Validation in a multiple urology practice cohort of the Prostate Cancer Prevention Trial calculator for predicting prostate cancer detection

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

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

The Huashan risk calculators performed better in prediction of prostate cancer in Chinese population: a training study followed by a validation study

The performances of the Prostate Cancer Prevention Trial (PCPT) risk calculator and other risk calculators for prostate cancer (PCa) prediction in Chinese populations were poorly understood. We performed this study to build risk calculators (Huashan risk calculators) based on Chinese population and validated the performance of prostate-specific antigen (PSA), PCPT risk calculator, and Huashan risk calculators in a validation cohort. We built Huashan risk calculators based on data from 1059 men who underwent initial prostate biopsy from January 2006 to December 2010 in a training cohort. Then, we validated the performance of PSA, PCPT risk calculator, and Huashan risk calculators in an observational validation study from January 2011 to December 2014. All necessary clinical information were collected before the biopsy. The results showed that Huashan risk calculators 1 and 2 outperformed the PCPT risk calculator for predicting PCa in both entire training cohort and stratified population (with PSA from 2.0 ng ml⁻¹ to 20.0 ng m). In the validation study, Huashan risk calculator 1 still outperformed the PCPT risk calculator in the entire validation cohort (0.849 vs 0.779 in area under the receiver operating characteristic curve [AUC] and stratified population. A considerable reduction of unnecessary biopsies (approximately 30%) was also observed when the Huashan risk calculators were used. Thus, we believe that the Huashan risk calculators (especially Huashan risk calculator 1) may have added value for predicting PCa in Chinese population. However, these results still needed further evaluation in larger populations.

The prostate cancer prevention trial risk calculator 2.0 performs equally for standard biopsy and MRI/US fusion-guided biopsy

BACKGROUND

The Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC) is a widely used risk-based calculator used to assess a man's risk of prostate cancer (PCa) before biopsy. This risk calculator was created from data of a patient cohort undergoing a 6-core sextant biopsy, and subsequently validated in men undergoing 12-core systematic biopsy (SBx). The accuracy of the PCPTRC has not been studied in patients undergoing magnetic resonance imaging/ultrasound (MRI/US) fusion-guided biopsy (FBx). We sought to assess the performance of the PCPTRC for straitifying PCa risk in a FBx cohort.

METHODS

A review of a prospective cohort undergoing MRI and FBx/SBx was conducted. Data from consecutive FBx/SBx were collected between August 2007 and February 2014, and PCPTRC scores using the PCPTRC2.0R-code were calculated. The risk of positive biopsy and high-grade cancer (Gleason ⩾7) on biopsy was calculated and compared with overall and high-grade cancer detection rates (CDRs). Receiver operating characteristic curves were generated and the areas under the curves (AUCs) were compared using DeLong's test.

RESULTS

Of 595 men included in the study, PCa was detected in 39% (232) by SBx compared with 48% (287) on combined FBx/SBx biopsy. The PCPTRC AUCs for the CDR were similar (P=0.70) for SBx (0.69) and combined biopsy (0.70). For high-grade disease, AUCs for SBx (0.71) and combined biopsy (0.70) were slightly higher, but were not statistically different (P=0.55).

CONCLUSIONS

In an MRI-screened population of men undergoing FBx, PCPTRC continues to represent a practical method of accurately stratifying PCa risk.

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.

Overdiagnosis and overtreatment of prostate cancer

Prostate cancer is a ubiquitous disease, affecting as many as two-thirds of men in their 60s. Through widespread prostate-specific antigen (PSA) testing, increasing rates of prostate biopsy, and increased sampling of the prostate, a larger fraction of low-grade, low-volume tumors have been detected, consistent with tumors often found at autopsy. These tumors have historically been treated in a manner similar to that used for higher-grade tumors but, more recently, it has become evident that with a plan of active surveillance that reserves treatment for only those patients whose tumors show evidence of progression, very high disease-specific survival can be achieved. Unfortunately, the frequency of recommendation of an active surveillance strategy in the United States is low. An alternative strategy to improve prostate cancer detection is through selected biopsy of those men who are at greater risk of harboring high-grade, potentially lethal cancer. This strategy is currently possible through the use of risk assessment tools such as the Prostate Cancer Prevention Trial Risk Calculator (www.prostate.cancer.risk.calculator.com) as well as others. These tools can predict with considerable accuracy a man's risk of low-grade and high-grade cancer, allowing informed decision making for the patient with a goal of detection of high-risk disease. Ultimately, other biomarkers including PCA3, TMPRSS2:ERG, and [-2]proPSA will likely aid in discriminating these two types of cancer before biopsy.

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.

Can urinary PCA3 supplement PSA in the early detection of prostate cancer?

PURPOSE

Given the limited sensitivity and specificity of prostate-specific antigen (PSA), its widespread use as a screening tool has raised concerns for the overdiagnosis of low-risk and the underdiagnosis of high-grade prostate cancer. To improve early-detection biopsy decisions, the National Cancer Institute conducted a prospective validation trial to assess the diagnostic performance of the prostate cancer antigen 3 (PCA3) urinary assay for the detection of prostate cancer among men screened with PSA.

PATIENTS AND METHODS

In all, 859 men (mean age, 62 years) from 11 centers scheduled for a diagnostic prostate biopsy between December 2009 and June 2011 were enrolled. The primary outcomes were to assess whether PCA3 could improve the positive predictive value (PPV) for an initial biopsy (at a score > 60) and the negative predictive value (NPV) for a repeat biopsy (at a score < 20).

RESULTS

For the detection of any cancer, PPV was 80% (95% CI, 72% to 86%) in the initial biopsy group, and NPV was 88% (95% CI, 81% to 93%) in the repeat biopsy group. The addition of PCA3 to individual risk estimation models (which included age, race/ethnicity, prior biopsy, PSA, and digital rectal examination) improved the stratification of cancer and of high-grade cancer.

CONCLUSION

These data independently support the role of PCA3 in reducing the burden of prostate biopsies among men undergoing a repeat prostate biopsy. For biopsy-naive patients, a high PCA3 score (> 60) significantly increases the probability that an initial prostate biopsy will identify cancer.

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.

The Prostate Cancer Prevention Trial risk calculator and the relationship between prostate-specific antigen and biopsy outcome

BACKGROUND

The Prostate Cancer Prevention Trial (PCPT) Risk Calculator is a widely used prediction tool for aiding decisions about biopsy for prostate cancer. This study hypothesized that recently reported differences between predictions from the model and findings from other cohorts were due to how prostate-specific antigen (PSA) was entered into the statistical model, and to the inclusion of protocol end-of-study biopsies for which there was no clinical indication.

METHODS

Data was obtained from the 5088 PCPT participants and was used to construct the PCPT Risk Calculator. The relationship between PSA and the risk of a positive biopsy was modeled by using locally-weighted regression (lowess), an empirical estimate of actual risks observed which does not depend on a statistical model. Risks were estimated with and without the 3514 end-of-study biopsies.

RESULTS

For PSA levels above biopsy thresholds (∼4 ng/mL), the PCPT Risk Calculator greatly overestimated actual empirical risks (eg, 44% versus 26% at 5 ng/mL). The change in risk with increasing PSA was less among for-cause biopsies compared with the end-of-study biopsies (P = .001). Risk of high-grade disease was overestimated at PSA level of ≥ 6 ng/mL.

CONCLUSIONS

The PCPT Risk Calculator overestimates risks for PSAs close to and above typical biopsy thresholds. Separating for-cause biopsies from end-of-study biopsies and using empirical rather than model-based risks reduces overall risk estimates and replicates prior findings that, in men who have been screened with PSA, there is no rapid increase in prostate cancer risk with higher PSA. Revision of the PCPT Risk Calculator should be considered.

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.

The impact of prostate volume, number of biopsy cores and American Urological Association symptom score on the sensitivity of cancer detection using the Prostate Cancer Prevention Trial risk calculator

PURPOSE

We assessed the independent predictive value of prostate volume, number of biopsy cores and AUASS (American Urological Association symptom score) compared to risk factors included in the PCPTRC (Prostate Cancer Prevention Trial risk calculator for prostate cancer) and PCPTHG (Prostate Cancer Prevention Trial risk calculator for high grade cancer [Gleason grade 7 or greater]).

MATERIALS AND METHODS

Of 5,519 PCPT (Prostate Cancer Prevention Trial) participants used to construct the PCPTRC 4,958 with AUASS and prostate specific antigen 10 ng/ml or less were included on logistic regression analysis. Risk algorithms were evaluated in 571 EDRN (Early Detection Research Network) participants using the ROC AUC.

RESULTS

A total of 1,094 participants (22.1%) had prostate cancer, of whom 232 (21.2%) had high grade disease. For prostate cancer prediction higher prostate specific antigen, abnormal digital rectal examination, family history of prostate cancer and number of cores were associated with increased risk, while volume was associated with decreased risk. Excluding prostate volume and number of cores, a history of negative biopsy and increased AUASS were also associated with lower risk. For high grade cancer higher prostate specific antigen, abnormal digital rectal examination, black race and number of cores were associated with increased risk and volume, while AUASS was associated with decreased risk. The AUC of the PCPTRC adjusted for volume and number of cores was 72.7% (using EDRN data), 68.2% when adjusted for AUASS alone and 67.6% PCPTRC. For high grade disease the AUCs were 74.8%, 74.0% and 73.5% (PCPTHG), respectively.

CONCLUSIONS

Adjusted PCPT risk calculators for volume, number of cores and AUASS improve cancer detection.

A simple decision analytic solution to the comparison of two binary diagnostic tests

One of the most basic biostatistical problems is the comparison of two binary diagnostic tests. Commonly, one test will have greater sensitivity, and the other greater specificity. In this case, the choice of the optimal test generally requires a qualitative judgment as to whether gains in sensitivity are offset by losses in specificity. Here, we propose a simple decision analytic solution in which sensitivity and specificity are weighted by an intuitive parameter, the threshold probability of disease at which a patient will opt for treatment. This gives a net benefit that can be used to determine which of two diagnostic tests will give better clinical results at a given threshold probability and whether either is superior to the strategy of assuming that all or no patients have disease. We derive a simple formula for the relative diagnostic value, which is the difference in sensitivities of two tests divided by the difference in the specificities. We show that multiplying relative diagnostic value by the odds at the prevalence gives the odds of the threshold probability below which the more sensitive test is preferable and above which the more specific test should be chosen. The methodology is easily extended to incorporate combinations of tests and the risk or side effects of a test.

PCA3 sensitivity and specificity for prostate cancer detection in patients with abnormal PSA and/or suspicious digital rectal examination. First Latin American experience

INTRODUCTION

Prostate Cancer Gene 3 (PCA3) is a recently described and highly specific urinary marker for prostate cancer (CaP). Its introduction in clinical practice to supplement low specificity of prostate specific antigen (PSA) can improve CaP diagnosis and follow-up. However, before its introduction, it is necessary to validate the method of PCA3 detection in distinct geographic populations.

OBJECTIVES

Our aim was to describe for the first time in Latin America, the application of the PROGENSA PCA3 assay for PCA3 detection in urine in Chilean men and its utility for CaP diagnosis in men with an indication of prostate biopsy.

MATERIALS AND METHODS

Sixty-four Chilean patients (mean age, 64 years) with indication of prostate biopsy because of elevated PSA and/or suspicious digital rectal examination (DRE) were prospectively recruited. PCA3 scores were assessed from urine samples obtained after DRE, before biopsy, and compared with PSA levels and biopsy outcome.

RESULTS

The median PSA value and mean PCA3 score were 5.8 ng/ml and 31.7, respectively. Using a cutoff PCA3 score of 35, the sensitivity and specificity for detecting CaP were 52% and 87%, respectively. The receiver operating characteristic (ROC) curve analysis showed an area under the curve of 0.77 for PCA3 and 0.57 for PSA, for the same group of patients. In patients with previous negative biopsy, PCA3 specificity increased by 2.2%.

CONCLUSIONS

This is the first report in Latin America on the use of PCA3 in diagnosing CaP. Our results are comparable to those reported in other populations in the literature, demonstrating the reproducibility of the test. PCA3 score was highly specific and we specially recommend its use in patients with persistent elevated PSA and prior negative biopsies.

The use of the finasteride-adjusted Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator in a Mexican referral population: a validation study

OBJECTIVES

To perform the first validation study of the finasteride-adjusted Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (finPCPTRC) in a contemporary referral population in Mexico.

METHODS

837 patients referred to the Instituto Nacional de Cancerología, Mexico City, Mexico, between 2005 and 2009 were used to validate the finPCPTRC by examining various measures of discrimination and calibration. Net benefit curve analysis was used to gain insight into the use of the finPCPTRC for clinical decisions.

RESULTS

Prostate cancer (PCa) incidence (72.8%) was high in this Mexican referral cohort and 45.7% of men who were diagnosed with PCa had high-grade lesions (HGPCa, Gleason score >6). 1.3% of the patients were taking finasteride. The finPCPTRC was a superior diagnostic tool compared to prostate-specific antigen alone when discriminating patients with PCa from those without PCa (AUC = 0.784 vs. AUC = 0.687, p < 0.001) and when discriminating patients with HGPCa from those without HGPCa (AUC = 0.768 vs. AUC = 0.739, p < 0.001). The finPCPTRC underestimated the risk of PCa but overestimated the risk of HGPCa (both p < 0.001). Compared with other strategies to opt for biopsy, the net benefit would be larger with utilization of the finPCPTRC for patients accepting higher risks of HGPCa.

CONCLUSIONS

Rates of biopsy-detectable PCa and HGPCa were high and 1.3% of this referral cohort in Mexico was taking finasteride. The risks of PCa or HGPCa calculated by the finPCPTRC were not well calibrated for this referral Mexican population and new clinical diagnostic tools are needed.

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.

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.

Identification of functionally active, low frequency copy number variants at 15q21.3 and 12q21.31 associated with prostate cancer risk

Copy number variants (CNVs) are a recently recognized class of human germ line polymorphisms and are associated with a variety of human diseases, including cancer. Because of the strong genetic influence on prostate cancer, we sought to identify functionally active CNVs associated with susceptibility of this cancer type. We queried low-frequency biallelic CNVs from 1,903 men of Caucasian origin enrolled in the Tyrol Prostate Specific Antigen Screening Cohort and discovered two CNVs strongly associated with prostate cancer risk. The first risk locus (P = 7.7 × 10(-4), odds ratio = 2.78) maps to 15q21.3 and overlaps a noncoding enhancer element that contains multiple activator protein 1 (AP-1) transcription factor binding sites. Chromosome conformation capture (Hi-C) data suggested direct cis-interactions with distant genes. The second risk locus (P = 2.6 × 10(-3), odds ratio = 4.8) maps to the α-1,3-mannosyl-glycoprotein 4-β-N-acetylglucosaminyltransferase C (MGAT4C) gene on 12q21.31. In vitro cell-line assays found this gene to significantly modulate cell proliferation and migration in both benign and cancer prostate cells. Furthermore, MGAT4C was significantly overexpressed in metastatic versus localized prostate cancer. These two risk associations were replicated in an independent PSA-screened cohort of 800 men (15q21.3, combined P = 0.006; 12q21.31, combined P = 0.026). These findings establish noncoding and coding germ line CNVs as significant risk factors for prostate cancer susceptibility and implicate their role in disease development and progression.

Averaged differential expression for the discovery of biomarkers in the blood of patients with prostate cancer

BACKGROUND

The identification of a blood-based diagnostic marker is a goal in many areas of medicine, including the early diagnosis of prostate cancer. We describe the use of averaged differential display as an efficient mechanism for biomarker discovery in whole blood RNA. The process of averaging reduces the problem of clinical heterogeneity while simultaneously minimizing sample handling.

METHODOLOGY/PRINCIPAL FINDINGS

RNA was isolated from the blood of prostate cancer patients and healthy controls. Samples were pooled and subjected to the averaged differential display process. Transcripts present at different levels between patients and controls were purified and sequenced for identification. Transcript levels in the blood of prostate cancer patients and controls were verified by quantitative RT-PCR. Means were compared using a t-test and a receiver-operating curve was generated. The Ring finger protein 19A (RNF19A) transcript was identified as having higher levels in prostate cancer patients compared to healthy men through the averaged differential display process. Quantitative RT-PCR analysis confirmed a more than 2-fold higher level of RNF19A mRNA levels in the blood of patients with prostate cancer than in healthy controls (p = 0.0066). The accuracy of distinguishing cancer patients from healthy men using RNF19A mRNA levels in blood as determined by the area under the receiving operator curve was 0.727.

CONCLUSIONS/SIGNIFICANCE

Averaged differential display offers a simplified approach for the comprehensive screening of body fluids, such as blood, to identify biomarkers in patients with prostate cancer. Furthermore, this proof-of-concept study warrants further analysis of RNF19A as a clinically relevant biomarker for prostate cancer detection.

Prostate cancer risk prediction in a urology clinic in Mexico

OBJECTIVES

To evaluate factors affecting the risk of prostate cancer (CaP) and high-grade disease (HGCaP, Gleason score ≥ 7) in a Mexican referral population, with comparison to the Prostate Cancer Prevention Trial Prostate Cancer Risk Calculator (PCPTRC).

METHODS AND MATERIALS

From a retrospective study of 826 patients who underwent prostate biopsy between January 2005 and December 2009 at the Instituto Nacional de Cancerología, Mexico, logistic regression was used to assess the effects of age, prostate-specific antigen (PSA), digital rectal exam (DRE), first-degree family history of CaP, and history of a prior prostate biopsy on CaP and HGCaP, separately. Internal discrimination, goodness-of-fit, and clinical utility of the resulting models were assessed with comparison to the PCPTRC.

RESULTS

Rates of both CaP (73.2%) and HGCaP (33.3%) were high among referral patients in this Mexican urology clinic. The PCPTRC generally underestimated the risk of CaP but overestimated the risk of HGCaP. Four factors influencing CaP on biopsy were logPSA, DRE, family history and a prior biopsy history (all P < 0.001). The internal AUC of the logistic model was 0.823 compared with 0.785 of the PCPTRC for CaP (P < 0.001). The same 4 factors were significantly associated with HGCaP as well and the AUC was 0.779 compared with 0.766 of the PCPTRC for HGCaP (P = 0.13).

CONCLUSIONS

Lack of screening programs or regular urologic checkups in Mexico imply that men typically first reach specialized clinics with a high cancer risk. This renders diagnostic tools developed on comparatively healthy populations, such as the PCPTRC, of lesser utility. Continued efforts are needed to develop and externally validate new clinical diagnostic tools specific to high-risk referral populations incorporating new biomarkers and more clinical characteristics.

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.

Prediction models in cancer care

Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem; many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings, can incorporate novel predictors such as genomic data, and can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! Online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information, there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision-making. Key issues will be integration of models into the electronic health record and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.

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.

Deciding whom to biopsy

Biopsy results from the Prostate Cancer Prevention Trial (PCPT) showed that prostate cancer exists at all PSA levels and that a significant number of men with "normal" PSA levels have high grade cancer. These findings and the low specificity of total PSA in discriminating cancer from benign disease have added to the debate about how best to use PSA in selecting men for prostate biopsy. Lower PSA thresholds for consideration of biopsy, particularly in younger men, are advocated by some. PSA velocity measurements may assist in the identification of men most likely to harbor cancer, and lower PSA velocity thresholds may be more appropriate in younger men. A more individualized approach using a predictive model developed from PCPT biopsy results is promoted by others. While able to incorporate risk variables other than PSA, including new markers, this risk calculator does not include PSA velocity since this variable was not found to have independent predictive value in this model. This article will present differing viewpoints on selecting men for prostate biopsy, one advocating the use of a PSA cut-off or PSA velocity measure (Dr. Catalona) and the other arguing for the routine use of established risk nomograms (Dr. Klein).

Prospective evaluation of operating characteristics of prostate cancer detection biomarkers

PURPOSE

We assessed the independent predictive values of the serum markers free prostate specific antigen, proenzyme prostate specific antigen, neuroendocrine marker and Dickkopf-1 compared to serum prostate specific antigen and other standard risk factors for early prostate cancer detection.

MATERIALS AND METHODS

From the prospectively collected SABOR cohort 250 prostate cancer cases, and 250 mean age matched and proportion of African-American race/ethnicity matched controls were selected who had a prior available prostate specific antigen and digital rectal examination. Serum samples were obtained, and free prostate specific antigen, [-2]proenzyme prostate specific antigen, Dickkopf-1 and neuroendocrine marker were measured. AUC, sensitivities and specificities were calculated, and multivariable logistic regression was used to assess the independent predictive value compared to prostate specific antigen, digital rectal examination, family history, prior biopsy history, race/ethnicity and age.

RESULTS

The AUCs (95% CI) were 0.76 (0.71, 0.8) for free prostate specific antigen, 0.72 (0.67, 0.76) for [-2]proenzyme prostate specific antigen, 0.76 (0.72, 0.8) for %free prostate specific antigen, 0.61 (0.56, 0.66) for %[-2]proenzyme prostate specific antigen, 0.73 (0.68, 0.77) for prostate health index, 0.53 (0.48, 0.58) for Dickkopf-1 and 0.53 (0.48, 0.59) for neuroendocrine marker. In the 2 to 10 ng/ml prostate specific antigen range the AUCs (95% CI) were 0.58 (0.49, 0.67) for free prostate specific antigen, 0.53 (0.44, 0.62) for [-2]proenzyme prostate specific antigen, 0.67 (0.59, 0.75) for %free prostate specific antigen, 0.57 (0.49, 0.65) for %[-2]proenzyme prostate specific antigen and 0.59 (0.51, 0.67) for phi. Only %free prostate specific antigen retained independent predictive value compared to the traditional risk factors.

CONCLUSIONS

Free prostate specific antigen retained independent diagnostic usefulness for prostate cancers detected through prostate specific antigen and digital rectal examination screening. Prostate specific antigen isoforms are highly correlated with prostate specific antigen. Future research is needed to identify new markers associated with prostate cancer through different mechanisms.

Prostate cancer screening 2010: updated recommendations from the American Cancer Society

In 2009, the American Cancer Society (ACS) initiated a series of systematic evidence reviews to update recommendations for early prostate cancer detection. The evidence reviews focused on studies of screening, the performance of screening tests, harms associated with testing and therapy for localized prostate cancer, and shared and informed decision making in prostate cancer screening. Based on this evidence, the ACS recommends that asymptomatic men who have at least a 10-year life expectancy have an opportunity to make an informed decision with their health care provider about screening for prostate cancer after receiving information about the uncertainties, risks, and potential benefits associated with prostate cancer screening. Prostate cancer screening should not occur without an informed decision-making process. Men at average risk should receive this information beginning at age 50. Men in higher-risk groups should receive this information before age 50. Men should either receive this information directly from their health care providers or be referred to reliable and culturally appropriate sources. Patient decision aids are helpful in preparing men to make a decision whether to be tested, and the use of such aids is encouraged.

Treatment of localized prostate cancer: when is active surveillance appropriate?

Testing for prostate-specific antigen (PSA) has caused a dramatic increase in the incidence of prostate cancer during the past two decades. Many cancers identified by repeated PSA testing are small volume, low-grade lesions that pose little threat of progression over 15-20 years. Data from a recently reported randomized trial indicate that as many as 48 men must undergo treatment to prevent one prostate cancer-related death. Unfortunately, no test is currently available that can identify those men who have clinically significant disease. Men least likely to experience disease progression are men who harbor tumors with a Gleason score of 6 involving 2 needle cores or less; these men may want to consider active surveillance as their initial treatment option. Researchers have followed over 2,500 men on active surveillance protocols (over 200 men have been followed for >10 years). To date, prostate cancer-specific survival is over 99%. About 25% of men enrolled in active surveillance programs have abandoned this approach because of concerns about disease progression. For men harboring tumors with a Gleason score >7, data from two recently reported Swedish trials suggest lower prostate cancer-related mortality for those men receiving either surgery or radiation.