Evidence review for following up people at risk of prostate cancer

Introduction

A negative prostate biopsy and/or negative MRI does not definitively exclude the presence of cancer. People who have had a negative biopsy or MRI may still have prostate cancer. Factors that might indicate undetected prostate cancer include a raised prostate specific antigen (PSA), abnormal digital rectal examination (DRE), abnormal results of other PSA-based tests, such as free PSA to total PSA expressed as a percentage (free-to-total PSA%), PSA density and PSA velocity and new biomarkers, such as the prostate cancer gene 3 (PCA3) assessed prior to initial biopsy.

This review aims to identify studies reporting accuracy data for measures that can help simulate strategies to follow-up people who have a raised PSA, negative MRI and/ or negative biopsy as specified in Table 1. For full details of the review protocol, see appendix A.

PICO table

Table 1

PICO table.

Methods and process

This evidence review was developed using the methods and process described in Developing NICE guidelines: the manual (2014). Methods specific to this review question are described in the review protocol in appendix A, and the methods section in appendix B.

Declarations of interest were recorded according to NICE’s 2018 conflicts of interest policy

Clinical evidence

Summary of clinical studies included in the evidence review

Prostate cancer antigen 3 urinary assay

Table

Study location Italy

Multiparametric MRI

Table

Study location UK

PSA and PSA derivatives

Table

Study location USA

See appendix E for full evidence tables.

Quality assessment of clinical studies included in the evidence review

See appendix G for full GRADE tables.

Economic evidence

Economic model

The committee identified this question as its top priority for original modelling. There has been substantial variability of practice, especially since MRI became a routine part of the diagnostic pathway, with little certainty about the long-term follow-up of people with apparently negative findings. For full details of the methods and results of the analysis, please see the health economic appendix.

Methods

We developed a lifetime Markov model with 3-monthly cycle to explore the follow-up of people who have a raised PSA, negative MRI and/or negative prostate biopsy. A follow-up protocol was defined as a strategy that combined screening tests over a follow-up time and, if the screening test is positive, a further diagnostic procedure was required. Prostate cancer diagnosis can only be determined by a positive prostate biopsy. The model adopted a patient perspective for outcomes and an NHS and PSS perspective for costs, in line with Developing NICE guidelines (2014). Health outcomes and costs were discounted applying a discount rate at 3.5% per year.

The simulated population enter the decision problem with a negative diagnosis, though some people are true negative (no cancer) and some are false negative (undetected cancer). People with no cancer are at risk of developing prostate cancer (false negative); at some point, those with undetected prostate cancer are likely to be diagnosed and hence become true positive cases (detected prostate cancer). The model assumes that prostate biopsies are perfectly specific; hence, a false positive state is not required. People with diagnosed or undiagnosed cancer are risk stratified into states representing low-risk (clinically non-significant) prostate cancer, intermediate-risk and high-risk localised disease and metastatic disease. The model simulates symptomatic or incidental findings (e.g., urinary symptoms that may indicate prostate pathology and skeletal pain that may indicate metastatic disease) as triggers that would lead to a potential diagnosis regardless of other markers. The model assumes that undiagnosed metastatic disease would be identified when people developed symptoms.

Clinically significant prostate cancer was defined as Gleason score ≥ 3+4 (i.e. any score of 7 or more). The terms used for health states in the model follow the cancer risk categories recommended by NICE (CG175 2014). A schematic depiction of the model structure is provided in Figure 1.

Figure 1

Schematic depiction of original health economic model.

The base-case modelled cohort comprises men at age 66 with suspected prostate cancer and prior negative findings on mpMRI and/or 1 or 2 biopsies. Therefore, the model addresses different baseline populations based on diagnostic history, and each has a different starting distribution of people with true negative and false negative status, as shown in Table 2. Evidence to calculate these probabilities was predominantly drawn from evidence review D of this update, which investigates the optimal diagnostic pathway for people with suspected prostate cancer, with particular reliance on PROMIS (Ahmed et al., (2017) and PRECISION (Kasivisvanathan et al., 2018).

Table 2

Baseline distribution of the modelled population based on previous diagnostic tests.

The prevalence of clinically significant prostate cancer was based on that reported in PROMIS, as the committee indicated that the eligibility criteria for the study are representative of the population of interest for this question. The prevalence of clinically non-significant prostate cance was also obtained from PROMIS, Figure 2.

Figure 2

The prevalence of clinically significant and non-significant prostate cancer obtained from PROMIS.

The simulated follow-up strategies were formed based on screening and diagnostic tests that the committee considered clinically meaningful. They ranged from the least intensive strategies, i.e. no screening and waiting for symptoms, to the most rigorous ones i.e. performing a transperineal template mapping (TPM) biopsy, assumed to be perfectly sensitive, for all people. In the base case, all follow-up strategies stopped when the modelled cohort reached 75 years, which the committee advised was a realistic upper threshold (mostly because the average person would be unlikely to be considered for radical therapy on diagnosis beyond this age). However, this was subject to sensitivity analysis, recognising that people can still receive radical treatment at an age more than 75 in clinical practice.

The natural history of prostate cancer is simulated using data derived from key UK or European studies. Prostate cancer specific mortality is taken from STAMPEDE where James et al. (2016) reported findings on the overall survival for people with metastatic prostate cancer. A study by Gnanapragasam et al. (2016) analysed UK registry data on people with localised prostate cancer and reported disease specific mortality according to risk groups. We used their findings to derive the progression probabilities within people with diagnosed prostate cancer. The rates of adverse events associated with prostate cancer primary treatments were sourced from ProtecT (Donovan et al., 2016) for localised disease and from STAMPEDE for metastatic prostate cancer. Findings on metastases risk rates from different risk groups of localised prostate cancer were reported in the Scandinavian Prostate Cancer Group 4 trial (SPCG4), by Bill-Axelson et al. (2014), where participants were assigned either to radical prostatectomy or watchful waiting. The watchful waiting represented a non-curative strategy. Thus, it appeared to be relevant to source the progression probabilities in our undiagnosed population.

In this analysis, people with undiagnosed and diagnosed metastatic prostate cancer are at risk of disease specific mortality obtained from the standard of care arm and the docetaxel arm in STAMPEDE, respectively. The base case model deploys disease specific mortality as a proportional hazard to general mortality. The model seems to fit the data better than the scenario where disease specific mortality was assigned a constant probability.

Results

The screening tests included in the follow-up strategies simulated in our model were obtained from our clinical review that identified a number of tests. GRADE tables in Appendix G show these tests with their accuracy data. Optimal follow-up strategies were identified for different sub-populations. Table 3 shows the results of the base case analysis where all possible strategies were included.

The strategy where people receive TPM biopsy at the beginning of follow-up appeared to be the most optimal strategies in the majority of the sub-populations. However, this type of biopsy was assumed to be perfectly sensitive in the model, which may not be the case in clinical practice. In addition, it may lead to overdiagnosis, causing potential harms that the base case model may underestimate. The committee also advised that it was not feasible to adopt this strategy, as TPM was resource intensive and, although the model predicted that the resources would be justified, the healthcare system was not currently equipped to perform a large number of such procedures, mostly under general anaesthetic, resulting in an unrealistic burden for histopathology services. Thus, the model generated results with this strategy excluded and all prostate biopsies within the follow-up were TRUS,

Table 3

Optimal follow-up strategies for different sub-populations, including the strategy where all patients are eligible to receive TPM.

Measures derived from PSA tests, including velocity at a threshold of 0.75 ng/ml/year and density at a threshold of 0.15 ng/ml/ml, appear to be reliable indicators that trigger further diagnostics within the majority of subpopulations. However, “no screening” strategy appears optimal for the lowest-risk subpopulation who had MRI Likert scores of 1 or 2 and 2 previous negative biopsies, unless QALYs are valued at a little over £20,000 each. The model generates consistent results, as the optimal frequency of tests changes proportionally with the potential risk of disease. For example, within the population who had negative mpMRI (Likert 1 or 2), the optimal frequency of the PSA velocity test was every 6 months, every year or 2-yearly for people who had no biopsy, 1 biopsy or 2 biopsies, respectively (when QALYs are valued at £30,000). The percentage of free PSA test appears effective in directing people to further diagnostics. The strategy, where people receive this test every 6 months and, if the percentage of free PSA was ≤15%,, they were directed to TRUS, seems to be optimal within the population who had MRI Likert score of 5 and 1 previous negative biopsy.

Table 4

Optimal follow-up strategies for different sub-populations, excluding TPM as part of any strategy.

Sensitivity analysis

Our findings seemed to be robust, in terms of the types of screening tests triggering further investigation, in a number of different scenarios. However, when the modelled cohort entered the model at a younger age (52 years), strategies with greater frequency were found to be optimal. For example, the strategy associated with the highest net health benefits for people that had Likert score at 3 and previous negative biopsy included PSA velocity at a threshold of 0.75 ng/ml/year determining people who need TRUS, but to be performed annually instead of every 2 years in the base case analysis. In addition, “no screening” strategy was not found optimal anymore in any sub-population.

Following the strategy where all receive an immediate TPM inevitably leads to overtreatment of people with clinically non-significant disease, which may cause harm more than benefits, e.g. increased anxiety as a consequence of the diagnosis. In the absence of evidence on this disutility due to overdiagnosis, we did not include it in the base-case analysis. However, to explore its potential impact, we applied disutility (0.05) to the diagnosis of low-risk prostate cancer in a scenario analysis. This resulted in the “no screening” strategies being more encouraged within the least risk sub-population. This scenario was also in favour of less frequent screening test, using PSA velocity at a threshold of 0.75 ng/ml/year, PSA density at threshold of 0.15 ng/ml/ml and %free PSA.

In a further scenario analysis, we applied both the disutility associated with the diagnosis of clinically non-significant disease and a higher cost of TPM, assuming that it required staying overnight in hospital in all cases. Under these conditions, the strategy of offering an immediate TPM to all would not be optimal in the majority of subpopulations. Optimal strategies included PSA screening tests, using PSA velocity at a threshold of 0.75 ng/ml/year, PSA density at threshold of 0.15 ng/ml/ml and %free PSA. The frequency of test varied based on the risk from yearly to 3-yearly based on the prostate cancer risk. For people with negative biopsies but did not receive Mp-MRI, optimal strategies included Mp-MRI to direct people to prostate biopsy, if Likert score ≥4.

Evidence statements

The committee’s discussion of the evidence

Review protocol: What is the most clinically- and cost-effective follow-up protocol for people who have a raised PSA, negative MRI and/ or negative biopsy?

Table

To identify studies reporting accuracy data for PSA measures that can help simulate strategies to follow-up people who have a raised PSA, negative MRI and/ or negative biopsy. No existing recommendations

Diagnostic test accuracy evidence

In this guideline, diagnostic test accuracy (DTA) data are classified as any data in which a feature – be it a symptom, a risk factor, a test result or the output of some algorithm that combines many such features – is observed in some people who have the condition of interest at the time of the test and some people who do not. Such data either explicitly provide, or can be manipulated to generate, a 2x2 classification of true positives and false negatives (in people who, according to the reference standard, truly have the condition) and false positives and true negatives (in people who, according to the reference standard, do not).

The ‘raw’ 2x2 data can be summarised in a variety of ways. Those that were used for decision making in this guideline are as follows:

• Positive likelihood ratios describe how many times more likely positive features are in people with the condition compared to people without the condition. Values greater than 1 indicate that a positive result makes the condition more likely.

  • LR⁺ = (TP/[TP+FN])/(FP/[FP+TN])
• Negative likelihood ratios describe how many times less likely negative features are in people with the condition compared to people without the condition. Values less than 1 indicate that a negative result makes the condition less likely.

  • LR^- = (FN/[TP+FN])/(TN/[FP+TN])
• Sensitivity is the probability that the feature will be positive in a person with the condition.

  • sensitivity = TP/(TP+FN)
• Specificity is the probability that the feature will be negative in a person without the condition.

  • specificity = TN/(FP+TN)

The following schema, adapted from the suggestions of Jaeschke et al. (1994), was used to interpret the likelihood ratio findings from diagnostic test accuracy reviews.

Table 5. Interpretation of likelihood ratios

The schema above has the effect of setting a minimal important difference for positive likelihoods ratio at 2, and a corresponding minimal important difference for negative likelihood ratios at 0.5. Likelihood ratios (whether positive or negative) falling between these thresholds were judged to indicate no meaningful change in the probability of disease.

Evidence statements

The evidence statements were based on likelihood ratios (a MID for positive likelihoods ratio was set at 2, and a corresponding MID for negative likelihood ratios at 0.5) and these are classified in to one of four categories:

• Situations where the data are only consistent, at a 95% confidence level, with an effect in one direction (i.e. one that is ‘statistically significant’), and the magnitude of that effect is most likely to meet or exceed the MID (i.e. the point estimate is not in the zone of equivalence). In such cases, we state that the index test lead to a moderate, large and very large increase/decrease in probability of disease
• Situations where the data are only consistent, at a 95% confidence level, with an effect in one direction (i.e. one that is ‘statistically significant’), but the magnitude of that effect is most likely to be less than the MID (i.e. the point estimate is in the zone of equivalence). In such cases, we state that the index test could not meaningfully alter the probability of disease.
• In all other cases, we state that the index test could not alter the probability between the comparators
• When the likelihood ratios were reversed for example – positive likelihood ratio of 0.1 and negative likelihood ratio of 3, we state that the index test has no diagnostic value.

Methods for combining diagnostic test accuracy evidence

Meta-analysis of diagnostic test accuracy data was conducted with reference to the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Deeks et al. 2010).

Where applicable, diagnostic syntheses were stratified by:

• Presenting symptomatology (features shared by all participants in the study, but not all people who could be considered for a diagnosis in clinical practice).
• The reference standard used for true diagnosis.

Where five or more studies were available for all included strata, a bivariate model was fitted using the mada package in R v3.4.0, which accounts for the correlations between positive and negative likelihood ratios, and between sensitivities and specificities. Where sufficient data were not available (2-4 studies), separate independent pooling was performed for positive likelihood ratios, negative likelihood ratios, sensitivity and specificity, using Microsoft Excel. This approach is conservative as it is likely to somewhat underestimate test accuracy, due to failing to account for the correlation and trade-off between sensitivity and specificity (see Deeks 2010).

Random-effects models (der Simonian and Laird) were fitted for all syntheses, as recommended in the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (Deeks et al. 2010).

In any meta-analyses where some (but not all) of the data came from studies at high risk of bias, a sensitivity analysis was conducted, excluding those studies from the analysis. Results from both the full and restricted meta-analyses are reported. Similarly, in any meta-analyses where some (but not all) of the data came from indirect studies, a sensitivity analysis was conducted, excluding those studies from the analysis.

To meta-analyse the data, - in any cases where different thresholds were used across studies the following rules were adapted

Publication bias

Publication bias was assessed in two ways. First, if evidence of conducted but unpublished studies was identified during the review (e.g. conference abstracts or protocols without accompanying published data), available information on these unpublished studies was reported as part of the review. Secondly, where 10 or more studies were included as part of a single meta-analysis, a funnel plot was produced to graphically assess the potential for publication bias.

Modified GRADE for diagnostic test accuracy evidence

GRADE has not been developed for use with diagnostic studies; therefore a modified approach was applied using the GRADE framework. GRADE assessments were only undertaken for positive and negative likelihood ratios, as the MIDs used to assess imprecision were based on these outcomes, but results for sensitivity and specificity are also presented alongside those data.

Cross-sectional and cohort studies were initially rated as high-quality evidence if well conducted, and then downgraded according to the standard GRADE criteria (risk of bias, inconsistency, imprecision and indirectness) as detailed in Table 6 below.

Table 6. Rationale for downgrading quality of evidence for diagnostic questions

The quality of evidence for each outcome was upgraded if either of the following conditions were met:

• Data showing an effect size sufficiently large that it cannot be explained by confounding alone.
• Data where all plausible residual confounding is likely to increase our confidence in the effect estimate.

Search summary

The search strategies were based on the review protocol provided. The prostate cancer population terms have been removed for this question as the main focus was for patients who haven’t yet been diagnosed with prostate cancer.

Clinical searches

Sources searched for this review question:

• Cochrane Database of Systematic Reviews – CDSR (Wiley)
• Cochrane Central Register of Controlled Trials – CENTRAL (Wiley)
• Database of Abstracts of Reviews of Effects – DARE (Wiley)
• Health Technology Assessment Database – HTA (Wiley)
• EMBASE (Ovid)
• MEDLINE (Ovid)
• MEDLINE In-Process (Ovid)
• Epub Ahead of Print (Ovid)

The clinical searches were conducted in April 2018.

The MEDLINE search strategy is presented below. It was translated for use in all other databases.

Table

Study design filters and limit

The McMaster diagnosis filter plus the prostate diagnosis subhedings (OVID) were appended to the strategy above and are presented below. They were translated for use in the MEDLINE In-Process and Embase databases.

Table

McMaster Diagnosis studies 1. sensitiv:.mp. OR diagnos:.mp. OR di.fs.

An English language limit was applied. Animal studies and certain publication types (letters, historical articles, comments, editorials, news and case reports) were also excluded.

Health Economics search strategy

Economic evaluations and quality of life data.

Sources searched:

• NHS Economic Evaluation Database – NHS EED (Wiley) (legacy database)
• Health Technology Assessment (HTA Database)
• EconLit (Ovid)
• Embase (Ovid)
• MEDLINE (Ovid)
• MEDLINE In-Process (Ovid)

Search filters to retrieve economic evaluations and quality of life papers were appended to the population search terms in MEDLINE, MEDLINE In-Process and Embase to identify relevant evidence and can be seen below.

An English language limit was applied. Animal studies and certain publication types (letters, historical articles, comments, editorials, news and case reports) were also excluded.

The economic searches were conducted in April 2018.

Health Economics filters

Table

Clinical searches

Sources searched for this review question:

• Cochrane Database of Systematic Reviews – CDSR (Wiley)
• Cochrane Central Register of Controlled Trials – CENTRAL (Wiley)
• Database of Abstracts of Reviews of Effects – DARE (Wiley)
• Health Technology Assessment Database – HTA (Wiley)
• EMBASE (Ovid)
• MEDLINE (Ovid)
• MEDLINE In-Process (Ovid)
• MEDLINE Epub Ahead of Print (Ovid)

The clinical searches were conducted in April 2018

The MEDLINE search strategy is presented below. It was translated for use in all other databases.

Table

Study design filters and limit

The MEDLINE McMaster Diagnosis filter was appended to the strategy above along with the diagnosis subheadings that were available in MEDLINE (Ovid) related to the prostate. This is presented below and was translated for use in the MEDLINE In-Process and Embase databases.

Table

An English language limit has been applied. Animal studies and certain publication types (letters, historical articles, comments, editorials, news and case reports) have been excluded.

Health Economics filters

Table

Clinical evidence

Economic evidence

Multiparametric MRI

Multiparametric MRI (score ≥3) sensitivity and specificity Any cancer

Multiparametric MRI (score ≥3) positive and negative likelihood ratios Any cancer

Multiparametric MRI (score ≥3) sensitivity and specificity - clinically significant prostate cancer

Multiparametric MRI (score ≥3) poitive and negative likelhood ratio clinically significant cancer

Multiparametric MRI (score ≥4) sensitivity and specificity - clinically significant cancer

Multiparametric MRI (score ≥4) poitive and negative likelhood ratio – clinically significant cancer

Total prostate specific antigen

Threshold 3.5-4.4ng/ml Sensitivity and Specificty

Threshold 3.5-4.4ng/ml Positive and Negative Likelihood ratios

Threshold 4.5-5.4ng/ml Sensitivity and Specificity

Threshold 4.5-5.4ng/ml Positive and Negative Likelihood ratios

Threshold 5.5 – 6.4ng/ml Sensitivity and Specificty

Threshold 5.5 – 6.4ng/ml Likelihood ratios

Threshold 6.5 -7.4ng/ml Sensitivity and Specificity

Threshold 6.5 -7.4ng/ml Likelihood ratios

Prostate specific antigen Density

Threshold 0.10ng/ml/ml sensitivity and specificity

Threshold 10ng/ml/ml positive and negative likelihood ratios

Threshold ≥0.10ng/ml/ml sensitivity and specificity

Threshold ≥0.10ng/ml/ml positive and negative likelihood ratios

Threshold ≥15ng/ml/ml sensitivity and specificity

Threshold ≥15ng/ml/cm³ Negative and likelihood ratio

Threshold ≥30ng/ml/cm³ sensitivity and specificity

Threshold ≥30ng/ml/ml negative and positive likelihood ratio

Prostate specific antigen density of the transition zone

Threshold <0.20ng/ml/ml sensitivity and specificity

Threshold <0.20ng/mlml positive and negative likelihood ratio

Prostate specific antigen velocity

Threshold 0.75 ng/ml/year - sensitivity and specificity

Threshold 0.75 ng/ml/year - Positive and Negative likelihood ratios

%Free Prostate specific antigen

Threshold 10% sensitivity and specificity

Threshold <10% positive and negative likelihood ratios

Threshold 15% Sensitivity and specificity

Threshold <15% positive and negative likelihood ratio

Threshold 20% sensititvity and specificity

Threshold 20% positive and negative likelihood ratios

Threshold 25% sensitivity and specificity

Threshold 25% positive and negative likelihood ratio

Threshold 30% sensitivity and specificity

Threshold 30% positive and negative likelihood ratio

Abnormal digital rectal examination

Positive DRE - Sensitivity and specificity

Positive DRE- Positive and negative likelihood ratios

Clinical studies

Table

Study does not contain any relevant index tests Study looked mp-MRI - targeted TRUS-B

Economic studies

Table

Clinical studies - included

• Abd-Alazeez Mohamed, Ahmed Hashim U, Arya Manit, Charman Susan C, Anastasiadis Eleni, Freeman Alex, Emberton Mark, and Kirkham Alex (2014) The accuracy of multiparametric MRI in men with negative biopsy and elevated PSA level--can it rule out clinically significant prostate cancer?. Urologic oncology 32(1), 45.e17–22 [PMC free article: PMC4082533] [PubMed: 24055430]

• Auprich M, Augustin H, Budaus L, Kluth L, Mannweiler S, Shariat S F, Fisch M, Graefen M, Pummer K, and Chun F K. H (2012) A comparative performance analysis of total prostate-specific antigen, percentage free prostate-specific antigen, prostate-specific antigen velocity and urinary prostate cancer gene 3 in the first, second and third repeat prostate biopsy. BJU International 109(11), 1627–1635 [PubMed: 21939492]

• Barbera Michele, Pepe Pietro, Paola Quintino, and Aragona Francesco (2012) PCA3 score accuracy in diagnosing prostate cancer at repeat biopsy: our experience in 177 patients. Archivio italiano di urologia, and andrologia : organo ufficiale [di] Societa italiana di ecografia urologica e nefrologica 84(4), 227–9 [PubMed: 23427750]

• Benecchi L (2006) PSA velocity and PSA slope. Prostate cancer and prostatic diseases 9(2), 169–72 [PubMed: 16568147]

• Boesen Lars, Noergaard Nis, Chabanova Elizaveta, Logager Vibeke, Balslev Ingegerd, Mikines Kari, and Thomsen Henrik S (2015) Early experience with multiparametric magnetic resonance imaging-targeted biopsies under visual transrectal ultrasound guidance in patients suspicious for prostate cancer undergoing repeated biopsy. Scandinavian journal of urology 49(1), 25–34 [PubMed: 24922550]

• Boesen L, Norgaard N, Logager V, Balslev I, and Thomsen H S (2018) Multiparametric MRI in men with clinical suspicion of prostate cancer undergoing repeat biopsy: a prospective comparison with clinical findings and histopathology. Acta Radiologica 59(3), 371–380 [PubMed: 28679325]

• Busetto Gian Maria, De Berardinis, Ettore, Sciarra Alessandro, Panebianco Valeria, Giovannone Riccardo, Rosato Stefano, D’Errigo Paola, Di Silverio, Franco, Gentile Vincenzo, and Salciccia Stefano (2013) Prostate cancer gene 3 and multiparametric magnetic resonance can reduce unnecessary biopsies: decision curve analysis to evaluate predictive models. Urology 82(6), 1355–60 [PubMed: 24080222]

• Chen C S, Wang S S, Li J R, Cheng C L, Yang C R, Chen W M, Ou Y C, Ho H C, Chiu K Y, and Yang C K (2011) PSA density as a better predictor of prostate cancer than percent-free PSA in a repeat biopsy. Journal of the Chinese Medical Association 74(12), 552–555 [PubMed: 22196470]

• Ciatto S, Rubeca T, Martinelli F, Pontenani G, Lombardi C, Di Lollo, and S (2008) PSA doubling time as a predictor of the outcome of random prostate biopsies prompted by isolated PSA elevation in subjects referred to an outpatient biopsy facility in a routine clinical scenario. The International journal of biological markers 23(3), 187–91 [PubMed: 18949746]

• Girometti Rossano, Bazzocchi Massimo, Como Giuseppe, Brondani Giovanni, Del Pin, Matteo, Frea Bruno, Martinez Guillermo, and Zuiani Chiara (2012) Negative predictive value for cancer in patients with “gray-zone” PSA level and prior negative biopsy: preliminary results with multiparametric 3.0 Tesla MR. Journal of magnetic resonance imaging : JMRI 36(4), 943–50 [PubMed: 22649035]

• Gittelman Mc, Hertzman B, Bailen J, Williams T, Koziol I, Henderson Rj, Efros M, Bidair M, and Ward Jf (2013) PCA3 molecular urine test as a predictor of repeat prostate biopsy outcome in men with previous negative biopsies: a prospective multicenter clinical study. Journal of urology 190(1), 64–69 [PubMed: 23416644]

• Haese A, de la Taille, A, van Poppel, H, Marberger M, Stenzl A, Mulders P F. A, Huland H, Abbou C C, Remzi M, Tinzl M, Feyerabend S, Stillebroer A B, van Gils, M P M. Q, and Schalken J A (2008) Clinical Utility of the PCA3 Urine Assay in European Men Scheduled for Repeat Biopsy. European Urology 54(5), 1081–1088 [PubMed: 18602209]

• Hansen Nienke L, Barrett Tristan, Koo Brendan, Doble Andrew, Gnanapragasam Vincent, Warren Anne, Kastner Christof, and Bratt Ola (2017) The influence of prostate-specific antigen density on positive and negative predictive values of multiparametric magnetic resonance imaging to detect Gleason score 7-10 prostate cancer in a repeat biopsy setting. BJU international 119(5), 724–730 [PubMed: 27488931]

• Hong C W, Walton-Diaz A, Rais-Bahrami S, Hoang A N, Turkbey B, Stamatakis L, Xu S, Amalou H, Minhaj Siddiqui, M, Nix J W, Vourganti S, Merino M J, Choyke P L, Wood B J, and Pinto P A (2014) Imaging and pathology findings after an initial negative MRI-US fusion-guided and 12-core extended sextant prostate biopsy session. Diagnostic and Interventional Radiology 20(3), 234–238 [PMC free article: PMC4289157] [PubMed: 24509182]

• Horinaga Minoru, Nakashima Jun, Ishibashi Midori, Oya Mototsugu, Ohigashi Takashi, Marumo Ken, and Murai Masaru (2002) Clinical value of prostate specific antigen based parameters for the detection of prostate cancer on repeat biopsy: the usefulness of complexed prostate specific antigen adjusted for transition zone volume. The Journal of urology 168(3), 986–90 [PubMed: 12187205]

• Keetch D W, McMurtry J M, Smith D S, Andriole G L, and Catalona W J (1996) Prostate specific antigen density versus prostate specific antigen slope as predictors of prostate cancer in men with initially negative prostatic biopsies. The Journal of urology 156(2 Pt 1), 428–31 [PubMed: 8683695]

• Lazzeri Massimo, Briganti Alberto, Scattoni Vincenzo, Lughezzani Giovanni, Larcher Alessandro, Gadda Giulio Maria, Lista Giuliana, Cestari Andrea, Buffi Nicolomaria, Bini Vittorio, Freschi Massimo, Rigatti Patrizio, Montorsi Francesco, and Guazzoni Giorgio (2012) Serum index test %[-2]proPSA and Prostate Health Index are more accurate than prostate specific antigen and %fPSA in predicting a positive repeat prostate biopsy. The Journal of urology 188(4), 1137–43 [PubMed: 22901578]

• Lee J G, Bae S H, Choi S H, Kwon T G, and Kim T H (2012) Role of prostate-specific antigen change ratio at initial biopsy as a novel decision-making marker for repeat prostate biopsy. Korean Journal of Urology 53(7), 467–471 [PMC free article: PMC3406192] [PubMed: 22866217]

• Lista F, Castillo E, Gimbernat H, Rodriguez-Barbero J M, Panizo J, and Angulo J C (2015) Multiparametric magnetic resonance imaging predicts the presence of prostate cancer in patients with negative prostate biopsy. Actas urologicas espanolas 39(2), 85–91 [PubMed: 25267460]

• Marks Leonard S, Fradet Yves, Deras Ina Lim, Blase Amy, Mathis Jeannette, Aubin Sheila M. J, Cancio Anthony T, Desaulniers Marie, Ellis William J, Rittenhouse Harry, and Groskopf Jack (2007) PCA3 molecular urine assay for prostate cancer in men undergoing repeat biopsy. Urology 69(3), 532–5 [PubMed: 17382159]

• Marks L S, Fradet Y, Lim Deras, I, Blase A, Mathis J, Aubin S M. J, Cancio A T, Desaulniers M, Ellis W J, Rittenhouse H, and Groskopf J (2007) PCA3 Molecular Urine Assay for Prostate Cancer in Men Undergoing Repeat Biopsy. Urology 69(3), 532–535 [PubMed: 17382159]

• Merola Roberta, Tomao Luigi, Antenucci Anna, Sperduti Isabella, Sentinelli Steno, Masi Serena, Mandoj Chiara, Orlandi Giulia, Papalia Rocco, Guaglianone Salvatore, Costantini Manuela, Cusumano Giuseppe, Cigliana Giovanni, Ascenzi Paolo, Gallucci Michele, and Conti Laura (2015) PCA3 in prostate cancer and tumor aggressiveness detection on 407 high-risk patients: a National Cancer Institute experience. Journal of experimental & clinical cancer research : CR 34, 15 [PMC free article: PMC4324853] [PubMed: 25651917]

• Michielsen D P, De Boe, V R, Braeckman J G, and Keuppens F I (1998) Specificity and accuracy of TRUS-measured PSA-density and transition zone-PSA in the diagnosis of prostate cancer. European journal of ultrasound : official journal of the European Federation of Societies for Ultrasound in Medicine and Biology 8(2), 125–8 [PubMed: 9845794]

• Murray N P, Reyes E, Orellana N, Fuentealba C, and Duenas R (2014) A comparative performance analysis of total PSA, percentage free PSA, PSA velocity, and PSA density versus the detection of primary circulating prostate cells in predicting initial prostate biopsy findings in chilean men. BioMed Research International 2014, 676572 [PMC free article: PMC4101233] [PubMed: 25101294]

• Murray Nigel P, Reyes Eduardo, Orellana Nelson, Fuentealba Cynthia, and Jacob Omar (2016) Head to Head Comparison of the Chun Nomogram, Percentage Free PSA and Primary Circulating Prostate Cells to Predict the Presence of Prostate Cancer at Repeat Biopsy. Asian Pacific journal of cancer prevention : APJCP 17(6), 2941–6 [PubMed: 27356715]

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• Scattoni V, Lazzeri M, Lughezzani G, De Luca, S, Passera R, Bollito E, Randone D, Abdollah F, Capitanio U, Larcher A, Lista G, Gadda G M, Bini V, Montorsi F, and Guazzoni G (2013) Head-to-head comparison of prostate health index and urinary PCA3 for predicting cancer at initial or repeat biopsy. Journal of Urology 190(2), 496–501 [PubMed: 23466239]

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• Simmons Lam, Kanthabalan A, Arya M, Briggs T, Barratt D, Charman Sc, Freeman A, Gelister J, Hawkes D, Hu Y, Jameson C, McCartan N, Moore Cm, Punwani S, Ramachandran N, Meulen J, Emberton M, and Ahmed Hu (2017) The PICTURE study: diagnostic accuracy of multiparametric MRI in men requiring a repeat prostate biopsy. British journal of cancer (no pagination), [PMC free article: PMC5418442] [PubMed: 28350785]

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Clinical studies – Excluded

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• Baltaci Sumer, Aksoy Hakan, Turkolmez Kadir, Elhan Atilla H, Ozden Eriz, and Gogus Orhan (2003) Use of percent free prostate-specific antigen density to improve the specificity for detecting prostate cancer in patients with normal rectal examinations and intermediate prostate-specific antigen levels. Urologia internationalis 70(1), 36–41 [PubMed: 12566813]

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• Benecchi Luigi, Pieri Anna Maria, Destro Pastizzaro, Carmelo, and Potenzoni Michele (2008) Optimal measure of PSA kinetics to identify prostate cancer. Urology 71(3), 390–4 [PubMed: 18342170]

• Benecchi L, Pieri A M, Melissari M, Potenzoni M, and Pastizzaro C D (2008) A Novel Nomogram to Predict the Probability of Prostate Cancer on Repeat Biopsy. Journal of Urology 180(1), 146–149 [PubMed: 18485383]

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• Beyersdorff Dirk, Taupitz Matthias, Winkelmann Bjoern, Fischer Thomas, Lenk Severin, Loening Stefan A, and Hamm Bernd (2002) Patients with a history of elevated prostate-specific antigen levels and negative transrectal US-guided quadrant or sextant biopsy results: value of MR imaging. Radiology 224(3), 701–6 [PubMed: 12202702]

• Bhatia Cathleeyakorn, Phongkitkarun Sith, Booranapitaksonti Dechaphol, Kochakarn Wachira, and Chaleumsanyakorn Panas (2007) Diagnostic accuracy of MRI/MRSI for patients with persistently high PSA levels and negative TRUS-guided biopsy results. Journal of the Medical Association of Thailand = Chotmaihet thangphaet 90(7), 1391–9 [PubMed: 17710982]

• Bhindi Bimal, Jiang Haiyan, Poyet Cedric, Hermanns Thomas, Hamilton Robert J, Li Kathy, Toi Ants, Finelli Antonio, Zlotta Alexandre R, van der Kwast, Theodorus H, Evans Andrew, Fleshner Neil E, and Kulkarni Girish S (2017) Creation and internal validation of a biopsy avoidance prediction tool to aid in the choice of diagnostic approach in patients with prostate cancer suspicion. Urologic oncology 35(10), 604.e17–604.e24 [PubMed: 28781111]

• Boegemann M, Stephan C, Cammann H, Vincendeau S, Houlgatte A, Jung K, Blanchet J S, and Semjonow A (2016) The percentage of prostate-specific antigen (PSA) isoform [-2]proPSA and the Prostate Health Index improve the diagnostic accuracy for clinically relevant prostate cancer at initial and repeat biopsy compared with total PSA and percentage free PSA in men aged <=65 years. BJU International 117(1), 72–79 [PubMed: 25818705]

• Boesen L, Norgaard N, Logager V, Balslev I, and Thomsen H S (2017) A Prospective Comparison of Selective Multiparametric Magnetic Resonance Imaging Fusion-Targeted and Systematic Transrectal Ultrasound-Guided Biopsies for Detecting Prostate Cancer in Men Undergoing Repeated Biopsies. Urologia Internationalis 99(4), 384–391 [PubMed: 28651247]

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• Bollito Enrico, De Luca, Stefano, Cicilano Matteo, Passera Roberto, Grande Susanna, Maccagnano Carmen, Cappia Susanna, Milillo Angela, Montorsi Francesco, Scarpa Roberto Mario, Papotti Mauro, and Randone Donato Franco (2012) Prostate cancer gene 3 urine assay cutoff in diagnosis of prostate cancer: a validation study on an Italian patient population undergoing first and repeat biopsy. Analytical and quantitative cytology and histology 34(2), 96–104 [PubMed: 22611765]

• Borboroglu P G, Comer S W, Riffenburgh R H, and Amling C L (2000) Extensive repeat transrectal ultrasound guided prostate biopsy in patients with previous benign sextant biopsies. The Journal of urology 163(1), 158–62 [PubMed: 10604336]

• Borkowetz A, Zastrow S, Platzek I, Toma M, Froehner M, Koch R, and Wirth M (2015) Assessment of tumour aggressiveness in tranperineal mri/ultrasound-fusion biopsy in comparison to transrectal systematic prostate biopsy. Journal of urology. 193(4 suppl. 1), e596

• Boulos M T, Rifkin M D, and Ross J (2001) Should prostate-specific antigen or prostate-specific antigen density be used as the determining factor when deciding which prostates should undergo biopsy during prostate ultrasound. Ultrasound quarterly 17(3), 177–80 [PubMed: 12973074]

• Brown Jeffrey G, Fulmer John R, Romano Javier, Pownell John, Rigler Wayne, Wirtshafter Amery, Sarno Mark, and Shappell Scott B (2014) Reflex PCA3 messenger ribonucleic acid testing: validation of postbiopsy urine samples and correlation with prostate biopsy findings in ~2000 patients. Urology 84(5), 1172–80 [PubMed: 25443926]

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• Campos-Fernandes J L, Bastien L, Nicolaiew N, Robert G, Terry S, Vacherot F, Salomon L, Allory Y, Vordos D, Hoznek A, Yiou R, Patard J J, Abbou C C, de la Taille, and A (2009) Prostate Cancer Detection Rate in Patients with Repeated Extended 21-Sample Needle Biopsy. European Urology 55(3), 600–609 [PubMed: 18597923]

• Capoluongo E, Zambon CF, Basso D, Boccia S, Rocchetti S, Leoncini E, Palumbo S, Padoan A, Albino G, Todaro A, Prayer-Galetti T, Zattoni F, Zuppi C, and Plebani M (2014) PCA3 score of 20 could improve prostate cancer detection: results obtained on 734 Italian individuals.. Clinica chimica acta, and international journal of clinical chemistry 429, 46–50 [PubMed: 24269853]

• Carver Brett S, Bozeman Caleb B, Simoneaux Walter J, Venable Dennis D, Kattan Michael W, and Eastham James A (2004) Race is not a predictor of prostate cancer detection on repeat prostate biopsy. The Journal of urology 172(5 Pt 1), 1853–5 [PubMed: 15540737]

• Catalona W J, Beiser J A, and Smith D S (1997) Serum free prostate specific antigen and prostate specific antigen density measurements for predicting cancer in men with prior negative prostatic biopsies. The Journal of urology 158(6), 2162–7 [PubMed: 9366336]

• Celhay Olivier, de la Taille, Alexandre, Salomon Laurent, Dore Bertrand, and Irani Jacques (2007) Fluctuating prostate-specific antigen levels in patients with initial negative biopsy: should we be reassured?. BJU international 99(5), 1028–30 [PubMed: 17324221]

• Chang C H, Chiu H C, Lin W C, Ho T L, Chang H, Chang Y H, Huang C P, Wu H C, Yang C R, and Hsieh P F (2017) The Influence of Serum Prostate-Specific Antigen on the Accuracy of Magnetic Resonance Imaging Targeted Biopsy versus Saturation Biopsy in Patients with Previous Negative Biopsy. BioMed Research International 2017, 7617148 [PMC free article: PMC5660748] [PubMed: 29159180]

• Cheikh A B, Girouin N, Colombel M, Marechal J M, Gelet A, Bissery A, Rabilloud M, Lyonnet D, and Rouvieve O (2009) Evaluation of T2-weighted and dynamic contrast-enhanced MRI in localizing prostate cancer before repeat biopsy. European Radiology 19(3), 770–778 [PubMed: 18925403]

• Chen Rui, Huang Yiran, Cai Xiaobing, Xie Liping, He Dalin, Zhou Liqun, Xu Chuanliang, Gao Xu, Ren Shancheng, Wang Fubo, Ma Lulin, Wei Qiang, Yin Changjun, Tian Ye, Sun Zhongquan, Fu Qiang, Ding Qiang, Zheng Junhua, Ye Zhangqun, Ye Dingwei, Xu Danfeng, Hou Jianquan, Xu Kexin, Yuan Jianlin, Gao Xin, Liu Chunxiao, Pan Tiejun, Sun Yinghao, Chinese Prostate Cancer, and Consortium (2015) Age-Specific Cutoff Value for the Application of Percent Free Prostate-Specific Antigen (PSA) in Chinese Men with Serum PSA Levels of 4.0-10.0 ng/ml. PloS one 10(6), e0130308 [PMC free article: PMC4474838] [PubMed: 26091007]

• Ciatto S, Bonardi R, Lombardi C, Cappelli G, Castagnoli A, D’Agata A, Zappa M, and Gervasi G (2001) Predicting prostate biopsy outcome by findings at digital rectal examination, transrectal ultrasonography, PSA, PSA density and free-to-total PSA ratio in a population-based screening setting. International Journal of Biological Markers 16(3), 179–182 [PubMed: 11605730]

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• Ciatto Stefano, Rubeca Tiziana, Confortini Massimo, Pontenani Giovanni, Lombardi Claudio, Zendron Paola, Di Lollo, Simonetta, and Crocetti Emanuele (2004) Free to total PSA ratio is not a reliable predictor of prostate biopsy outcome. Tumori 90(3), 324–7 [PubMed: 15315313]

• Cirillo S, Petracchini M, Della Monica, P, Gallo T, Tartaglia V, Vestita E, Ferrando U, and Regge D (2008) Value of endorectal MRI and MRS in patients with elevated prostate-specific antigen levels and previous negative biopsies to localize peripheral zone tumours. Clinical radiology 63(8), 871–9 [PubMed: 18625351]

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• Comet-Batlle J, Vilanova-Busquets J C, Saladie-Roig J M, Gelabert-Mas A, and Barcelo-Vidal C (2003) The value of endorectal MRI in the early diagnosis of prostate cancer. European urology 44(2), 201–8 [PubMed: 12875939]

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• Costa D N, Bloch B N, Yao D F, Sanda M G, Ngo L, Genega E M, Pedrosa I, DeWolf W C, and Rofsky N M (2013) Diagnosis of relevant prostate cancer using supplementary cores from magnetic resonance imaging-prompted areas following multiple failed biopsies. Magnetic Resonance Imaging 31(6), 947–952 [PMC free article: PMC3676721] [PubMed: 23602725]

• Costa D N, Kay F U, Pedrosa I, Kolski L, Lotan Y, Roehrborn C G, Hornberger B, Xi Y, Francis F, and Rofsky N M (2017) An initial negative round of targeted biopsies in men with highly suspicious multiparametric magnetic resonance findings does not exclude clinically significant prostate cancer-Preliminary experience. Urologic Oncology: Seminars and Original Investigations 35(4), 149 [PMC free article: PMC5366083] [PubMed: 27939349]

• Crawford E D, Rove K O, Trabulsi E J, Qian J, Drewnowska K P, Kaminetsky J C, Huisman T K, Bilowus M L, Freedman S J, Glover Jr, W L, and Bostwick D G (2012) Diagnostic performance of PCA3 to detect prostate cancer in men with increased prostate specific antigen: A prospective study of 1,962 cases. Journal of Urology 188(5), 1726–1731 [PubMed: 22998901]

• Dason Shawn, Allard Christopher B, Wright Ian, and Shayegan Bobby (2016) Transurethral Resection of the Prostate Biopsy of Suspected Anterior Prostate Cancers Identified by Multiparametric Magnetic Resonance Imaging: A Pilot Study of a Novel Technique. Urology 91, 129–35 [PubMed: 26845054]

• De La Taille, A, Irani J, Graefen M, Chun F, De Reijke, T, Kil P, Gontero P, Mottaz A, and Haese A (2011) Clinical evaluation of the PCA3 assay in guiding initial biopsy decisions. Journal of Urology 185(6), 2119–2125 [PubMed: 21496856]

• De Luca, S, Passera R, Milillo A, Coda R, and Randone D F (2012) Histological chronic prostatitis and high-grade prostate intra-epithelial neoplasia do not influence urinary prostate cancer gene 3 score. BJU International 110(11 B), E778–E782 [PubMed: 23116408]

• De Luca, S, Passera R, Bollito E, Manfredi M, Scarpa R M, Sottile A, Randone D F, and Porpiglia F (2014) Comparison of prostate cancer gene 3 score, prostate health index and percentage free prostate-specific antigen for differentiating histological inflammation from prostate cancer and other non-neoplastic alterations of the prostate at initial Biopsy. Anticancer Research 34(12), 7159–7165 [PubMed: 25503144]

• De Luca, S, Passera R, Cappia S, Bollito E, Randone D F, Milillo A, Papotti M, and Porpiglia F (2014) Fluctuation in prostate cancer gene 3 (PCA3) score in men undergoing first or repeat prostate biopsies. BJU International 114(6), E56–E61 [PubMed: 24472071]

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Economic studies - Excluded

• Blute Jr ML, Abel EJ, Downs TM, Kelcz F, Jarrard DF. Addressing the need for repeat prostate biopsy: new technology and approaches. Nature Reviews Urology. 2015 Aug;12(8):435. [PubMed: 26171803]