Predicting the outcome of prostate biopsy: comparison of a novel logistic regression-based model, the prostate cancer risk calculator, and prostate-specific antigen level alone

Re-evaluating the diagnostic efficacy of PSA as a referral test to detect clinically significant prostate cancer in contemporary MRI-based image-guided biopsy pathways

Introduction

Modern image-guided biopsy pathways at diagnostic centres have greatly refined the investigations of men referred with suspected prostate cancer. However, the referral criteria from primary care are still based on historical prostate-specific antigen (PSA) cut-offs and age-referenced thresholds. Here, we tested whether better contemporary pathways and biopsy methods had improved the predictive utility value of PSA referral thresholds.

Methods

PSA referral thresholds, age-referenced ranges and PSA density (PSAd) were assessed for positive predictive value (PPV) in detection of clinically significant prostate cancer (csPCa - histological ⩾ Grade Group 2). Data were analysed from men referred to three diagnostics centres who used multi-parametric magnetic resonance imaging (mpMRI)-guided prostate biopsies for disease characterisation. Findings were validated in a separate multicentre cohort. Results: Data from 2767 men were included in this study. The median age, PSA and PSAd were 66.4 years, 7.3 ng/mL and 0.1 ng/mL2, respectively. Biopsy detected csPCa was found in 38.7%. The overall area under the curve (AUC) for PSA was 0.68 which is similar to historical performance. A PSA threshold of ⩾ 3 ng/mL had a PPV of 40.3%, but this was age dependent (PPV: 24.8%, 32.7% and 56.8% in men 50-59 years, 60-69 years and ⩾ 70 years, respectively). Different PSA cut-offs and age-reference ranges failed to demonstrate better performance. PSAd demonstrated improved AUC (0.78 vs 0.68, p < 0.0001) and improved PPV compared to PSA. A PSAd of ⩾ 0.10 had a PPV of 48.2% and similar negative predictive value (NPV) to PSA ⩾ 3 ng/mL and out-performed PSA age-reference ranges. This improved performance was recapitulated in a separate multi-centre cohort (n = 541).

Conclusion

The introduction of MRI-based image-guided biopsy pathways does not appear to have altered PSA diagnostic test characteristics to positively detect csPCa. We find no added value to PSA age-referenced ranges, while PSAd offers better PPV and the potential for a single clinically useful threshold (⩾0.10) for all age groups.

Level of evidence

IV.

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

OBJECTIVE

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

DESIGN

Systematic review.

DATA SOURCES

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

ELIGIBILITY

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

DATA EXTRACTION AND SYNTHESIS

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

RESULTS

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

CONCLUSION

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

The combination of prostate imaging reporting and data system version 2 (PI-RADS v2) and periprostatic fat thickness on multi-parametric MRI to predict the presence of prostate cancer

PURPOSE

To evaluate the auxiliary effectiveness of periprostatic fat thickness (PPFT) on multi-parametric magnetic resonance imaging (mp-MRI) to Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) in predicting the presence of prostate cancer (PCa) and high-grade prostate cancer (HGPCa, Gleason Score ≥ 7).

RESULTS

Overall, there were 371 patients (54.3%) with PCa and 292 patients (42.8%) with HGPCa. The mean value of PPFT was 4.04 mm. Multivariate analysis revealed that age, prostatic specific antigen (PSA), volume, PI-RADS score, and PPFT were independent predictors of PCa. All factors plus abnormal digital rectal exam were independent predictors of HGPCa. In addition, the PPFT was the independent predictor of PCa (Odds ratio [OR] 2.56, p = 0.004) and HGPCa (OR 2.70, p = 0.014) for subjects with PI-RADS grade 3. The present two nomograms based on multivariate analysis outperformed the single PI-RADS in aspects of predicting accuracy for PCa (area under the curve: 0.922 vs. 0.883, p = 0.029) and HGPCa (0.919 vs. 0.873, p = 0.007). Decision-curve analysis also indicated the favorable clinical utility of the present two nomograms.

MATERIALS AND METHODS

The clinical data of 683 patients who received transrectal ultrasound guided biopsy and prior mp-MRI were reviewed. PPFT was measured as the shortest perpendicular distance from the pubic symphysis to the prostate on MRI. Univariate and multivariate analyses were performed to determine the independent predictors of PCa and HGPCa. We also constructed two nomograms for predicting PCa and HGPCa based on the logistic regression.

CONCLUSION

The PPFT on mp-MRI is an independent predictor of PCa and HGPCa, notably for patients with PI-RADS grade 3. The nomograms incorporated predictors of PPFT and PI-RADS demonstrated good predictive performance.

A nomogram for prediction of prostate cancer on multi-core biopsy using age, serum prostate-specific antigen, prostate volume and digital rectal examination in Singapore

AIM

To develop and internally validate two nomograms for predicting the probability of overall and clinically-significant prostate cancer on initial biopsy in a Singaporean population.

METHODS

Data were collected from men undergoing initial prostate biopsy at a single center. The indications for biopsy were serum prostate-specific antigen (PSA) ≥4.0 ng/mL or suspicious digital rectal examination (DRE) findings. Men with PSA >30 ng/mL were excluded. Age, PSA, prostate volume (PV) and DRE were predictors included in our logistic regression model and used to construct two nomograms for overall prostate cancer and clinically-significant (Gleason sum ≥7) cancer detection. Predictive accuracies of our nomograms were assessed using area under curve (AUC) of their receiver-operator characteristic curves. Internal validation was performed using the bootstrap method. Our nomograms were compared to a model based on PSA alone using AUC and decision curve analysis (DCA).

RESULTS

Out of 672 men analyzed, our positive biopsy rate was 26.2% (n = 176), of which 63.6% (n = 112) had clinically significant disease. Age, PSA, PV and DRE status were all independent risk factors for both overall prostate cancer detection as well as clinically-significant cancer detection (all P < 0.05). Our nomogram outperformed serum PSA for both overall and clinically-significant cancer detection (0.736 vs 0.642, P < 0.001 and 0.793 vs 0.696, P < 0.001, respectively). Using DCA, our nomograms had superior net benefit and net reduction in biopsy rate compared to PSA alone.

CONCLUSIONS

Our nomograms have been shown to be superior to PSA alone, on both AUC and DCA. However, it warrants external validation.

Prostate Cancer Predictive Simulation Modelling, Assessing the Risk Technique (PCP-SMART): Introduction and Initial Clinical Efficacy Evaluation Data Presentation of a Simple Novel Mathematical Simulation Modelling Method, Devised to Predict the Outcome of Prostate Biopsy on an Individual Basis

INTRODUCTION

We developed a mathematical "prostate cancer (PCa) conditions simulating" predictive model (PCP-SMART), from which we derived a novel PCa predictor (prostate cancer risk determinator [PCRD] index) and a PCa risk equation. We used these to estimate the probability of finding PCa on prostate biopsy, on an individual basis.

MATERIALS AND METHODS

A total of 371 men who had undergone transrectal ultrasound-guided prostate biopsy were enrolled in the present study. Given that PCa risk relates to the total prostate-specific antigen (tPSA) level, age, prostate volume, free PSA (fPSA), fPSA/tPSA ratio, and PSA density and that tPSA ≥ 50 ng/mL has a 98.5% positive predictive value for a PCa diagnosis, we hypothesized that correlating 2 variables composed of 3 ratios (1, tPSA/age; 2, tPSA/prostate volume; and 3, fPSA/tPSA; 1 variable including the patient's tPSA and the other, a tPSA value of 50 ng/mL) could operate as a PCa conditions imitating/simulating model. Linear regression analysis was used to derive the coefficient of determination (R²), termed the PCRD index. To estimate the PCRD index's predictive validity, we used the χ² test, multiple logistic regression analysis with PCa risk equation formation, calculation of test performance characteristics, and area under the receiver operating characteristic curve analysis using SPSS, version 22 (P < .05).

RESULTS

The biopsy findings were positive for PCa in 167 patients (45.1%) and negative in 164 (44.2%). The PCRD index was positively signed in 89.82% positive PCa cases and negative in 91.46% negative PCa cases (χ² test; P < .001; relative risk, 8.98). The sensitivity was 89.8%, specificity was 91.5%, positive predictive value was 91.5%, negative predictive value was 89.8%, positive likelihood ratio was 10.5, negative likelihood ratio was 0.11, and accuracy was 90.6%. Multiple logistic regression revealed the PCRD index as an independent PCa predictor, and the formulated risk equation was 91% accurate in predicting the probability of finding PCa. On the receiver operating characteristic analysis, the PCRD index (area under the curve, 0.926) significantly (P < .001) outperformed other, established PCa predictors.

CONCLUSION

The PCRD index effectively predicted the prostate biopsy outcome, correctly identifying 9 of 10 men who were eventually diagnosed with PCa and correctly ruling out PCa for 9 of 10 men who did not have PCa. Its predictive power significantly outperformed established PCa predictors, and the formulated risk equation accurately calculated the probability of finding cancer on biopsy, on an individual patient basis.

What risk of prostate cancer led urologist to recommend prostate biopsies?

OBJECTIVE

The aim of this study was to estimate the risk of prostate cancer that led urologists to perform prostate biopsies.

PATIENTS AND METHODS

Eight hundred and eight patients had prostate biopsies in 5 tertiary centres in 2010. Following data were collected: age, PSA, DRE, prostate volume, negative prior prostate biopsy and estimated life expectancy (> or <10 years). The risk of prostate cancer was calculated by validated nomogram of PCPT-CRC and SWOP-PRI and correlated with pathological biopsy results.

RESULTS

In final analysis, 625 patients were included, 568 (90.9%) had a life expectancy greater than 10 years. Prostate cancer was found in 291 (46.6%) cases. These patients were older (66.7 ± 6.8 vs 64.3 ± 5.6 years, P < 0.001), had higher PSA values (10 ± 7.9 vs 7.7 ± 4.3 ng/mL, P < 0.0001) and the prostate volume decreased (43.8 ± 19.8 vs 51.3 ± 20.7 mL, P < 0.0001) compared with healthy subjects. Digital Rectal Examination was more frequently suspicious in the group of patients with prostate cancer (43.6% vs 18.9%, P < 0.0001). Risk of prostate cancer estimated was 50.6 ± 14% for PCPT-CRC without ATCD, 56.2 ± 12.8% with PCPT-CRC ATCD and 31.2 ± 17.3% for SWOP-PRI. The likelihood of high-risk prostate cancer was 22.4 ± 16.9% with the PCPT-CRC, and 14.8 ± 18.2% with SWOP-PRI.

CONCLUSION

This study showed that urologists performed prostate biopsies when the risk of cancer was high.

Improving patient prostate cancer risk assessment: Moving from static, globally-applied to dynamic, practice-specific risk calculators

Clinical risk calculators are now widely available but have generally been implemented in a static and one-size-fits-all fashion. The objective of this study was to challenge these notions and show via a case study concerning risk-based screening for prostate cancer how calculators can be dynamically and locally tailored to improve on-site patient accuracy. Yearly data from five international prostate biopsy cohorts (3 in the US, 1 in Austria, 1 in England) were used to compare 6 methods for annual risk prediction: static use of the online US-developed Prostate Cancer Prevention Trial Risk Calculator (PCPTRC); recalibration of the PCPTRC; revision of the PCPTRC; building a new model each year using logistic regression, Bayesian prior-to-posterior updating, or random forests. All methods performed similarly with respect to discrimination, except for random forests, which were worse. All methods except for random forests greatly improved calibration over the static PCPTRC in all cohorts except for Austria, where the PCPTRC had the best calibration followed closely by recalibration. The case study shows that a simple annual recalibration of a general online risk tool for prostate cancer can improve its accuracy with respect to the local patient practice at hand.

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.

A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future validation

OBJECTIVES

To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC).

STUDY DESIGN AND SETTING

A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds.

RESULTS

In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively).

CONCLUSION

The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.

Incorporation of detailed family history from the Swedish Family Cancer Database into the PCPT risk calculator

PURPOSE

A detailed family history provides an inexpensive alternative to genetic profiling for individual risk assessment. We updated the PCPT Risk Calculator to include detailed family histories.

MATERIALS AND METHODS

The study included 55,168 prostate cancer cases and 638,218 controls from the Swedish Family Cancer Database who were 55 years old or older in 1999 and had at least 1 male first-degree relative 40 years old or older and 1 female first-degree relative 30 years old or older. Likelihood ratios, calculated as the ratio of risk of observing a specific family history pattern in a prostate cancer case compared to a control, were used to update the PCPT Risk Calculator.

RESULTS

Having at least 1 relative with prostate cancer increased the risk of prostate cancer. The likelihood ratio was 1.63 for 1 first-degree relative 60 years old or older at diagnosis (10.1% of cancer cases vs 6.2% of controls), 2.47 if the relative was younger than 60 years (1.5% vs 0.6%), 3.46 for 2 or more relatives 60 years old or older (1.2% vs 0.3%) and 5.68 for 2 or more relatives younger than 60 years (0.05% vs 0.009%). Among men with no diagnosed first-degree relatives the likelihood ratio was 1.09 for 1 or more second-degree relatives diagnosed with prostate cancer (12.7% vs 11.7%). Additional first-degree relatives with breast cancer, or first-degree or second-degree relatives with prostate cancer compounded these risks.

CONCLUSIONS

A detailed family history is an independent predictor of prostate cancer compared to commonly used risk factors. It should be incorporated into decision making for biopsy. Compared with other costly biomarkers it is inexpensive and universally available.

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.

Chinese nomogram to predict probability of positive initial prostate biopsy: a study in Taiwan region

Several nomograms for prostate cancer detection have recently been developed. Because the incidence of prostate cancer is lower in Chinese men, nomograms based on other populations cannot be directly applied to Chinese men. We, therefore, developed a model for predicting the probability of a positive initial prostate biopsy using clinical and laboratory data from a Chinese male population. Data were collected from 893 Chinese male referrals, 697 in the derivation set and 196 in the external validation set, who underwent initial prostate biopsies as individual screening. We analyzed age, prostate volume, total prostate-specific antigen (PSA), PSA density (PSAD), digital rectal examinations (DRE) and transrectal ultrasound (TRUS) echogenicity. Logistic regression analysis estimated odds ratio, 95% confidence intervals and P values. Independent predictors of a positive biopsy result included advanced age, small prostate volume, elevated total PSA, abnormal digital rectal examination, and hyperechoic or hypoechoic TRUS echogenicity. We developed a predictive nomogram for an initial positive biopsy using these variables. The area under the receiver-operating characteristic curve for the model was 88.8%, which was greater than that of the prediction based on total PSA alone (area under the receiver-operating characteristic curve 74.7%). If externally validated, the predictive probability was 0.827 and the accuracy rate was 78.1%, respectively. Incorporating clinical and laboratory data into a prebiopsy nomogram improved the prediction of prostate cancer compared with predictions based solely on the individual factors.

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.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

A nomogram based on age, prostate-specific antigen level, prostate volume and digital rectal examination for predicting risk of prostate cancer

Nomograms for predicting the risk of prostate cancer developed using other populations may introduce sizable bias when applied to a Chinese cohort. In the present study, we sought to develop a nomogram for predicting the probability of a positive initial prostate biopsy in a Chinese population. A total of 535 Chinese men who underwent a prostatic biopsy for the detection of prostate cancer in the past decade with complete biopsy data were included. Stepwise logistic regression was used to determine the independent predictors of a positive initial biopsy. Age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE) status, % free PSA and transrectal ultrasound (TRUS) findings were included in the analysis. A nomogram model was developed that was based on these independent predictors to calculate the probability of a positive initial prostate biopsy. A receiver-operating characteristic curve was used to assess the accuracy of using the nomogram and PSA levels alone for predicting positive prostate biopsy. The rate for positive initial prostate biopsy was 41.7% (223/535). The independent variables used to predict a positive initial prostate biopsy were age, PSA, PV and DRE status. The areas under the receiver-operating characteristic curve for a positive initial prostate biopsy for PSA alone and the nomogram were 79.7% and 84.8%, respectively. Our results indicate that the risk of a positive initial prostate biopsy can be predicted to a satisfactory level in a Chinese population using our nomogram. The nomogram can be used to identify and counsel patients who should consider a prostate biopsy, ultimately enhancing accuracy in diagnosing prostate cancer.

The risk of biopsy-detectable prostate cancer using the prostate cancer prevention Trial Risk Calculator in a community setting

MATERIALS AND METHODS

Risks of prostate cancer (CaP) and high-grade CaP were evaluated using the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) in an age-stratified random sample of 1,021 Caucasian and African-American men with no previous diagnosis of CaP, aged 55-74 years, residing in King County, WA, USA.

RESULTS

Median PCPTRC risks of CaP (high-grade CaP) were 15.6% (1.2%), 18.7% (2.0%), 18.5% (2.2%), and 26.4% (5.1%) for 55-59, 60-64, 65-69, and 70-74-year-old men, respectively; 25.2% of men aged 55-59 had a 25% or greater PCPTRC risk of CaP; this increased to 53.1% in men aged 70-74; 9.4% of men aged 55-59 had a 6% or greater PCPTRC risk of high-grade CaP, increasing to 44.1% in men aged 70-74.

CONCLUSIONS

PCPTRC risk of CaP in a community of US males is high and confounded with overdetected cancers. In contrast, average community PCPTRC risk of high-grade disease is low and increases gradually by age and may better serve for counseling purposes.

Updating risk prediction tools: a case study in prostate cancer

Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically, the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [-2]proPSA measured on an external case-control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network.

Evaluating the PCPT risk calculator in ten international biopsy cohorts: results from the Prostate Biopsy Collaborative Group

OBJECTIVES

To evaluate the discrimination, calibration, and net benefit performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) across five European randomized study of screening for prostate cancer (ERSPC), 1 United Kingdom, 1 Austrian, and 3 US biopsy cohorts.

METHODS

PCPTRC risks were calculated for 25,733 biopsies using prostate-specific antigen (PSA), digital rectal examination, family history, history of prior biopsy, and imputation for missing covariates. Predictions were evaluated using the areas underneath the receiver operating characteristic curves (AUC), discrimination slopes, chi-square tests of goodness of fit, and net benefit decision curves.

RESULTS

AUCs of the PCPTRC ranged from a low of 56% in the ERSPC Goeteborg Rounds 2-6 cohort to a high of 72% in the ERSPC Goeteborg Round 1 cohort and were statistically significantly higher than that of PSA in 6 out of the 10 cohorts. The PCPTRC was well calibrated in the SABOR, Tyrol, and Durham cohorts. There was limited to no net benefit to using the PCPTRC for biopsy referral compared to biopsying all or no men in all five ERSPC cohorts and benefit within a limited range of risk thresholds in all other cohorts.

CONCLUSIONS

External validation of the PCPTRC across ten cohorts revealed varying degree of success highly dependent on the cohort, most likely due to different criteria for and work-up before biopsy. Future validation studies of new calculators for prostate cancer should acknowledge the potential impact of the specific cohort studied when reporting successful versus failed validation.

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.

An epidemiological reappraisal of the familial aggregation of prostate cancer: a meta-analysis

Studies on familial aggregation of cancer may suggest an overall contribution of inherited genes or a shared environment in the development of malignant disease. We performed a meta-analysis on familial clustering of prostate cancer. Out of 74 studies reporting data on familial aggregation of prostate cancer in unselected populations retrieved by a Pubmed search and browsing references, 33 independent studies meeting the inclusion criteria were used in the analysis performed with the random effects model. The pooled rate ratio (RR) for first-degree family history, i.e. affected father or brother, is 2.48 (95% confidence interval: 2.25-2.74). The incidence rate for men who have a brother who got prostate cancer increases 3.14 times (CI:2.37-4.15), and for those with affected father 2.35 times (CI:2.02-2.72). The pooled estimate of RR for two or more affected first-degree family members relative to no history in father and in brother is 4.39 (CI:2.61-7.39). First-degree family history appears to increase the incidence rate of prostate cancer more in men under 65 (RR:2.87, CI:2.21-3.74), than in men aged 65 and older (RR:1.92, CI:1.49-2.47), p for interaction = 0.002. The attributable fraction among those having an affected first-degree relative equals to 59.7% (CI:55.6-63.5%) for men at all ages, 65.2% (CI:57.7-71.4%) for men younger than 65 and 47.9% (CI:37.1-56.8%) for men aged 65 or older. For those with a family history in 2 or more first-degree family members 77.2% (CI:65.4-85.0%) of prostate cancer incidence can be attributed to the familial clustering. Our combined estimates show strong familial clustering and a significant effect-modification by age meaning that familial aggregation was associated with earlier disease onset (before age 65).

Prospective multi-institutional study evaluating the performance of prostate cancer risk calculators

PURPOSE

Prostate cancer risk calculators incorporate many factors to evaluate an individual's risk for prostate cancer. We validated two common North American-based, prostate cancer risk calculators.

PATIENTS AND METHODS

We conducted a prospective, multi-institutional study of 2,130 patients who underwent a prostate biopsy for prostate cancer detection from five centers. We evaluated the performance of the Sunnybrook nomogram-based prostate cancer risk calculator (SRC) and the Prostate Cancer Prevention Trial (PCPT) -based risk calculator (PRC) to predict the presence of any cancer and high-grade cancer. We examined discrimination, calibration, and decision curve analysis techniques to evaluate the prediction models.

RESULTS

Of the 2,130 patients, 867 men (40.7%) were found to have cancer, and 1,263 (59.3%) did not have cancer. Of the patients with cancer, 403 (46.5%) had a Gleason score of 7 or more. The area under the [concentration-time] curve (AUC) for the SRC was 0.67 (95% CI, 0.65 to 0.69); the AUC for the PRC was 0.61 (95% CI, 0.59 to 0.64). The AUC was higher for predicting aggressive disease from the SRC (0.72; 95% CI, 0.70 to 0.75) compared with that from the PRC (0.67; 95% CI, 0.64 to 0.70). Decision curve analyses showed that the SRC performed better than the PRC for risk thresholds of more than 30% for any cancer and more than 15% for aggressive cancer.

CONCLUSION

The SRC performed better than the PRC, but neither one added clinical benefit for risk thresholds of less than 30%. Further research is needed to improve the AUCs of the risk calculators, particularly for higher-grade cancer.

The prostate cancer risk calculator from the Prostate Cancer Prevention Trial underestimates the risk of high grade cancer in contemporary referral patients

PURPOSE

The prostate cancer risk calculator from the Prostate Cancer Prevention Trial estimates the risk of positive biopsy and 1 containing high grade disease (Gleason score 7 or greater) based on prostate specific antigen, digital rectal examination, family history, race and prior negative biopsy. Since data used to create the calculator came from an unreferred population that underwent mainly sextant biopsy, to our knowledge its usefulness in the contemporary urology practice is unknown.

MATERIALS AND METHODS

We performed the same multivariate logistic regression used to derive the prostate cancer risk calculator in a cohort of men from the Stanford Prostate Needle Biopsy Database who underwent initial prostate needle biopsy using an extended 12-core scheme.

RESULTS

Our predictions of overall prostate cancer risk did not differ significantly from those of the calculator. Prostate specific antigen, abnormal digital rectal examination and family history were independent risk factors. However, our model predicted a much greater risk of high grade disease than the prostate cancer risk calculator. Prostate specific antigen, abnormal digital rectal examination and age were independent risk factors for high grade disease.

CONCLUSIONS

The difference between our estimated risk of high grade prostate cancer and that of the prostate cancer risk calculator can be potentially explained by 1) differences between the cohorts (referred vs unreferred) or 2) the difference in grading, ie grading accuracy due to the difference in biopsy schemes or to temporally related grade shifts. Caution should be used when applying the prostate cancer risk calculator to counsel patients referred for suspicion of prostate cancer since it underestimates the risk of high grade disease.

Techniques and predictive models to improve prostate cancer detection

The use of prostate-specific antigen (PSA) as a screening test remains controversial. There have been several attempts to refine PSA measurements to improve its predictive value. These modifications, including PSA density, PSA kinetics, and the measurement of PSA isoforms, have met with limited success. Therefore, complex statistical and computational models have been created to assess an individual's risk of prostate cancer more accurately. In this review, the authors examined the methods used to modify PSA as well as various predictive models used in prostate cancer detection. They described the mathematical underpinnings of these techniques along with their intrinsic strengths and weaknesses, and they assessed the accuracy of these methods, which have been shown to be better than physicians' judgment at predicting a man's risk of cancer. Without understanding the design and limitations of these methods, they can be applied inappropriately, leading to incorrect conclusions. These models are important components in counseling patients on their risk of prostate cancer and also help in the design of clinical trials by stratifying patients into different risk categories. Thus, it is incumbent on both clinicians and researchers to become familiar with these tools. Cancer 2009;115(13 suppl):3085-99. (c) 2009 American Cancer Society.

Critical appraisal of prostate-specific antigen in prostate cancer screening: 20 years later

Prostate-specific antigen (PSA) is secreted by all types of prostate epithelial cells and has been used for 2 decades as a biologic marker for prostate cancer (PCa). Since the implementation of PSA screening in the United States, the detection of PCa has increased, accompanied by a decrease in the incidence of high-grade cancer and PCa-specific mortality rates. It has been suggested that these decreases have resulted from the enhanced detection of PCa while still curable. These data have been the impetus for early detection programs, which have recommended the initiation of screening as early as 40 years of age. Despite widespread use, PSA screening remains controversial, principally because of the lack of evidence from randomized controlled trials demonstrating a mortality benefit that could outweigh the concerns of the costs of overdiagnosis and overtreatment. Two ongoing, randomized controlled trials are examining whether screening reduces the risk of PCa-related mortality, and the results of these studies are expected soon. Although it has its limitations, PSA still remains the best-studied marker for the detection of PCa.