Prediction of prostate cancer in unscreened men: external validation of a risk calculator. Eur J Cancer. 2011;47:903-909. [PMID: 21163642 DOI: 10.1016/j.ejca.2010.11.012]

Risk Calculator Strategy Before Magnetic Resonance Imaging Stratification for Biopsy-naïve Men with Suspicion for Prostate Cancer: A Cost-effectiveness Analysis

Background and objective

Current guidelines on prostate cancer (PCa) diagnosis recommend risk stratification before prostate biopsy, using either a risk calculator (RC) or magnetic resonance imaging (MRI). The aim of our study was to assess the effectiveness and cost effectiveness of an RC strategy and a direct MRI (dMRI) strategy.

Methods

Data for biopsy-naïve men suspected of having PCa on the basis of elevated prostate specific antigen (PSA) and/or abnormal digital rectal examination (DRE) were retrospectively collected from two large teaching hospitals. The RC and dMRI strategies were evaluated for PCa detection, effectiveness, and costs. The RC strategy used the Rotterdam prostate cancer risk calculator 3/4 and MRI for stratification, while the dMRI strategy directly used MRI findings. Clinically significant (cs)PCa was defined as a Gleason score ≥3 + 4.

Key findings and limitations

In total, 1458 men were included for analysis, of whom 944 were in the RC group and 514 were in the dMRI group. The RC strategy significantly reduced MRI use by 47.8% (52.2% vs 99.8%; p < 0.001) and reduced costs by 14.3% (€422.45 vs €492.77; p < 0.001) in comparison to the dMRI strategy. The number of patients who underwent prostate biopsy (36.5% vs. 40.9%; p = 0.11) and the csPCa detection rate (43.5% vs 45.2%; p = 0.69) were similar between the groups. The study is limited by its retrospective nature, so the findings should be interpreted with caution.

Conclusions and clinical implications

Both the RC strategy and the dMRI strategy are viable options for PCa diagnosis, with the former significantly reducing MRI use and overall diagnostic costs per person. Therefore, the RC strategy might be preferred over dMRI, particularly in contexts aiming for sustainable health care practices that optimize resource allocation and cost effectiveness.

Patient summary

We compared two different approaches for men with a suspicion of prostate cancer. One uses a risk calculator to decide on whether to perform an MRI (magnetic resonance imaging) scan, and the other proceeds directly to MRI. In both cases, prostate biopsy is performed in cases with positive MRI findings. The number of patients who needed a biopsy and the cancer detection rate were similar for the two approaches.

External validation of the Rotterdam prostate cancer risk calculator within a high-risk Dutch clinical cohort

PURPOSE

This study aims to externally validate the Rotterdam Prostate Cancer Risk Calculator (RPCRC)-3/4 and RPCRC-MRI within a Dutch clinical cohort.

METHODS

Men subjected to prostate biopsies, between 2018 and 2021, due to a clinical suspicion of prostate cancer (PCa) were retrospectively included. The performance of the RPCRC-3/4 and RPCRC-MRI was analyzed in terms of discrimination, calibration and net benefit. In addition, the need for recalibration and adjustment of risk thresholds for referral was investigated. Clinically significant (cs) PCa was defined as Gleason score ≥ 3 + 4.

RESULTS

A total of 1575 men were included in the analysis. PCa was diagnosed in 63.2% (996/1575) of men and csPCa in 41.7% (656/1575) of men. Use of the RPCRC-3/4 could have prevented 37.3% (587/1575) of all MRIs within this cohort, thereby missing 18.3% (120/656) of csPCa diagnoses. After recalibration and adjustment of risk thresholds to 20% for PCa and 10% for csPCa, use of the recalibrated RPCRC-3/4 could have prevented 15.1% (238/1575) of all MRIs, resulting in 5.3% (35/656) of csPCa diagnoses being missed. The performance of the RPCRC-MRI was good; use of this risk calculator could have prevented 10.7% (169/1575) of all biopsies, resulting in 1.2% (8/656) of csPCa diagnoses being missed.

CONCLUSION

The RPCRC-3/4 underestimates the probability of having csPCa within this Dutch clinical cohort, resulting in significant numbers of csPCa diagnoses being missed. For optimal performance of a risk calculator in a specific cohort, evaluation of its performance within the population under study is essential.

Augmenting prostate magnetic resonance imaging reporting to incorporate diagnostic recommendations based upon clinical risk calculators

Risk calculators have offered a viable tool for clinicians to stratify patients at risk of prostate cancer (PCa) and to mitigate the low sensitivity and specificity of screening prostate specific antigen (PSA). While initially based on clinical and demographic data, incorporation of multiparametric magnetic resonance imaging (MRI) and the validated prostate imaging reporting and data system suspicion scoring system has standardized and improved risk stratification beyond the use of PSA and patient parameters alone. Biopsy-naïve patients with lower risk profiles for harboring clinically significant PCa are often subjected to uncomfortable, invasive, and potentially unnecessary prostate biopsy procedures. Incorporating risk calculator data into prostate MRI reports can broaden the role of radiologists, improve communication with clinicians primarily managing these patients, and help guide clinical care in directing the screening, detection, and risk stratification of PCa.

External Validation of the Prostate Biopsy Collaborative Group Risk Calculator and the Rotterdam Prostate Cancer Risk Calculator in a Swedish Population-based Screening Cohort

Background

External validation of risk calculators (RCs) is necessary to determine their clinical applicability beyond the setting in which these were developed.

Objective

To assess the performance of the Rotterdam Prostate Cancer RC (RPCRC) and the Prostate Biopsy Collaborative Group RC (PBCG-RC).

Design, setting, and participants

We used data from the prospective, population-based STHLM3 screening study, performed in 2012–2015. Participants with prostate-specific antigen ≥3 ng/ml who underwent systematic prostate biopsies were included.

Outcome measurements and statistical analysis

Probabilities for clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology grade ≥2, were calculated for each participant. External validity was assessed by calibration, discrimination, and clinical usefulness for both original and recalibrated models.

Results and limitations

Out of 5841 men, 1054 (18%) had csPCa. Distribution of risk predictions differed between RCs; median risks for csPCa using the RPCRC and PBCG-RC were 3.3% (interquartile range [IQR] 2.1–7.1%) and 20% (IQR 15–28%), respectively. The correlation between RC risk estimates on individual level was moderate (Spearman’s r = 0.55). Using the RPCRC’s recommended risk threshold of ≥4% for finding csPCa, 36% of participants would get concordant biopsy recommendations. At 10% risk cut-off, RCs agreed in 23% of cases. Both RCs showed good discrimination, with areas under the curves for the RPCRC of 0.74 (95% confidence interval [CI] 0.72–0.76) and the PBCG-RC of 0.70 (95% CI 0.68–0.72). Calibration was adequate using the PBCG-RC (calibration slope: 1.13 [95% CI 1.03–1.23]), but the RPCRC underestimated the risk of csPCa (calibration slope: 0.73 [0.68–0.79]). The PBCG-RC showed a net benefit in a decision curve analysis, whereas the RPCRC showed no net benefit at clinically relevant risk threshold levels. Recalibration improved clinical benefit, and differences between RCs decreased.

Conclusions

Assessment of calibration is essential to ensure the clinical value of risk prediction tools. The PBCG-RC provided clinical benefit in its current version online. On the contrary, the RPCRC cannot be recommended in this setting.

Patient summary

Predicting the probability of finding prostate cancer on biopsy differed between two assessed risk calculators. After recalibration, the agreement of the models improved, and both were shown to be clinically useful.

MRI-based nomogram for the prediction of prostate cancer diagnosis: A multi-centre validated patient–physician decision tool

Objective:

To update and externally validate a magnetic resonance imaging (MRI)-based nomogram for predicting prostate biopsy outcomes with a multi-centre cohort.

Materials and methods:

Prospective data from five UK-based centres were analysed. All men were biopsy naïve. Those with missing data, no MRI, or prostate-specific antigen (PSA) > 30 ng/mL were excluded. Logistic regression analysis was used to confirm predictors of prostate cancer outcomes including MRI-PIRADS (Prostate Imaging Reporting and Data System) score, PSA density, and age. Clinically significant disease was defined as International Society of Urological Pathology (ISUP) Grade Group ⩾ 2 (Gleason grade ⩾ 7). Biopsy strategy included transrectal and transperineal approaches. Nomograms were produced using logistic regression analysis results.

Results:

A total of 506 men were included in the analysis with median age 66 (interquartile range (IQR) = 60–69). Median PSA was 6.6 ng/mL (IQR = 4.72–9.26). PIRADS ⩾ 3 was reported in 387 (76.4%). Grade Group ⩾ 2 detection was 227 (44.9%) and 318 (62.8%) for any cancer. Performance of the MRI-based nomogram was an area under curve (AUC) of 0.84 (95% confidence interval (CI) = 0.81–0.88) for Grade Group ⩾ 2% and 0.85 (95% CI = 0.82–0.88) for any prostate cancer.

Conclusion:

We present external validation of a novel MRI-based nomogram in a multi-centre UK-based cohort, showing good discrimination in identifying men at high risk of having clinically significant disease. These findings support this risk calculator use in the prostate biopsy decision-making process.

Level of evidence:

2c

Individual Patient Data Meta-analysis of Discrimination of the Four Kallikrein Panel Associated With the Inclusion of Prostate Volume

OBJECTIVE

To assess whether adding prostate volume to the kallikrein panel improves discrimination for ISUP Grade Group 2 or higher (GG2+) disease, as some men may have volume measurements available at the time of blood draw. While prostate volume predicts biopsy outcome, it requires an imaging procedure for measurement. The four kallikrein panel - commercially available as the 4Kscore - predicts risk of GG2+ disease and requires only a blood draw.

MATERIALS AND METHODS

A total of 9131 patients with available prostate volume and total PSA ≤25 ng/ml from 5 historical (sextant biopsy, pre-ISUP 2005 grading) and 4 contemporary cohorts (10+ cores, ISUP 2005 grading). Previously published kallikrein panel models were used to predict risk of GG2+. Volume was added to the model in each cohort and change in discrimination was meta-analyzed.

RESULTS

Increased prostate volume was associated with decreased risk of GG2+ disease after controlling for the kallikrein panel in 7/9 cohorts. However, kallikrein panel discrimination (0.817, 95% CI 0.802, 0.831) was not improved after including volume (AUC difference 0.002, 95% CI -0.003, 0.006). Heterogeneity (P <.0001) was driven by an AUC increase in 1 cohort of academic cancer centers (0.044, 95% CI 0.025, 0.064), with no evidence of heterogeneity after excluding this cohort (P = .15).

CONCLUSION

The kallikrein panel provides a non-invasive approach to assess the risk of high-grade prostate cancer. Our results do not justify the inclusion of prostate volume in the four kallikrein panel. There is some evidence that the predictive value of prostate volume is provider dependent: further research is needed to address this question.

Predicting high-grade prostate cancer at initial biopsy: clinical performance of the ExoDx (EPI) Prostate Intelliscore test in three independent prospective studies

Background

The ability to discriminate indolent from clinically significant prostate cancer (PC) at the initial biopsy remains a challenge. The ExoDx Prostate (IntelliScore) (EPI) test is a noninvasive liquid biopsy that quantifies three RNA targets in urine exosomes. The EPI test stratifies patients for risk of high-grade prostate cancer (HGPC; ≥ Grade Group 2 [GG] PC) in men ≥ 50 years with equivocal prostate-specific antigen (PSA) (2–10 ng/mL). Here, we present a pooled meta-analysis from three independent prospective-validation studies in men presenting for initial biopsy decision.

Methods

Pooled data from two prospective multi-site validation studies and the control arm of a clinical utility study were analyzed. Performance was evaluated using the area under the receiver-operating characteristic curve (AUC), negative predictive value (NPV), positive predictive value (PPV), sensitivity, and specificity for discriminating ≥ GG2 from GG1 and benign pathology.

Results

The combined cohort (n = 1212) of initial-biopsy subjects had a median age of 63 years and median PSA of 5.2 ng/mL. The EPI AUC (0.70) was superior to PSA (0.56), Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) (0.62), and The European Randomized Study of Screening for Prostate Cancer (ERSPC) (0.59), (all p-values <0.001) for discriminating GG2 from GG1 and benign histology. The validated cutoff of 15.6 would avoid 23% of all prostate biopsies and 30% of “unnecessary” (benign or Gleason 6/GG1) biopsies, with an NPV of 90%.

Conclusions

EPI is a noninvasive, easy-to-use, urine exosome–RNA assay that has been validated across 3 independent prospective multicenter clinical trials with 1212 subjects. The test can discriminate high-grade (≥GG2) from low-grade (GG1) cancer and benign disease. EPI effectively guides the biopsy-decision process independent of PSA and other standard-of-care factors.

Prostate Cancer Risk Calculators for Healthy Populations: Systematic Review

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Trial Registration

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

Tumour markers of prostate cancer: The post-PSA era

Although PSA-based prostate cancer (PCa) screening had a positive impact in reducing PCa mortality, it also led to overdiagnosis, overtreatment and to a significant number of unnecessary biopsies. In the post-PSA era, new biomarkers have emerged that can complement the information given by PSA, towards a better cancer diagnostic specificity, and also allow a better estimate of the aggressiveness of the disease and its clinical outcome. That means those markers have the potential to assist the clinician in the decision-making processes, such as whether or not to perform a biopsy, and to make the best treatment choice among the new therapeutic options available, including active surveillance (AS) in lower risk disease. In this article, we will review several of those more recent diagnostic markers (4Kscore®, [-2]proPSA and Prostate Health Index (PHI), SelectMDx®, ConfirmMDx®, Progensa® Prostate Cancer Antigen 3, Mi-Prostate Score, ExoDx™ Prostate Test, the Stockholm-3 test and ERSPC risk calculators) and prognostic markers (OncotypeDX® Genomic Prostate Score, Prolaris®, Decipher® and ProMark®). We will also address some new liquid biopsy approaches - circulating tumour cells and cell-free DNA (cfDNA) - with a potential role in metastatic castration-resistant PCa and will briefly give some future perspectives, mostly outlooking epigenetic markers.

Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection

Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate cancer. Peripheral blood serum obtained from 436 men undergoing transrectal ultrasound guided biopsy were assessed for a panel of 18 inflammatory serum biomarkers in addition to Total and Free Prostate Specific Antigen (PSA). This panel was integrated into a previously developed Irish clinical risk calculator (IPRC) for the detection of prostate cancer and high-grade prostate cancer (Gleason Score ≥ 7). Using logistic regression and multinomial regression methods, two models (Logst-RC and Multi-RC) were developed considering linear and nonlinear effects of the panel in conjunction with clinical and demographic parameters for determination of the two endpoints. Both models significantly improved the predictive ability of the clinical model for detection of prostate cancer (from 0.656 to 0.731 for Logst-RC and 0.713 for Multi-RC) and high-grade prostate cancer (from 0.716 to 0.785 for Logst-RC and 0.767 for Multi-RC) and demonstrated higher clinical net benefit. This improved discriminatory power and clinical utility may allow for individualised risk stratification improving clinical decision making.

Urine Extracellular Vesicle GATA2 mRNA Discriminates Biopsy Result in Men with Suspicion of Prostate Cancer

PURPOSE

Prostate specific antigen has limited performance in detecting prostate cancer. The transcription factor GATA2 is expressed in aggressive prostate cancer. We analyzed the predictive value of urine extracellular vesicle GATA2 mRNA alone and in combination with a multigene panel to improve detection of prostate cancer and high risk disease.

MATERIALS AND METHODS

GATA2 mRNA was analyzed in matched extracellular vesicles isolated from urines before and after prostatectomy (16) and paired urine and tissue prostatectomy samples (19). Extracellular vesicle GATA2 mRNA performance to distinguish prostate cancer and high grade disease was tested in training (52) and validation (165) cohorts. The predictive value of a multigene score including GATA2, PCA3 and TMPRSS2-ERG (GAPT-E) was tested in both cohorts.

RESULTS

Confirming its prostate origin, urine extracellular vesicle GATA2 mRNA levels decreased significantly after prostatectomy and correlated with prostate cancer tissue GATA2 mRNA levels. In the training and validation cohort GATA2 discriminated prostate cancer (AUC 0.74 and 0.66) and high grade disease (AUC 0.78 and 0.65), respectively. Notably, the GAPT-E score improved discrimination of prostate cancer (AUC 0.84 and 0.72) and high grade cancer (AUC 0.85 and 0.71) in both cohorts when compared with each biomarker alone and PT-E (PCA3 and TMPRSS2-ERG). A GAPT-E score for high grade prostate cancer would avoid 92.1% of unnecessary prostate biopsies, compared to 61.9% when a PT-E score is used.

CONCLUSIONS

Urine extracellular vesicle GATA2 mRNA analysis improves the detection of high risk prostate cancer and may reduce the number of unnecessary biopsies.

Prospective development of a prostate cancer risk calculator in a racially diverse population: The Kaiser Permanente Prostate Cancer Risk Calculator

PURPOSE

To prospectively develop a prostate cancer (CaP) risk calculator in a racially diverse population.

MATERIALS AND METHODS

All patients referred for prostate biopsy due to an elevated prostate-specific antigen or abnormal digital rectal exam in a 19-months period at Kaiser Permanente Northern California underwent a standardized systematic, ultrasound-guided biopsy scheme (14-cores for initial biopsy, 18-20 cores for repeat biopsy). All pertinent clinical variables were prospectively collected. The highest Gleason score for each patient was recorded for all positive biopsies. We used a split sample design to develop and validate 3 multivariable prediction models using multinomial logistic regression with the least absolute shrinkage and selection operator. All models included these core variables: age, race, prostate-specific antigen, prior biopsy status, body mass index, and family history of CaP. Model 1 included only the core variables, Model 2 added digital rectal exam, and Model 3 added digital rectal exam and prostate volume. We considered 3 outcomes: high-grade disease (Gleason score ≥7), low-grade disease (Gleason score = 6), and no cancer. Predictive discrimination was quantified using the c-statistic.

RESULTS

Complete data were available for 2,967 patients. Cancer was found in 50% of patients: of these, 58% were Gleason score ≥7 and 42% were low grade. Compared to Caucasians, African Americans were at a higher risk while Asians and Hispanics were at a lower risk for overall and high-grade cancer detection. The number of prior negative biopsies was also protective for these outcomes. The c-statistics for Model 1, 2, and 3 to predict high-grade disease vs. low-grade or no cancer were 0.76, 0.79, and 0.85, respectively. The c-statistics for Model 1, 2, and 3 to predict any CaP vs. no cancer were 0.69, 0.70, and 0.79, respectively. All models were well calibrated for all outcomes.

CONCLUSIONS

In men with elevated PSA levels, our calculator provides useful information that may enhance the shared decision-making process regarding the need for biopsy.

The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study

Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.

Validating the European randomised study for screening of prostate cancer (ERSPC) risk calculator in a contemporary South African cohort

INTRODUCTION

Numerous prostate cancer predictive tools have been developed to help with decision-making in men needing prostate biopsy. However, they have been modelled and validated almost exclusively in Caucasian cohorts, hence limiting their use in other population groups. The aim of this study was to assess the validity of the ERSPC risk calculator in a South African cohort.

METHODS

Patients who have had a transrectal ultrasound (TRUS)-guided prostate biopsy at Groote Schuur Hospital from January 2008 to August 2017 were reviewed. Predictor variables were entered into the ERSPC risk calculator and results were compared with prostate biopsy pathology results. Predictive accuracy of the ERSPC risk calculator for these patients was derived using receiver operator characteristics (ROC) Area under the curve and is expressed as a percentage.

RESULTS

516 prostate biopsy sessions in 475 different men were analysed. The predictive accuracy of the ERSPC risk calculator was better than a PSA/DRE strategy for the presence of cancer-0.738 (95% CI 0.695-0.781) vs 0.686 (95% CI 0.639-0.732), and for significant PCa-0.833 (95% CI 0.789-0.876) vs 0.793 (95% CI 0.741-0.846). This translated into 50 less biopsies when compared to a PSA > 4/abnormal DRE strategy. Use of the ERSPC RC would have missed eight non-significant cancers [Significant cancer being defined as having a tumour stage T2b (> 1/2 lobe involved with prostate cancer) and/or a Gleason Score equal to or greater than 7].

CONCLUSION

Our results confirm the validity of the ERSPC RC in a South African cohort. Application of this calculator to the wider South African population would allow better selection of patients for prostate biopsy and spare a significant number its adverse consequences.

A Prospective Adaptive Utility Trial to Validate Performance of a Novel Urine Exosome Gene Expression Assay to Predict High-grade Prostate Cancer in Patients with Prostate-specific Antigen 2-10ng/ml at Initial Biopsy

BACKGROUND

Discriminating indolent from clinically significant prostate cancer (PCa) in the initial biopsy setting remains an important issue. Prospectively evaluated diagnostic assays are necessary to ensure efficacy and clinical adoption.

OBJECTIVE

Performance and utility assessment of ExoDx Prostate (IntelliScore) (EPI) urine exosome gene expression assay versus standard clinical parameters for discriminating Grade Group (GG) ≥2 PCa from GG1 PCa and benign disease on initial biopsy.

DESIGN, SETTING, AND PARTICIPANTS

A two-phase adaptive clinical utility study (NCT03031418) comparing EPI results with biopsy outcomes in men, with age ≥50 yr and prostate-specific antigen (PSA) 2-10ng/ml, scheduled for initial prostate biopsy. After EPI performance assessment during phase I, a clinical implementation document (ie, CarePath) was developed for utilizing the EPI test in phase II, where the biopsy decision is uncertain.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Performance evaluation of the EPI test in patients enrolled in phase I and publication of a consensus CarePath for phase II.

RESULTS AND LIMITATIONS

In a total of 503 patients, with median age of 64 yr, median PSA 5.4ng/ml, 14% African American, 70% Caucasian, 53% positive biopsy rate (22% GG1, 17% GG2, and 15% ≥ GG3), EPI was superior to an optimized model of standard clinical parameters with an area under the curve (AUC) 0.70 versus 0.62, respectively, comparable with previously published results (n=519 patients, EPI AUC 0.71). Validated cut-point 15.6 would avoid 26% of unnecessary prostate biopsies and 20% of total biopsies, with negative predictive value (NPV) 89% and missing 7% of ≥GG2 PCa. Alternative cut-point 20 would avoid 40% of unnecessary biopsies and 31% of total biopsies, with NPV 89% and missing 11% of ≥GG2 PCa. The clinical investigators reached consensus recommending use of the 15.6 cut-point for phase II. Outcome of the decision impact cohort in phase II will be reported separately.

CONCLUSIONS

EPI is a noninvasive, easy-to-use, gene expression urine assay, which has now been successfully validated in over 1000 patients across two prospective validation trials to stratify risk of ≥GG2 from GG1 cancer and benign disease. The test improves identification of patients with higher grade disease and would reduce the total number of unnecessary biopsies.

PATIENT SUMMARY

It is challenging to predict which men are likely to have high-grade prostate cancer (PCa) at initial biopsy with prostate-specific antigen 2-10ng/ml. This study further demonstrates that the ExoDx Prostate (IntelliScore) test can predict ≥GG2 PCa at initial biopsy and defer unnecessary biopsies better than existing risk calculator's and standard clinical data.

Prediction of High-grade Prostate Cancer Following Multiparametric Magnetic Resonance Imaging: Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculators

BACKGROUND

The Rotterdam European Randomized Study of Screening for Prostate Cancer risk calculators (ERSPC-RCs) help to avoid unnecessary transrectal ultrasound-guided systematic biopsies (TRUS-Bx). Multivariable risk stratification could also avoid unnecessary biopsies following multiparametric magnetic resonance imaging (mpMRI).

OBJECTIVE

To construct MRI-ERSPC-RCs for the prediction of any- and high-grade (Gleason score ≥3 + 4) prostate cancer (PCa) in 12-core TRUS-Bx±MRI-targeted biopsy (MRI-TBx) by adding Prostate Imaging Reporting and Data System (PI-RADS) and age as parameters to the ERSPC-RC3 (biopsy-naïve men) and ERSPC-RC4 (previously biopsied men).

DESIGN, SETTING, AND PARTICIPANTS

A total of 961 men received mpMRI and 12-core TRUS-Bx±MRI-TBx (in case of PI-RADS ≥3) in five institutions. Data of 504 biopsy-naïve and 457 previously biopsied men were used to adjust the ERSPC-RC3 and ERSPC-RC4.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Logistic regression models were constructed. The areas under the curve (AUCs) of the original ERSPC-RCs and MRI-ERSPC-RCs (including PI-RADS and age) for any- and high-grade PCa were compared. Decision curve analysis was performed to assess the clinical utility of the MRI-ERSPC-RCs.

RESULTS AND LIMITATIONS

MRI-ERSPC-RC3 had a significantly higher AUC for high-grade PCa compared with the ERSPC-RC3: 0.84 (95% confidence interval [CI] 0.81-0.88) versus 0.76 (95% CI 0.71-0.80, p<0.01). Similarly, MRI-ERSPC-RC4 had a higher AUC for high-grade PCa compared with the ERSPC-RC4: 0.85 (95% CI 0.81-0.89) versus 0.74 (95% CI 0.69-0.79, p<0.01). Unlike for the MRI-ERSPC-RC3, decision curve analysis showed clear net benefit of the MRI-ERSPC-RC4 at a high-grade PCa risk threshold of ≥5%. Using a ≥10% high-grade PCa risk threshold to biopsy for the MRI-ERSPC-RC4, 36% biopsies are saved, missing low- and high-grade PCa, respectively, in 15% and 4% of men who are not biopsied.

CONCLUSIONS

We adjusted the ERSPC-RCs for the prediction of any- and high-grade PCa in 12-core TRUS-Bx±MRI-TBx. Although the ability of the MRI-ERSPC-RC3 for biopsy-naïve men to avoid biopsies remains questionable, application of the MRI-ERSPC-RC4 in previously biopsied men in our cohort would have avoided 36% of biopsies, missing high-grade PCa in 4% of men who would not have received a biopsy.

PATIENT SUMMARY

We have constructed magnetic resonance imaging-based Rotterdam European Randomized study of Screening for Prostate Cancer (MRI-ERSPC) risk calculators for prostate cancer prediction in transrectal ultrasound-guided biopsy and MRI-targeted biopsy by incorporating age and Prostate Imaging Reporting and Data System score into the original ERSPC risk calculators. The MRI-ERSPC risk calculator for previously biopsied men could be used to avoid one-third of biopsies following MRI.

Multivariable risk-based patient selection for prostate biopsy in a primary health care setting: referral rate and biopsy results from a urology outpatient clinic

Background

According to their guidelines, Dutch general practitioners (GPs) refer men with prostate-specific antigen (PSA) level ≥3.0 ng/mL to the urologist for risk-based patient selection for prostate biopsy using the Rotterdam Prostate Cancer Risk Calculator (RPCRC). Use of the RPCRC in primary care could optimize the diagnostic pathway even further by reducing unnecessary referrals. To investigate this, we calculated the risk and assessed the rate of men referred to the urologist with PSA level ≥3.0 ng/mL by implementing the RPCRC in a primary health care setting.

Methods

In January 2014, an exploratory study was initiated in collaboration with the primary health care facility of the GP laboratory in Rotterdam. GPs were given the possibility to refer men with a suspicion of prostate cancer (PCa) or a screening wish to this primary care facility (STAR-SHL) where further assessment was performed by specially trained personnel. Risk-based advice on referral to the urologist was given to the GP on the basis of the RPCRC results. If requested, advice on the treatment of lower urinary tract symptoms (LUTS) was provided. All men signed informed consent.

Results

Between January 2014 and September 2017, a total of 243 men, median age 64 [interquartile range (IQR), 57-70] years were referred for a consultation at the primary care facility. Of the 108 men with PSA level ≥3.0 ng/mL and a referral related to PCa, GPs were advised to refer 58 men to the urologist (54%). Of the men with available follow-up (FU) data [n=187, median FU, 16 (IQR, 9-25) months] 54 men were considered high-risk (i.e., had an elevated risk of PCa as calculated by the RPCRC). Of these men, 51 (94%) were actually referred to secondary care by their GP, and so far 38 men underwent biopsy. PCa was detected in 30 men [47% had Gleason score (GS) ≥3+4 PCa], translating to an overall positive predictive value (PPV) of 79%. Within the available FU time, 2 out of 38 (5%) men with PSA level ≥3.0 ng/mL which were considered low-risk have been diagnosed with GS 3+3 PCa.

Conclusions

Risk-stratification with the RPCRC in a primary health care setting could prevent almost half of referrals of men with PSA level ≥3.0 ng/mL to the urologist. In more than three-quarters of men referred for prostate biopsy, the suspicion of PCa was confirmed and almost half of men had clinically significant PCa (GS ≥3+4 PCa). These data show a huge potential for multivariable risk-stratification in primary care.

Next-generation prostate cancer risk calculator for primary care physicians

INTRODUCTION

Current prostate cancer risk calculators are limited in impact because only a probability of having prostate cancer is provided. We developed the next generation of prostate cancer risk calculator that incorporates life expectancy in order to better evaluate prostate cancer risk in context to a patient's age and comorbidity.

METHODS

We combined two cohorts to develop the new risk calculator. The first was 5638 subjects who all underwent a prostate biopsy for prostate cancer detection. The second was 979 men diagnosed with prostate cancer with long-term survival data. Two regression models were used to create multivariable nomograms and an online prostate cancer risk calculator was developed.

RESULTS

Of the 5638 patients who underwent a prostate biopsy, 629 (11%) were diagnosed with aggressive prostate cancer (Gleason Score 7[4+3] or more). Of the 979 patients who underwent treatment for prostate cancer, the 10-year overall survival (OS) was 49.6% (95% confidence interval [CI] 46.6-52.9). The first multivariable nomogram for cancer risk had a concordance index of 0.74 (95% CI 0.72, 0.76), and the second nomogram to predict survival had a concordance index of 0.71 (95% CI 0.69-0.72). The next-generation prostate cancer risk calculator was developed online and is available at: http://riskcalc.org/ProstateCA_Screen_Tool.

CONCLUSIONS

We have developed the next-generation prostate cancer risk calculator that incorporates a patient's life expectancy based on age and comorbidity. This approach will better evaluate prostate cancer risk. Future studies examining other populations will be needed for validation.

Atypical small acinar proliferation at index prostate biopsy: rethinking the re-biopsy paradigm

PURPOSE

Guidelines for atypical small acinar proliferation (ASAP) diagnosed on prostate biopsy recommend repeat biopsy within 3-6 months after diagnosis. We sought to discern the rate of detecting clinically significant prostate cancer on repeat biopsy and predictors associated with progression.

MATERIALS AND METHODS

We performed a retrospective chart review of patients who underwent prostate biopsy at our institution from January 1, 2008, to December 31, 2015. Gleason grade group (GGG) system and D'Amico stratification were used to report pathology and risk stratification, respectively. Logistic and linear regression analyses were performed.

RESULTS

A total of 593 patients underwent transrectal ultrasound-guided prostate biopsy, of which 27 (4.6%) had the diagnosis of ASAP. Of these, 11 (41%) had a repeat biopsy. Median time from diagnosis to repeat biopsy was 147 days (IQR 83.5-247.0). Distribution across the GGG system on repeat biopsy was as follows: 7 (63.6%) benign, 3 (27.3%) GG1, and 1 (9.1%) GG2. ASAP was not associated with subsequent diagnosis of clinically significant prostate cancer (OR 0.46, 95% CI 0.064-3.247, P = 0.432). There was no association between ASAP and high cancer risk (ASAP: β = - 0.12; P = 0.204).

CONCLUSIONS

Patients diagnosed with ASAP managed according to guideline recommendations are more likely diagnosed with benign pathology and indolent prostate cancer on repeat biopsy. These findings support prior studies suggesting refinement of guidelines in regard to the appropriateness and timeliness of repeat biopsy among patients diagnosed with ASAP.

Prospective evaluation on the effect of interobserver variability of digital rectal examination on the performance of the Rotterdam Prostate Cancer Risk Calculator

OBJECTIVES

To assess the level of agreement between digital rectal examination findings of two urologists and its effect on risk prediction using the digital rectal examination-based Rotterdam Prostate Cancer Risk Calculator.

METHODS

The study sample consisted of a prospective cohort of asymptomatic unscreened men with prostate-specific antigen ≤50.0 ng/mL and transrectal ultrasound volume ≤110 mL who underwent transrectal ultrasound-guided prostate biopsy. Both urologists' digital rectal examination findings were graded normal or abnormal (nodularity and/or induration), and volume classified as 25, 40 or 60 mL, according to the risk calculator algorithm. Interrater agreement analysis using Cohen's kappa (κ) statistic was carried out to determine consistency of digital rectal examination outcome and volume assessment. Receiver operating characteristic curve analysis and calibration plots were constructed to determine the effect of interrater differences. Decision curve analysis was applied to evaluate the clinical usefulness of the model.

RESULTS

Of the 241 men included in the study, 41% (n = 98) had prostate cancer (81 were clinically significant, i.e. Gleason ≥3 + 4). There was substantial agreement in the digital rectal examination (abnormal/normal; κ = 0.78; P < 0.001) and volume estimation (κ = 0.79; P < 0.001). Receiver operating characteristic analyses showed good discrimination (0.75-0.78) and were comparable for both urologists. In the high-risk cohort, at a probability threshold of 25%, the risk calculator reduced the prostate biopsy rate by 9%, without missing cancers.

CONCLUSIONS

Slight differences in digital rectal examination findings seem to have very limited impact on the performance of the Rotterdam Prostate Cancer Risk Calculator. Therefore, this can be considered a useful prostate biopsy outcome prediction tool.

Characteristics of Prostate Cancer Found at Fifth Screening in the European Randomized Study of Screening for Prostate Cancer Rotterdam: Can We Selectively Detect High-grade Prostate Cancer with Upfront Multivariable Risk Stratification and Magnetic Resonance Imaging?

BACKGROUND

The harm of screening (unnecessary biopsies and overdiagnosis) generally outweighs the benefit of reducing prostate cancer (PCa) mortality in men aged ≥70 yr. Patient selection for biopsy using risk stratification and magnetic resonance imaging (MRI) may improve this benefit-to-harm ratio.

OBJECTIVE

To assess the potential of a risk-based strategy including MRI to selectively identify men aged ≥70 yr with high-grade PCa.

DESIGN, SETTING, AND PARTICIPANTS

Three hundred and thirty-seven men with prostate-specific antigen ≥3.0 ng/ml at a fifth screening (71-75 yr) in the European Randomized study of Screening for Prostate Cancer Rotterdam were biopsied. One hundred and seventy-nine men received six-core transrectal ultrasound biopsy (TRUS-Bx), while 158 men received MRI, 12-core TRUS-Bx, and fusion TBx in case of Prostate Imaging Reporting and Data System ≥3 lesions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was the overall, low-grade (Gleason Score 3+3) and high-grade (Gleason Score ≥ 3+4) PCa rate. Secondary outcome was the low- and high-grade PCa rate detected by six-core TRUS-Bx, 12-core TRUS-Bx, and MRI ± TBx. Tertiary outcome was the reduction of biopsies and low-grade PCa detection by upfront risk stratification with the Rotterdam Prostate Cancer Risk Calculator 4.

RESULTS AND LIMITATIONS

Fifty-five percent of men were previously biopsied. The overall, low-grade, and high-grade PCa rates in biopsy naïve men were 48%, 27%, and 22%, respectively. In previously biopsied men these PCa rates were 25%, 20%, and 5%. Sextant TRUS-Bx, 12-core TRUS-Bx, and MRI ± TBx had a similar high-grade PCa rate (11%, 12%, and 11%) but a significantly different low-grade PCa rate (17%, 28%, and 7%). Rotterdam Prostate Cancer Risk Calculator 4-based stratification combined with 12-core TRUS-Bx ± MRI-TBx would have avoided 65% of biopsies and 68% of low-grade PCa while detecting an equal percentage of high-grade PCa (83%) compared with a TRUS-Bx all men approach (79%).

CONCLUSIONS

After four repeated screens and ≥1 previous biopsies in half of men, a significant proportion of men aged ≥70 yr still harbor high-grade PCa. Upfront risk stratification and the combination of MRI and TRUS-Bx would have avoided two-thirds of biopsies and low-grade PCa diagnoses in our cohort, while maintaining the high-grade PCa detection of a TRUS-Bx all men approach. Further studies are needed to verify these results.

PATIENT SUMMARY

Prostate cancer screening reduces mortality but is accompanied by unnecessary biopsies and overdiagnosis of nonaggressive tumors, especially in repeatedly screened elderly men. To tackle these drawbacks screening should consist of an upfront risk-assessment followed by magnetic resonance imaging and transrectal ultrasound-guided biopsy.

Additional benefit of using a risk-based selection for prostate biopsy: an analysis of biopsy complications in the Rotterdam section of the European Randomized Study of Screening for Prostate Cancer

OBJECTIVE

To investigate biopsy complications and hospital admissions that could be reduced by the use of European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculators.

MATERIALS AND METHODS

All biopsies performed in the Rotterdam section of the ERSPC between 1993 and 2015 were included. Biopsy complications and hospital admission data were prospectively recorded in questionnaires that were completed 2 weeks after biopsy. The ERSPC risk calculators 3 (RC3) and 4 (RC4) were applied to men attending the first and subsequent rounds of screening, respectively. Applying the predefined RC3/4 probability thresholds for prostate cancer (PCa) risk of ≥12.5% and high-grade PCa risk ≥3%, we assessed the number of complications, admissions and costs that could be reduced by avoiding biopsies in men below these thresholds.

RESULTS

A total of 10 747 biopsies with complete questionnaires were included. For these biopsies a complication rate of 67.9% (7294/10 747), a post-biopsy fever rate of 3.9% (424/10747) and a hospital admission rate of 0.9% (92/10747) were recorded. The fever rate was found to be static over the years, but the hospital admission rate tripled from 0.6% (1993-1996) to 2.1% (2009-2015). Among 7704 biopsies which fit the criteria for RC3 or RC4, 35.8% of biopsies (2757/7704), 37.4% of complications (1972/5268), 39.4% of fever events (128/325) and 42.3% of admissions (30/71) could have been avoided by using one of the risk calculators. More complications could have been avoided if RC4 had been used and for more recent biopsies (2009-2015). Our findings show that 35.9% of the total cost of biopsies and complication treatment could have been avoided.

CONCLUSION

A significant proportion of biopsy complications, hospital admissions and costs could be reduced if biopsy decisions were based on ERSPC risk calculators instead of PSA only. This effect was most prominent in more recent biopsies and in men with repeated biopsies or screening.

Improving the Rotterdam European Randomized Study of Screening for Prostate Cancer Risk Calculator for Initial Prostate Biopsy by Incorporating the 2014 International Society of Urological Pathology Gleason Grading and Cribriform growth

BACKGROUND

The survival rate for men with International Society of Urological Pathology (ISUP) grade 2 prostate cancer (PCa) without invasive cribriform (CR) and intraductal carcinoma (IDC) is similar to that for ISUP grade 1. If updated into the European Randomized Study of Screening for Prostate Cancer (ERSPC Rotterdam) risk calculator number 3 (RC3), this may further improve upfront selection of men who need a biopsy.

OBJECTIVE

To improve the number of possible biopsies avoided, while limiting undiagnosed clinically important PCa by applying the updated RC3 for risk-based patient selection.

DESIGN, SETTING, AND PARTICIPANTS

The RC3 is based on the first screening round of the ERSPC Rotterdam, which involved 3616 men. In 2015, histopathologic slides for PCa cases (n=885) were re-evaluated. Low-risk (LR) PCa was defined as ISUP grade 1 or 2 without CR/IDC. High-risk (HR) PCa was defined as ISUP grade 2 with CR/IDC and PCa with ISUP grade≥3.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We updated the RC3 using multinomial logistic regression analysis, including data on age, PSA, digital rectal examination, and prostate volume, for predicting LR and HR PCa. Predictive accuracy was quantified using receiver operating characteristic analysis and decision curve analysis.

RESULTS AND LIMITATIONS

Men without PCa could effectively be distinguished from men with LR PCa and HR PCa (area under the curve 0.70, 95% confidence interval [CI] 0.68-0.72 and 0.92, 95% CI 0.90-0.94). At a 1% risk threshold, the updated calculator would lead to a 34% reduction in unnecessary biopsies, while only 2% of HR PCa cases would be undiagnosed.

CONCLUSIONS

A relatively simple risk stratification tool augmented with a highly sensitive contemporary pathologic biopsy classification would result in a considerable decrease in unnecessary prostate biopsies and overdiagnosis of potentially indolent disease.

PATIENT SUMMARY

We improved a well-known prostate risk calculator with a new pathology classification system that better reflects disease burden. This new risk calculator allows individualized prediction of the chance of having (potentially aggressive) biopsy-detectable prostate cancer and can guide shared decision-making when considering prostate biopsy.

Adaptation and external validation of the European randomised study of screening for prostate cancer risk calculator for the Chinese population

BACKGROUND

To adapt the well-performing European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator to the Chinese setting and perform an external validation.

METHODS

The original ERSPC risk calculator 3 (RC3) for prostate cancer (PCa) and high-grade PCa (HGPCa) was applied to a development cohort of 3006 previously unscreened Hong Kong Chinese men with initial transrectal biopsies performed from 1997 to 2015, age 50-80 years, PSA 0.4-50 ng ml^-1 and prostate volume 10-150 ml. A simple adaptation to RC3 was performed and externally validated in a cohort of 2214 Chinese men from another Hong Kong hospital. The performance of the models were presented in calibration plots, area under curve (AUC) of receiver operating characteristics (ROCs) and decision curve analyses.

RESULTS

PCa and HGPCa was diagnosed in 16.7% (503/3006) and 7.8% (234/3006) men in the development cohort, and 20.2% (447/2204) and 9.7% (214/2204) men in the validation cohort, respectively. The AUCs using the original RC3 model in the development cohort were 0.75 and 0.84 for PCa and HGPCa, respectively, but the calibration plots showed considerable overestimation. In the external validation of the recalibrated RC3 model, excellent calibration was observed, and discrimination was good with AUCs of 0.76 and 0.85 for PCa and HGPCa, respectively. Decision curve analyses in the validation cohort showed net clinical benefit of the recalibrated RC3 model over PSA.

CONCLUSIONS

A recalibrated ERSPC risk calculator for the Chinese population was developed, and it showed excellent discrimination, calibration and net clinical benefit in an external validation cohort.

Prediction of Prostate Cancer: External Validation of the ERSPC Risk Calculator in a Contemporary Dutch Clinical Cohort

BACKGROUND

The validity of prediction models needs external validation to assess their value beyond the original development setting.

OBJECTIVE

To report the diagnostic accuracy of the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC)3 and RC4 in a contemporary Dutch clinical cohort.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively identified all men who underwent prostate biopsy (PBx) in the Jeroen Bosch Hospital, The Netherlands, between 2007 and 2016. Patients were included if they met ERSPC RC requirements of age (50-80 yr), prostate-specific antigen (PSA) (0.4-50 ng/ml), and prostate volume (10-150ml). The probability of a positive biopsy for prostate cancer (PCa) and significant PCa (Gleason score ≥7 and/or higher than T2b) were calculated and compared with PBx pathology results.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Evaluation was performed by calibration, discrimination, and clinical usefulness using calibration plots, area under the receiver operating characteristic curves (AUCs), and decision curve analyses (DCAs), respectively.

RESULTS AND LIMITATIONS

A total of 2270 PBx sessions were eligible for final analysis. Discriminative ability of RC3 (AUC) was 0.78 and 0.90 for any PCa and significant PCa, respectively. For RC4 the calculated AUCs were 0.62 (any PCa) and 0.76 (significant PCa). The calibration plots of RC3 showed good results for both any PCa risk and significant PCa risk. In the repeat PBx group, RC4 tended to underestimate outcomes for PCa and showed moderate calibration for significant PCa. DCA showed an overall net benefit compared with PSA and digital rectal examination (DRE) alone. Limitations of this study are its retrospective single-institution design, retrospectively assessed DRE outcomes, no time restrictions between the first and repeat biopsy sessions, and no anterior sampling in the repeat PBx protocol.

CONCLUSIONS

The ERSPC RCs performed well in a contemporary clinical setting. Most pronounced in the biopsy-naive group, both RCs should be favoured over a PSA plus DRE-based stratification in the decision whether or not to perform PBx.

PATIENT SUMMARY

We looked at the ability of the existing European Randomized Study of Screening for Prostate Cancer risk calculator (RC), using different clinical data to predict the presence of prostate cancer in Dutch men. The RC performed well and should be favoured in the decision of whether or not to perform prostate biopsies over the conventional diagnostic pathway.

Does the molecular classification of breast cancer point the way for biomarker identification in prostate cancer?

There is significant variation in clinical outcome between patients diagnosed with prostate cancer (CaP). Although useful, statistical nomograms and risk stratification tools alone do not always accurately predict an individual’s need for and response to treatment. The factors that determine this variation are not fully elucidated. In particular, cellular response to androgen ablation and subsequent paracrine/autocrine adaptation is poorly understood and despite best therapies, median survival in castrate resistant patients is only approximately 35 mo. We propose that one way of understanding this is to look for correlates in other comparable malignancies, such as breast cancer, where markers of at least 4 distinct gene clusters coding for 4 different phenotypic subtypes have been identified. These subtypes have been shown to demonstrate prognostic significance and successfully guide appropriate treatment regimens. In this paper we assess and review the evidence demonstrating parallels in the biology and treatment approach between breast and CaP, and consider the feasibility of patients with CaP being stratified into different molecular classes that could be used to complement prostate specific antigen and histological grading for clinical decision making. We show that there are significant correlations between the molecular classification of breast and CaP and explain how techniques used successfully to predict response to treatment in breast cancer can be applied to the prostate. Molecular phenotyping is possible in CaP and identification of distinct subtypes may allow personalised risk stratification way beyond that currently available.

The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation

BACKGROUND

There is no single definitive test to identify prostate cancer in men. Biopsies are commonly used to obtain samples of prostate tissue for histopathological examination. However, this approach frequently misses cases of cancer, meaning that repeat biopsies may be necessary to obtain a diagnosis. The PROGENSA(®) prostate cancer antigen 3 (PCA3) assay (Hologic Gen-Probe, Marlborough, MA, USA) and the Prostate Health Index (phi; Beckman Coulter Inc., Brea, CA, USA) are two new tests (a urine test and a blood test, respectively) that are designed to be used to help clinicians decide whether or not to recommend a repeat biopsy.

OBJECTIVE

To evaluate the clinical effectiveness and cost-effectiveness of the PCA3 assay and the phi in the diagnosis of prostate cancer.

DATA SOURCES

Multiple publication databases and trial registers were searched in May 2014 (from 2000 to May 2014), including MEDLINE, EMBASE, The Cochrane Library, ISI Web of Science, Medion, Aggressive Research Intelligence Facility database, ClinicalTrials.gov, International Standard Randomised Controlled Trial Number Register and World Health Organization International Clinical Trials Registry Platform.

REVIEW METHODS

The assessment of clinical effectiveness involved three separate systematic reviews, namely reviews of the analytical validity, the clinical validity of these tests and the clinical utility of these tests. The assessment of cost-effectiveness comprised a systematic review of full economic evaluations and the development of a de novo economic model.

SETTING

The perspective of the evaluation was the NHS in England and Wales.

PARTICIPANTS

Men suspected of having prostate cancer for whom the results of an initial prostate biopsy were negative or equivocal.

INTERVENTIONS

The use of the PCA3 score or phi in combination with existing tests (including histopathology results, prostate-specific antigen level and digital rectal examination), multiparametric magnetic resonance imaging and clinical judgement.

RESULTS

In addition to documents published by the manufacturers, six studies were identified for inclusion in the analytical validity review. The review identified issues concerning the precision of the PCA3 assay measurements. It also highlighted issues relating to the storage requirements and stability of samples intended for analysis using the phi assay. Fifteen studies met the inclusion criteria for the clinical validity review. These studies reported results for 10 different clinical comparisons. There was insufficient evidence to enable the identification of appropriate test threshold values for use in a clinical setting. In addition, the implications of adding either the PCA3 assay or the phi to clinical assessment were not clear. Furthermore, the addition of the PCA3 assay or the phi to clinical assessment plus magnetic resonance imaging was not found to improve discrimination. No published papers met the inclusion criteria for either the clinical utility review or the cost-effectiveness review. The results from the cost-effectiveness analyses indicated that using either the PCA3 assay or the phi in the NHS was not cost-effective.

LIMITATIONS

The main limitations of the systematic review of clinical validity are that the review conclusions are over-reliant on findings from one study, the descriptions of clinical assessment vary widely within reviewed studies and many of the reported results for the clinical validity outcomes do not include either standard errors or confidence intervals.

CONCLUSIONS

The clinical benefit of using the PCA3 assay or the phi in combination with existing tests, scans and clinical judgement has not yet been confirmed. The results from the cost-effectiveness analyses indicate that the use of these tests in the NHS would not be cost-effective.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42014009595.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculators significantly outperform the Prostate Cancer Prevention Trial (PCPT) 2.0 in the prediction of prostate cancer: a multi-institutional study

OBJECTIVE

To analyse the performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) and two iterations of the European Randomised Study of Screening for Prostate Cancer (ERSPC) Risk Calculator, one of which incorporates prostate volume (ERSPC-RC) and the other of which incorporates prostate volume and the prostate health index (PHI) in a referral population (ERSPC-PHI).

PATIENTS AND METHODS

The risk of prostate cancer (PCa) and significant PCa (Gleason score ≥7) in 2001 patients from six tertiary referral centres was calculated according to the PCPT-RC and ERSPC-RC formulae. The calculators' predictions were analysed using the area under the receiver-operating characteristic curve (AUC), calibration plots, Hosmer-Lemeshow test for goodness of fit and decision-curve analysis. In a subset of 222 patients for whom the PHI score was available, each patient's risk was calculated as per the ERSPC-RC and ERSPC-PHI risk calculators.

RESULTS

The ERSPC-RC outperformed the PCPT-RC in the prediction of PCa, with an AUC of 0.71 compared with 0.64, and also outperformed the PCPT-RC in the prediction of significant PCa (P<0.001), with an AUC of 0.74 compared with 0.69. The ERSPC-RC was found to have improved calibration in this cohort and was associated with a greater net benefit on decision-curve analysis for both PCa and significant PCa. The performance of the ERSPC-RC was further improved through the addition of the PHI score in a subset of 222 patients. The AUCs of the ERSPC-PHI were 0.76 and 0.78 for PCa and significant PCa prediction, respectively, in comparison with AUC values of 0.72 in the prediction of both PCa and significant PCa for the ERSPC-RC (P = 0.12 and P = 0.04, respectively). The ERSPC-PHI risk calculator was well calibrated in this cohort and had an increase in net benefit over that of the ERSPC-RC.

CONCLUSIONS

The performance of the risk calculators in the present cohort shows that the ERSPC-RC is a superior tool in the prediction of PCa; however the performance of the ERSPC-RC in this population does not yet warrant its use in clinical practice. The incorporation of the PHI score into the ERSPC-PHI risk calculator allowed each patient's risk to be more accurately quantified. Individual patient risk calculation using the ERSPC-PHI risk calculator can be undertaken in order to allow a systematic approach to patient risk stratification and to aid in the diagnosis of PCa.

Atypical Small Acinar Proliferation: Repeat Biopsy and Detection of High Grade Prostate Cancer

Purpose. Atypical small acinar proliferation (ASAP) is diagnosed in 1-2% of prostate biopsies. 30–40% of patients with ASAP may be diagnosed with prostate cancer (PCa) on repeat biopsy. Our objective was to examine the association between ASAP and subsequent diagnosis of intermediate/high risk PCa. Materials and Methods. Ninety-six patients who underwent prostate biopsy from 2000 to 2013 and were diagnosed with ASAP were identified. Clinicopathologic features were analyzed. Comparison was made between those with subsequent PCa on repeat biopsy and those with benign repeat pathology. Results. 56/96 (58%) patients had a repeat biopsy. 22/56 (39%) were subsequently diagnosed with PCa. There was no significant difference in patients' characteristics. Presence of HGPIN on initial biopsy was associated with a benign repeat biopsy (68% versus 23%). 17/22 (77%) had Gleason grade (GG) 3+3 disease and only 5/22 (23%) had GG 3+4 disease. Conclusions. 22/56 patients (39%) of patients who underwent a subsequent prostate biopsy following a diagnosis of ASAP were found to have PCa. 77% of these men were diagnosed with GG 3+3 PCa. Only 23% were found to have intermediate risk PCa and no high risk PCa was identified. Immediate repeat prostate biopsy in patients diagnosed with ASAP may be safely delayed. A multi-institutional cohort is being analyzed.

Do prostate cancer risk models improve the predictive accuracy of PSA screening? A meta-analysis

BACKGROUND

Despite the extensive development of risk prediction models to aid patient decision-making on prostate screening, it is unknown whether these models could improve predictive accuracy of PSA testing to detect prostate cancer (PCa). The objective of this study was to perform a systematic review to identify PCa risk models and to assess the model's performance to predict PCa by conducting a meta-analysis.

DESIGN

A systematic literature search of Medline was conducted to identify PCa predictive risk models that used at least two variables, of which one of the variables was prostate-specific antigen (PSA) level. Model performance (discrimination and calibration) was assessed. Prediction models validated in ≥5 study populations and reported area under the curve (AUC) for prediction of any or clinically significant PCa were eligible for meta-analysis. Summary AUC and 95% CIs were calculated using a random-effects model.

RESULTS

The systematic review identified 127 unique PCa prediction models; however, only six models met study criteria for meta-analysis for predicting any PCa: Prostataclass, Finne, Karakiewcz, Prostate Cancer Prevention Trial (PCPT), Chun, and the European Randomized Study of Screening for Prostate Cancer Risk Calculator 3 (ERSPC RC3). Summary AUC estimates show that PCPT does not differ from PSA testing (0.66) despite performing better in studies validating both PSA and PCPT. Predictive accuracy to discriminate PCa increases with Finne (AUC = 0.74), Karakiewcz (AUC = 0.74), Chun (AUC = 0.76) and ERSPC RC3 and Prostataclass have the highest discriminative value (AUC = 0.79), which is equivalent to doubling the sensitivity of PSA testing (44% versus 21%) without loss of specificity. The discriminative accuracy of PCPT to detect clinically significant PCa was AUC = 0.71. Calibration measures of the models were poorly reported.

CONCLUSIONS

Risk prediction models improve the predictive accuracy of PSA testing to detect PCa. Future developments in the use of PCa risk models should evaluate its clinical effectiveness in practice.

Prostate-specific antigen-based prostate cancer screening: Past and future

Prostate-specific antigen-based prostate cancer screening remains a controversial topic. Up to now, there is worldwide consensus on the statement that the harms of population-based screening, mainly as a result of overdiagnosis (the detection of clinically insignificant tumors that would have never caused any symptoms), outweigh the benefits. However, worldwide opportunistic screening takes place on a wide scale. The European Randomized Study of Screening for Prostate Cancer showed a reduction in prostate cancer mortality through prostate-specific antigen based-screening. These population-based data need to be individualized in order to avoid screening in those who cannot benefit and start screening in those who will. For now, lacking a more optimal screening approach, screening should only be started after the process of shared decision-making. The focus of future research is the reduction of unnecessary testing and overdiagnosis by further research to better biomarkers and the value of the multiparametric magnetic resonance imaging, potentially combined in already existing prostate-specific antigen-based multivariate risk prediction models.

Left lobe of the prostate during clinical prostate cancer screening: the dark side of the gland for right-handed examiners

BACKGROUND

The predictive value of the abnormality side during digital rectal examination (DRE) has never been studied, suggesting that physicians examined the left lobe of the gland as well as the right lobe. We aimed to assess the predictive value of the side of DRE abnormality for prostate cancer (PCa) detection and aggressiveness in right-handed urologists.

METHODS

An analysis of a prospective database was carried out that included all consecutive men undergoing prostate biopsies between 2001 and 2012. The main end point was the predictive value of the abnormality side during DRE for cancer detection in clinically suspicious unilateral T2 disease. The diagnostic performance of left- versus right-sided abnormality was also assessed in terms of sensitivity, specificity and negative/positive predictive values.

RESULTS

Overall, 308 patients had a suspicious unilateral clinical disease (detection rate 57.5%). The cancer detection rate was significantly higher in case of left-sided compared with right-sided clinical T2 stage (odds ratio 2.1). In case of left-sided disease, the number of positive cores, the rate of perineural invasion, the rate of primary grade 4 pattern and the percentage of cancer involvement per core were significantly higher compared with those reported for right-sided disease. The predictive value of abnormality laterality for cancer detection and aggressiveness remained statistically independent in multivariate models. The positive predictive value for cancer detection was 64.6 in case of suspicious left-sided disease versus 46.9 in case of right-sided disease.

CONCLUSIONS

The risks of detecting PCa and aggressive disease on biopsy are significantly higher when DRE reveals a suspicious left-sided clinical disease as compared with right-sided disease. Right-handed physicians should be aware of this variance in diagnostic performance and potential underdetection of left-sided clinical disease, and should improve their examination of the left lobe of the gland by conducting longer exams or changing the patient's position.

The Kallikrein Panel for prostate cancer screening: its economic impact

BACKGROUND

Current diagnostic testing for prostate cancer results in numerous unnecessary biopsy procedures and creates a substantial financial burden. A statistical prediction model for prostate cancer has been developed, based on four Kallikrein markers in blood. This systematic review and meta-analysis examines the aggregated results from published studies of the Kallikrein Panel.

METHODS

Literature searches to identify relevant studies were conducted. A meta-analysis of the results was performed using inverse variance, mean difference with corresponding 95% confidence intervals (CI). The results of the meta-analysis were used to assess the Kallikrein Panel's effect on healthcare costs.

RESULTS

The Kallikrein Panel has been evaluated in more than 8,500 patients (2,780 with prostate cancer and 598 with high grade prostate cancer). Meta-analysis demonstrates a statistically significant improvement of 8-10% in predictive accuracy. In addition, 48% to 56% of current prostate biopsies could be avoided. Use of the Kallikrein Panel could result in annual US savings approaching $1 billion.

CONCLUSIONS

The Kallikrein Panel has the potential to improve patient outcomes and reduce costs. The panel provides significantly improved specificity. Because the Kallikrein Panel has been studied in a range of clinical settings, it is a test that could be readily and widely used in practice.

Risk-based prostate cancer screening: who and how?

The purpose of this review is to identify clinical risk factors for prostate cancer and to assess the utility and limitations of our current tools for prostate cancer screening. Prostate-specific antigen is the single most important factor for identifying men at increased risk of prostate cancer but is best assessed in the context of other clinical factors; increasing age, race, and family history are well-established risk factors for the diagnosis of prostate cancer. In addition to clinical risk calculators, novel tools such as multiparametric imaging, serum or urinary biomarkers, and genetic profiling show promise in improving prostate cancer diagnosis and characterization. Optimal use of existing and future tools will help alleviate the problems of overdiagnosis and overtreatment of low-risk prostate cancer without reversing the substantial mortality declines that have been achieved in the screening era.

Early detection of prostate cancer: European Association of Urology recommendation

BACKGROUND

The recommendations and the updated EAU guidelines consider early detection of PCa with the purpose of reducing PCa-related mortality and the development of advanced or metastatic disease.

OBJECTIVE

This paper presents the recommendations of the European Association of Urology (EAU) for early detection of prostate cancer (PCa) in men without evidence of PCa-related symptoms.

EVIDENCE ACQUISITION

The working panel conducted a systematic literature review and meta-analysis of prospective and retrospective clinical studies on baseline prostate-specific antigen (PSA) and early detection of PCa and on PCa screening published between 1990 and 2013 using Cochrane Reviews, Embase, and Medline search strategies.

EVIDENCE SYNTHESIS

The level of evidence and grade of recommendation were analysed according to the principles of evidence-based medicine. The current strategy of the EAU recommends that (1) early detection of PCa reduces PCa-related mortality; (2) early detection of PCa reduces the risk of being diagnosed and developing advanced and metastatic PCa; (3) a baseline serum PSA level should be obtained at 40-45 yr of age; (4) intervals for early detection of PCa should be adapted to the baseline PSA serum concentration; (5) early detection should be offered to men with a life expectancy ≥ 10 yr; and (6) in the future, multivariable clinical risk-prediction tools need to be integrated into the decision-making process.

CONCLUSIONS

A baseline serum PSA should be offered to all men 40-45 yr of age to initiate a risk-adapted follow-up approach with the purpose of reducing PCa mortality and the incidence of advanced and metastatic PCa. In the future, the development and application of multivariable risk-prediction tools will be necessary to prevent over diagnosis and over treatment.

Can Western based online prostate cancer risk calculators be used to predict prostate cancer after prostate biopsy for the Korean population?

PURPOSE

To access the predictive value of the European Randomized Screening of Prostate Cancer Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC) in the Korean population.

MATERIALS AND METHODS

We retrospectively analyzed the data of 517 men who underwent transrectal ultrasound guided prostate biopsy between January 2008 and November 2010. Simple and multiple logistic regression analysis were performed to compare the result of prostate biopsy. Area under the receiver operating characteristics curves (AUC-ROC) and calibration plots were prepared for further analysis to compare the risk calculators and other clinical variables.

RESULTS

Prostate cancer was diagnosed in 125 (24.1%) men. For prostate cancer prediction, the area under curve (AUC) of the ERSPC-RC was 77.4%. This result was significantly greater than the AUCs of the PCPT-RC and the prostate-specific antigen (PSA) (64.5% and 64.1%, respectively, p<0.01), but not significantly different from the AUC of the PSA density (PSAD) (76.1%, p=0.540). When the results of the calibration plots were compared, the ERSPC-RC plot was more constant than that of PSAD.

CONCLUSION

The ERSPC-RC was better than PCPT-RC and PSA in predicting prostate cancer risk in the present study. However, the difference in performance between the ERSPC-RC and PSAD was not significant. Therefore, the Western based prostate cancer risk calculators are not useful for urologists in predicting prostate cancer in the Korean population.

External validation of the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate Cancer risk calculators in a Chinese cohort

Several prediction models have been developed to estimate the outcomes of prostate biopsies. Most of these tools were designed for use with Western populations and have not been validated across different ethnic groups. Therefore, we evaluated the predictive value of the Prostate Cancer Prevention Trial (PCPT) and the European Randomized Study of Screening for Prostate Cancer (ERSPC) risk calculators in a Chinese cohort. Clinicopathological information was obtained from 495 Chinese men who had undergone extended prostate biopsies between January 2009 and March 2011. The estimated probabilities of prostate cancer and high-grade disease (Gleason >6) were calculated using the PCPT and ERSPC risk calculators. Overall measures, discrimination, calibration and clinical usefulness were assessed for the model evaluation. Of these patients, 28.7% were diagnosed with prostate cancer and 19.4% had high-grade disease. Compared to the PCPT model and the prostate-specific antigen (PSA) threshold of 4 ng ml(-1), the ERSPC risk calculator exhibited better discriminative ability for predicting positive biopsies and high-grade disease (the area under the curve was 0.831 and 0.852, respectively, P<0.01 for both). Decision curve analysis also suggested the favourable clinical utility of the ERSPC calculator in the validation dataset. Both prediction models demonstrated miscalibration: the risk of prostate cancer and high-grade disease was overestimated by approximately 20% for a wide range of predicted probabilities. In conclusion, the ERSPC risk calculator outperformed both the PCPT model and the PSA threshold of 4 ng ml(-1) in predicting prostate cancer and high-grade disease in Chinese patients. However, the prediction tools derived from Western men significantly overestimated the probability of prostate cancer and high-grade disease compared to the outcomes of biopsies in a Chinese cohort.

Risk-based prostate cancer screening

CONTEXT

Widespread mass screening of prostate cancer (PCa) is not recommended because the balance between benefits and harms is still not well established. The achieved mortality reduction comes with considerable harm such as unnecessary biopsies, overdiagnoses, and overtreatment. Therefore, patient stratification with regard to PCa risk and aggressiveness is necessary to identify those men who are at risk and may actually benefit from early detection.

OBJECTIVE

This review critically examines the current evidence regarding risk-based PCa screening.

EVIDENCE ACQUISITION

A search of the literature was performed using the Medline database. Further studies were selected based on manual searches of reference lists and review articles.

EVIDENCE SYNTHESIS

Prostate-specific antigen (PSA) has been shown to be the single most significant predictive factor for identifying men at increased risk of developing PCa. Especially in men with no additional risk factors, PSA alone provides an appropriate marker up to 30 yr into the future. After assessment of an early PSA test, the screening frequency may be determined based on individualized risk. A limited list of additional factors such as age, comorbidity, prostate volume, family history, ethnicity, and previous biopsy status have been identified to modify risk and are important for consideration in routine practice. In men with a known PSA, risk calculators may hold the promise of identifying those who are at increased risk of having PCa and are therefore candidates for biopsy.

CONCLUSIONS

PSA testing may serve as the foundation for a more risk-based assessment. However, the decision to undergo early PSA testing should be a shared one between the patient and his physician based on information balancing its advantages and disadvantages.

Defining and predicting indolent and low risk prostate cancer

The early detection of asymptomatic prostate cancer has led to the increased incidence of tumours that are unlikely to become symptomatic during life, so called indolent cancers. The prediction of low risk and indolent prostate cancer is needed to avoid overtreatment by unnecessary invasive therapies, and select men for active surveillance. Some of the currently available nomograms predicting these low risk tumours have been validated in independent populations. However, assessment to the compliance with their treatment advises based on the calculation of probability are scarce. The ultimate value of nomograms for the urologic practice can only be assessed by analysing their practical implementation.

Compliance with biopsy recommendations of a prostate cancer risk calculator

Study Type - Diagnostic (cohort) Level of Evidence 2b What's known on the subject? and What does the study add? So far, few publications have shown that a prediction model influences the behaviour of both physicians and patients. To our knowledge, it was unknown whether urologists and patients are compliant with the recommendations of a prostate cancer risk calculator and their reasons for non-compliance. Recommendations of the European Randomized study of Screening for Prostate Cancer risk calculator (ERSPC RC) about the need of a prostate biopsy were followed in most patients. In most cases of non-compliance with 'no biopsy' recommendations, a PSA level ≥ 3 ng/mL was decisive to opt for biopsy. Before implementation of the ERSPC RC in urological practices at a large scale, it is important to obtain insight into the use of guidelines that might counteract the adoption of the use of the RC as a result of opposing recommendations.

OBJECTIVES

To assess both urologist and patient compliance with a 'no biopsy' or 'biopsy' recommendation of the European Randomized study of Screening for Prostate Cancer (ERSPC) Risk Calculator (RC), as well as their reasons for non-compliance. To assess determinants of patient compliance.

PATIENTS AND METHODS

The ERSPC RC calculates the probability on a positive sextant prostate biopsy (P(posb) ) using serum prostate-specific antigen (PSA) level, outcomes of digital rectal examination and transrectal ultrasonography, and ultrasonographically assessed prostate volume. A biopsy was recommended if P(posb) ≥20%. Between 2008 and 2011, eight urologists from five Dutch hospitals included 443 patients (aged 55-75 years) after a PSA test with no previous biopsy. Urologists calculated the P(posb) using the RC in the presence of patients and completed a questionnaire about compliance. Patients completed a questionnaire about prostate cancer knowledge, attitude towards prostate biopsy, self-rated health (12-Item Short Form Health Survey), anxiety (State Trait Anxiety Inventory-6, Memorial Anxiety Scale for Prostate Cancer) and decision-making measures (Decisional Conflict Scale).

RESULTS

Both urologists and patients complied with the RC recommendation in 368 of 443 (83%) cases. If a biopsy was recommended, almost all patients (96%; 257/269) complied, although 63 of the 174 (36%) patients were biopsied against the recommendation of the RC. Compliers with a 'no biopsy' recommendation had a lower mean P(posb) than non-compliers (9% vs 14%; P < 0.001). Urologists opted for biopsies against the recommendations of the RC because of an elevated PSA level (≥ 3 ng/mL) (78%; 49/63) and patients because they wanted certainty (60%; 38/63).

CONCLUSIONS

Recommendations of the ERSPC RC on prostate biopsy were followed in most patients. The RC hence may be a promising tool for supporting clinical decision-making.

Avoiding pitfalls in applying prediction models, as illustrated by the example of prostate cancer diagnosis

BACKGROUND

The use of different mathematical models to support medical decisions is accompanied by increasing uncertainties when they are applied in practice. Using prostate cancer (PCa) risk models as an example, we recommend requirements for model development and draw attention to possible pitfalls so as to avoid the uncritical use of these models.

CONTENT

We conducted MEDLINE searches for applications of multivariate models supporting the prediction of PCa risk. We critically reviewed the methodological aspects of model development and the biological and analytical variability of the parameters used for model development. In addition, we reviewed the role of prostate biopsy as the gold standard for confirming diagnoses. In addition, we analyzed different methods of model evaluation with respect to their application to different populations. When using models in clinical practice, one must validate the results with a population from the application field. Typical model characteristics (such as discrimination performance and calibration) and methods for assessing the risk of a decision should be used when evaluating a model's output. The choice of a model should be based on these results and on the practicality of its use.

SUMMARY

To avoid possible errors in applying prediction models (the risk of PCa, for example) requires examining the possible pitfalls of the underlying mathematical models in the context of the individual case. The main tools for this purpose are discrimination, calibration, and decision curve analysis.