Evaluating the Prostate Cancer Prevention Trial High Grade Prostate Cancer Risk Calculator in 10 international biopsy cohorts: results from the Prostate Biopsy Collaborative Group

Risk calculators for the detection of prostate cancer: a systematic review

BACKGROUND

Prostate cancer (PCa) (early) detection poses significant challenges, including unnecessary testing and the risk of potential overdiagnosis. The European Association of Urology therefore suggests an individual risk-adapted approach, incorporating risk calculators (RCs) into the PCa detection pathway. In the context of 'The PRostate Cancer Awareness and Initiative for Screening in the European Union' (PRAISE-U) project ( https://uroweb.org/praise-u ), we aim to provide an overview of the currently available clinical RCs applicable in an early PCa detection algorithm.

METHODS

We performed a systematic review to identify RCs predicting detection of clinically significant PCa at biopsy. A search was performed in the databases Medline ALL, Embase, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar for publications between January 2010 and July 2023. We retrieved relevant literature by using the terms "prostate cancer", "screening/diagnosis" and "predictive model". Inclusion criteria included systematic reviews, meta-analyses, and clinical trials. Exclusion criteria applied to studies involving pre-targeted high-risk populations, diagnosed PCa patients, or a sample sizes under 50 men.

RESULTS

We identified 6474 articles, of which 140 were included after screening abstracts and full texts. In total, we identified 96 unique RCs. Among these, 45 underwent external validation, with 28 validated in multiple cohorts. Of the externally validated RCs, 17 are based on clinical factors, 19 incorporate clinical factors along with MRI details, 4 were based on blood biomarkers alone or in combination with clinical factors, and 5 included urinary biomarkers. The median AUC of externally validated RCs ranged from 0.63 to 0.93.

CONCLUSIONS

This systematic review offers an extensive analysis of currently available RCs, their variable utilization, and performance within validation cohorts. RCs have consistently demonstrated their capacity to mitigate the limitations associated with early detection and have been integrated into modern practice and screening trials. Nevertheless, the lack of external validation data raises concerns about numerous RCs, and it is crucial to factor in this omission when evaluating whether a specific RC is applicable to one's target population.

BioPrev-C - development and validation of a contemporary prostate cancer risk calculator

Objectives

To develop a novel biopsy prostate cancer (PCa) prevention calculator (BioPrev-C) using data from a prospective cohort all undergoing mpMRI targeted and transperineal template saturation biopsy.

Materials and methods

Data of all men who underwent prostate biopsy in our academic tertiary care center between 11/2016 and 10/2019 was prospectively collected. We developed a clinical prediction model for the detection of high-grade PCa (Gleason score ≥7) based on a multivariable logistic regression model incorporating age, PSA, prostate volume, digital rectal examination, family history, previous negative biopsy, 5-alpha-reductase inhibitor use and MRI PI-RADS score. BioPrev-C performance was externally validated in another prospective Swiss cohort and compared with two other PCa risk-calculators (SWOP-RC and PBCG-RC).

Results

Of 391 men in the development cohort, 157 (40.2%) were diagnosed with high-grade PCa. Validation of the BioPrev C revealed good discrimination with an area under the curve for high-grade PCa of 0.88 (95% Confidence Interval 0.82-0.93), which was higher compared to the other two risk calculators (0.71 for PBCG and 0.84 for SWOP). The BioPrev-C revealed good calibration in the low-risk range (0 - 0.25) and moderate overestimation in the intermediate risk range (0.25 - 0.75). The PBCG-RC showed good calibration and the SWOP-RC constant underestimation of high-grade PCa over the whole prediction range. Decision curve analyses revealed a clinical net benefit for the BioPrev-C at a clinical meaningful threshold probability range (≥4%), whereas PBCG and SWOP calculators only showed clinical net benefit above a 30% threshold probability.

Conclusion

BiopPrev-C is a novel contemporary risk calculator for the prediction of high-grade PCa. External validation of the BioPrev-C revealed relevant clinical benefit, which was superior compared to other well-known risk calculators. The BioPrev-C has the potential to significantly and safely reduce the number of men who should undergo a prostate biopsy.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

BACKGROUND

Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool.

METHODS

We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance.

RESULTS

High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004).

CONCLUSIONS

We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

Assessment of men's risk thresholds to proceed with prostate biopsy for the early detection of prostate cancer

PURPOSE

To delineate the range of "risk thresholds" for prostate biopsy to determine how improved prostate cancer (CaP) risk prediction tools may impact shared decision-making (SDM).

METHODS

We conducted a cross-sectional survey study involving men 45-75 years old attending a multispecialty urology clinic. Data included demographics, personal and family prostate cancer history, and prostate biopsy history. Respondents were presented with a summary of the details, risks, and benefits of prostate biopsy, then asked to indicate the specific risk threshold (% chance) of high-grade CaP at which they would proceed with prostate biopsy.

RESULTS

Of a total of 103 respondents, 18 men (17%) had a personal history of CaP, and 31 (30%) had undergone prostate biopsy. The median risk threshold to proceed with prostate biopsy was 25% (interquartile range 10-50%). Risk thresholds did not vary by race, education, or employment. Personal history of CaP or prostate biopsy was significantly associated with lower mean risk thresholds (19% vs. 32% [P = 0.02] and 23% vs. 33% [P = 0.04], respectively). In the lowest versus highest risk threshold quartiles, there were significantly higher rates of CaP (36% vs. 1%, P = 0.01) and prior prostate biopsy (46% vs. 17%, P < 0.01).

CONCLUSIONS

Men have a wide range of risk thresholds for high-grade CaP to proceed with prostate biopsy. Men with a prior history of CaP or biopsy reported lower risk thresholds, which may reflect their greater concern for this disease. The extent to which refined risk prediction tools will improve SDM warrants further study.

Engaging African American Men as Citizen Scientists to Validate a Prostate Cancer Biomarker: Work-in-Progress

BACKGROUND

African American men (AAM) are under-represented in prostate cancer (PCa) research despite known disparities. Screening with prostate-specific antigen (PSA) has low specificity for high-grade PCa leading to PCa over diagnosis. The Prostate Health Index (PHI) has higher specificity for lethal PCa but needs validation in AAM. Engaging AAM as citizen scientists (CSs) may improve participation of AAM in PCa research.Results and Lessons Learned: Eight CSs completed all training modules and 139 AAM were recruited. Challenges included equity in research leadership among multiple principal investigators (PIs) and coordinating CSs trainings.

CONCLUSIONS

Engaging AAM CSs can support engaging/recruiting AAM in PCa biomarker validation research. Equity among multiple stakeholders can be challenging, but proves beneficial in engaging AAM in research.

OBJECTIVES

Assess feasibility of mobilizing CSs to recruit AAM as controls for PHI PCa validation biomarker study.

METHODS

We highlight social networks/assets of stakeholders, CSs curriculum development/implementation, and recruitment of healthy controls for PHI validation.

A Contemporary Prostate Biopsy Risk Calculator Based on Multiple Heterogeneous Cohorts

BACKGROUND

Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems.

OBJECTIVE

We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool.

DESIGN, SETTING, AND PARTICIPANTS

We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts.

RESULTS AND LIMITATIONS

Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies.

CONCLUSIONS

The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome.

PATIENT SUMMARY

A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.

Evaluation of Prostate Cancer Risk Calculators for Shared Decision Making Across Diverse Urology Practices in Michigan

OBJECTIVE

To compare the predictive performance of a logistic regression model developed with contemporary data from a diverse group of urology practices to that of the Prostate Cancer Prevention Trial (PCPT) Risk Calculator version 2.0.

MATERIALS AND METHODS

With data from all first-time prostate biopsies performed between January 2012 and March 2015 across the Michigan Urological Surgery Improvement Collaborative (MUSIC), we developed a multinomial logistic regression model to predict the likelihood of finding high-grade cancer (Gleason score ≥7), low-grade cancer (Gleason score ≤6), or no cancer on prostate biopsy. The performance of the MUSIC model was evaluated in out-of-sample data using 10-fold cross-validation. Discrimination and calibration statistics were used to compare the performance of the MUSIC model to that of the PCPT risk calculator in the MUSIC cohort.

RESULTS

Of the 11,809 biopsies included, 4289 (36.3%) revealed high-grade cancer; 2027 (17.2%) revealed low-grade cancer; and the remaining 5493 (46.5%) were negative. In the MUSIC model, prostate-specific antigen level, rectal examination findings, age, race, and family history of prostate cancer were significant predictors of finding high-grade cancer on biopsy. The 2 models, based on similar predictors, had comparable discrimination (multiclass area under the curve = 0.63 for the MUSIC model and 0.62 for the PCPT calculator). Calibration analyses demonstrated that the MUSIC model more accurately predicted observed outcomes, whereas the PCPT risk calculator substantively overestimated the likelihood of finding no cancer while underestimating the risk of high-grade cancer in this population.

CONCLUSION

The PCPT risk calculator may not be a good predictor of individual biopsy outcomes for patients seen in contemporary urology practices.

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.

Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

AIM

To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters.

MATERIAL AND METHODS

Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83).

CONCLUSION

The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Risk stratification of prostate cancer: integrating multiparametric MRI, nomograms and biomarkers

Accurate risk stratification of prostate cancer is achieved with a number of existing tools to ensure the identification of at-risk patients, characterization of disease aggressiveness, prediction of cancer burden and extrapolation of treatment outcomes for appropriate management of the disease. Statistical tables and nomograms using classic clinicopathological variables have long been the standard of care. However, the introduction of multiparametric MRI, along with fusion-guided targeted prostate biopsy and novel biomarkers, are being assimilated into clinical practice. The majority of studies to date present the outcomes of each in isolation. The current review offers a critical and objective assessment regarding the integration of multiparametric MRI and fusion-guided prostate biopsy with novel biomarkers and predictive nomograms in contemporary clinical practice.

External Evaluation of a Novel Prostate Cancer Risk Calculator (ProstateCheck) Based on Data from the Swiss Arm of the ERSPC

PURPOSE

We externally validated a novel prostate cancer risk calculator based on data from the Swiss arm of the ERSPC and assessed whether the risk calculator (ProstateCheck) is superior to the PCPT-RC and SWOP-RC in an independent Swiss cohort.

MATERIALS AND METHODS

Data from all men who underwent prostate biopsy at an academic tertiary care center between 2004 and 2012 were retrospectively analyzed. The probability of having any prostate cancer or high grade prostate cancer (Gleason score 7 or greater) on prostate biopsy was calculated using the ProstateCheck. Risk calculator performance was assessed using calibration and discrimination, and additionally compared with the PCPT-RC and SWOP-RC by decision curve analyses.

RESULTS

Of 1,615 men 401 (25%) were diagnosed with any prostate cancer and 196 (12%) with high grade prostate cancer. Our analyses of the ProstateCheck-RC revealed good calibration in the low risk range (0 to 0.4) and moderate overestimation in the higher risk range (0.4 to 1) for any and high grade prostate cancer. The AUC for the discrimination of any prostate cancer and high grade prostate cancer was 0.69 and 0.72, respectively, which was slightly but significantly higher compared to the PCPT-RC (0.66 and 0.69, respectively) and SWOP-RC (0.64 and 0.70, respectively). Decision analysis, taking into account the harms of transrectal ultrasound measurement of prostate volume, showed little benefit for ProstateCheck-RC, with properties inferior to those of the PCPT-RC and SWOP-RC.

CONCLUSIONS

Our independent external evaluation revealed moderate performance of the ProstateCheck-RC. Its clinical benefit is limited, and inferior to that of the PCPT-RC and SWOP-RC.

Development and external multicenter validation of Chinese Prostate Cancer Consortium prostate cancer risk calculator for initial prostate biopsy

OBJECTIVE

Substantial differences exist in the relationship of prostate cancer (PCa) detection rate and prostate-specific antigen (PSA) level between Western and Asian populations. Classic Western risk calculators, European Randomized Study for Screening of Prostate Cancer Risk Calculator, and Prostate Cancer Prevention Trial Risk Calculator, were shown to be not applicable in Asian populations. We aimed to develop and validate a risk calculator for predicting the probability of PCa and high-grade PCa (defined as Gleason Score sum 7 or higher) at initial prostate biopsy in Chinese men.

MATERIALS AND METHODS

Urology outpatients who underwent initial prostate biopsy according to the inclusion criteria were included. The multivariate logistic regression-based Chinese Prostate Cancer Consortium Risk Calculator (CPCC-RC) was constructed with cases from 2 hospitals in Shanghai. Discriminative ability, calibration and decision curve analysis were externally validated in 3 CPCC member hospitals.

RESULTS

Of the 1,835 patients involved, PCa was identified in 338/924 (36.6%) and 294/911 (32.3%) men in the development and validation cohort, respectively. Multivariate logistic regression analyses showed that 5 predictors (age, logPSA, logPV, free PSA ratio, and digital rectal examination) were associated with PCa (Model 1) or high-grade PCa (Model 2), respectively. The area under the curve of Model 1 and Model 2 was 0.801 (95% CI: 0.771-0.831) and 0.826 (95% CI: 0.796-0.857), respectively. Both models illustrated good calibration and substantial improvement in decision curve analyses than any single predictors at all threshold probabilities. Higher predicting accuracy, better calibration, and greater clinical benefit were achieved by CPCC-RC, compared with European Randomized Study for Screening of Prostate Cancer Risk Calculator and Prostate Cancer Prevention Trial Risk Calculator in predicting PCa.

CONCLUSIONS

CPCC-RC performed well in discrimination and calibration and decision curve analysis in external validation compared with Western risk calculators. CPCC-RC may aid in decision-making of prostate biopsy in Chinese or in other Asian populations with similar genetic and environmental backgrounds.

Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System

Background: Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa). To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa.

Methods: Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies. We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine. Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7).

Results: Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87. The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively. We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7). This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively. Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa. Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected.

Conclusions: This model of a biomarker panel with algorithmic interpretation can be used as a “liquid biopsy” to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions.

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.

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.

Evolution and immediate future of US screening guidelines

Although observational studies and simulation models have shed some interesting light on many of the uncertainties surrounding prostate cancer screening, well-done clinical trials provide the best evidence on screening among the extremes of age, the most appropriate interval to screen, and the best complement of tests to use. Enthusiasm for screening is temporized by the acknowledgment that overdetection leads to frequent overtreatment despite evidence supporting the safety of active surveillance in many men with low-risk disease.

Practice patterns of urologists in managing Korean men aged 40 years or younger with high serum prostate-specific antigen levels

OBJECTIVE

To investigate the practice patterns of urologists in managing Korean men aged 40 years or younger with high serum prostate-specific antigen (PSA).

MATERIALS AND METHODS

Data were collected from general health screenings conducted at 4 university hospitals between 2004 and 2012. Eligibility criteria were Korean men aged≤40 years who were seen by urologists for high PSA (>2.5 ng/mL). After excluding individuals with chronic prostatitis and any infectious symptoms and/or signs, the practice patterns of urologists managing 237 eligible men were analyzed.

RESULTS

The most common practice was observation after antibiotics (40.5%), followed by reassurance (38.4%), prostate biopsy (PBx) after antibiotics (11.8%), PBx after PSA follow-up (7.6%), and immediate PBx (1.7%). Antibiotics were prescribed empirically to 124 patients (52.3%). Of the entire patients, 145 of 237 (61.2%) had at least 1 follow-up PSA, and the follow-up PSA with median interval of 43 days (interquartile range, 26-149) was higher than initial PSA in 66 of 145 (45.5%). Of the 98 patients undergoing follow-up PSA after initial antibiotic treatment, 53 (54.1%) experienced a decline in PSA, whereas 45 (45.9%) experienced a rise in PSA. PBx was performed in 50 of 237 (21.1%), and only a single case (2%) of prostate cancer was diagnosed.

CONCLUSION

In managing men≤40 years with high PSA, the most common practice pattern was observation after antibiotic treatment despite lack of evidences. Furthermore, 1 in 5 urologists performed PBx to rule out cancer. Given the very low prevalence of cancer in this age group, clear guidelines are needed for appropriate management and consistency of care.

Does genotyping of risk-associated single nucleotide polymorphisms improve patient selection for prostate biopsy when combined with a prostate cancer risk calculator?

BACKGROUND

Genome-wide association studies have identified single nucleotide polymorphisms (SNPs) associated with higher risk of prostate cancer (PCa). This study aimed to evaluate whether published SNPs improve the performance of a clinical risk-calculator in predicting prostate biopsy result.

METHODS

Three hundred forty-six patients with a previous prostate biopsy (191 positive, 155 negative) were enrolled. After literature search, nine SNPs were selected for their statistically significant association with increased PCa risk. Allelic odds ratios were computed and a new logistic regression model was built integrating the clinical risk score (i.e., prior biopsy results, PSA level, prostate volume, transrectal ultrasound, and digital rectal examination) and a multilocus genetic risk score (MGRS). Areas under the receiver operating characteristic (ROC) curves (AUC) of the clinical score alone versus the integrated clinic-genetic model were compared. The added value of the MGRS was assessed using the Integrated Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) statistics.

RESULTS

Predictive performance of the integrated clinico-genetic model (AUC = 0.781) was slightly higher than predictive performance of the clinical score alone (AUC = 0.770). The prediction of PCa was significantly improved with an IDI of 0.015 (P-value = 0.035) and a continuous NRI of 0.403 (P-value < 0.001).

CONCLUSIONS

The predictive performance of the clinical model was only slightly improved by adding MGRS questioning the real clinical added value with regards to the cost of genetic testing and performance of current inexpensive clinical risk-calculators.

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.