large, multicenter, prospective trials are required to confirm these encouraging results on the clinical utility of these new biomarkers.

The Prostate Health Index: a new test for the detection of prostate cancer

A major focus in urologic research is the identification of new biomarkers with improved specificity for clinically-significant prostate cancer. A promising new test based on prostate-specific antigen (PSA) is called the Prostate Health Index (PHI), which has recently been approved in the United States, Europe and Australia. PHI is a mathematical formula that combines total PSA, free PSA and [-2] proPSA. Numerous international studies have consistently shown that PHI outperforms its individual components for the prediction of overall and high-grade prostate cancer on biopsy. PHI also predicts the likelihood of progression during active surveillance, providing another noninvasive modality to potentially select and monitor this patient population. This article reviews the evidence on this new blood test with significant promise for both prostate cancer screening and treatment decision-making.

Management of an increasing prostate-specific antigen level after negative prostate biopsy

Patients who have a previously negative biopsy in the setting of clinical suspicion of prostate cancer still have a high risk of harboring significant undiagnosed disease. Various markers such as prostate-specific antigen (PSA) velocity, PSA density, PCA3, and newer markers may aid in repeat biopsy selection. Repeating the same biopsy procedure in such patients does not yield high cancer detection rates. More anteriorly directed transrectal or transperineal biopsies are indicated. Multiparametric magnetic resonance imaging can detect abnormal areas, and lesion-targeted biopsies can improve the cancer detection rate.

Diagnostic ability of %p2PSA and prostate health index for aggressive prostate cancer: a meta-analysis

The role of [-2]proPSA (p2PSA) based diagnostic tests for the detection of aggressive prostate cancer (PCa) has not been fully evaluated. We conducted a meta-analysis to evaluate the diagnostic performance of p2PSA/free PSA (%p2PSA) and prostate health index (Phi) tests for PCa and to evaluate their ability in discriminating between aggressive and non-aggressive PCa. A total of 16 articles were included in this meta-analysis. For the detection of PCa, the pooled sensitivity, specificity, and AUC were 0.86 (95% CI, 0.84-0.87), 0.40 (95% CI, 0.39-042) and 0.72 (95% CI, 0.67-0.77) for %p2PSA respectively, and were 0.85 (95% CI, 0.83-0.86), 0.45 (95% CI, 0.44-0.47) and 0.70 (95% CI=0.65-0.74) for Phi, respectively. In addition, the sensitivity for discriminating PCa between higher Gleason score (≥7) and lower Gleason score (<7) was 0.96 (95% CI, 0.93-0.98) and 0.90 (95% CI, 0.87-0.92) for %p2PSA and Phi respectively, and the specificity was low, only 0.09 (95% CI, 0.06-0.12) and 0.17 (95% CI, 0.14-0.19) for %p2PSA and Phi, respectively. Phi and %p2PSA have a high diagnostic accuracy rates and can be used in PCa diagnosis. Phi and %p2PSA may be useful as tumor markers in predicating patients harboring more aggressive disease and guiding biopsy decisions.

Measurement of aberrant glycosylation of prostate specific antigen can improve specificity in early detection of prostate cancer

INTRODUCTION

We previously identified prostate cancer (PCa)-associated aberrant glycosylation of PSA, where α2,3-linked sialylation is an additional terminal N-glycan on free PSA (S2,3PSA). We then developed a new assay system measuring S2,3PSA using a magnetic microbead-based immunoassay. We compared the diagnostic accuracy of conventional PSA and percent-free PSA (%fPSA) tests.

METHODS

We used MagPlex beads to measure serum S2,3PSA levels using anti-human fPSA monoclonal antibody (8A6) for capture and anti-α2,3-linked sialic acid monoclonal antibody (HYB4) for detection. We determined the cutoff values in a training test and measured serum S2,3PSA levels in 314 patients who underwent biopsy, including 138 PCa and 176 non-PCa patients with PSA of <10.0 ng/ml. Serum S2,3PSA levels were presented as mean fluorescence intensity (MFI). Receiver operating characteristic curves were used to evaluate the diagnostic accuracy of total PSA, %fPSA, and S2,3PSA.

RESULTS

We determined an MFI cutoff value of 1130 with a sensitivity of 95.0% and specificity of 72.0% for the diagnosis of PCa in the training test. In the validation study, the area under the curve for the detection of PCa with S2,3PSA was 0.84, which was significantly higher than that with PSA or %fPSA.

CONCLUSIONS

Although the present study is small and preliminary, these results suggest that the measurement of serum S2,3PSA using a magnetic microbead-based immunoassay may improve the accuracy of early detection of PCa and reduce unnecessary prostate biopsy.

International Urology Journal Club via Twitter: 12-month experience

BACKGROUND

Online journal clubs have increasingly been utilised to overcome the limitations of the traditional journal club. However, to date, no reported online journal club is available for international participation.

OBJECTIVE

To present a 12-mo experience from the International Urology Journal Club, the world's first international journal club using Twitter, an online micro-blogging platform, and to demonstrate the viability and sustainability of such a journal club.

DESIGN, SETTING, AND PARTICIPANTS

#urojc is an asynchronous 48-h monthly journal club moderated by the Twitter account @iurojc. The open invitation discussions focussed on papers typically published within the previous 2-4 wk. Data were obtained via third-party Twitter analysis services.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Outcomes analysed included number of total and new users, number of tweets, and qualitative analysis of the relevance of tweets. Analysis was undertaken using GraphPad software, Microsoft Excel, and thematic qualitative analysis.

RESULTS AND LIMITATIONS

The first 12 mo saw a total of 189 unique users representing 19 countries and 6 continents. There was a mean of 39 monthly participants that included 14 first-time participants per month. The mean number of tweets per month was 195 of which 62% represented original tweets directly related to the topic of discussion and 22% represented retweets of original posts. A mean of 130 832 impressions, or reach, were created per month. The @iurojc moderator account has accumulated >1000 followers. The study is limited by potentially incomplete data extracted by third-party Twitter analysers.

CONCLUSIONS

Social media provides a potential for enormous international communication that has not been possible in the past. We believe the pioneering #urojc is both viable and sustainable. There is unlimited scope for journal clubs in other fields to follow the example of #urojc and utilise online portals to revitalise the traditional journal club while fostering international relationships.

Multicenter European external validation of a prostate health index-based nomogram for predicting prostate cancer at extended biopsy

BACKGROUND

External validation of a prediction tool is mandatory to assess the tool's accuracy and generalizability within different patient cohorts.

OBJECTIVE

To externally validate a previously developed Prostate Health Index (PHI)-based nomogram for predicting the presence of prostate cancer (PCa) at biopsy.

DESIGN, SETTING, AND PARTICIPANTS

The study population consisted of 883 patients who were scheduled for a prostate biopsy at one of five European tertiary care centers. Total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), and [-2]pro-prostate-specific antigen (p2PSA) levels were determined. The fPSA-to-tPSA ratio (%fPSA), p2PSA, and PHI ([p2PSA / fPSA] × √tPSA) were calculated.

INTERVENTION

Extended initial and repeat prostate biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression models were fitted to test the predictors of PCa and to determine their predictive accuracy. A calibration plot was used to evaluate the extent of overestimation or underestimation between nomogram predictions and observed PCa rate. Decision curve analysis (DCA) provided an estimate of the net benefit obtained by using the PHI-based nomogram.

RESULTS AND LIMITATIONS

Of 833 patients, 365 (41.3%) were diagnosed with PCa at extended prostate biopsy. In accuracy analyses, PHI was the most informative predictor of PCa (0.68), outperforming tPSA (0.51) and %fPSA (0.64). The predictive accuracy of the previously developed nomogram was 75.2% (95% confidence interval, 71.4-78.1). Calibration of the nomogram was good in patients at a low to intermediate predicted probability of PCa, while calibration was suboptimal, with a tendency to overestimate the presence of PCa, in high-risk patients. Finally, DCA demonstrated that the use of the PHI-based nomogram resulted in the highest net benefit. The main limitation of the study is the fact that only Caucasian patients were included.

CONCLUSIONS

At external validation, the previously developed PHI-based nomogram confirmed its ability to determine the presence of PCa at biopsy. These findings provide further evidence supporting the potential role of the nomogram in the biopsy decision pathway for European men with suspected PCa.

PATIENT SUMMARY

In the current study, we externally validated a Prostate Health Index-based nomogram to predict the presence of prostate cancer (PCa) at biopsy. This tool may help clinicians determine the need for a prostate biopsy in European patients with suspected PCa.