Prostate Cancer Prevention Trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer

MultiParametric Magnetic Resonance Imaging-Based Nomogram for Predicting Prostate Cancer and Clinically Significant Prostate Cancer in Men Undergoing Repeat Prostate Biopsy

OBJECTIVE

To develop and internally validate nomograms based on multiparametric magnetic resonance imaging (mpMRI) to predict prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in patients with a previous negative prostate biopsy.

MATERIALS AND METHODS

The clinicopathological parameters of 231 patients who underwent a repeat systematic prostate biopsy and mpMRI were reviewed. Based on Prostate Imaging and Reporting Data System, the mpMRI results were assigned into three groups: Groups "negative," "suspicious," and "positive." Two clinical nomograms for predicting the probabilities of PCa and csPCa were constructed. The performances of nomograms were assessed using area under the receiver operating characteristic curves (AUCs), calibrations, and decision curve analysis.

RESULTS

The median PSA was 15.03 ng/ml and abnormal DRE was presented in 14.3% of patients in the entire cohort. PCa was detected in 75 patients (32.5%), and 59 (25.5%) were diagnosed with csPCa. In multivariate analysis, age, prostate-specific antigen (PSA), prostate volume (PV), digital rectal examination (DRE), and mpMRI finding were significantly independent predictors for PCa and csPCa (all p < 0.01). Of those patients diagnosed with PCa or csPCa, 20/75 (26.7%) and 18/59 (30.5%) had abnormal DRE finding, respectively. Two mpMRI-based nomograms with super predictive accuracy were constructed (AUCs = 0.878 and 0.927, p < 0.001), and both exhibited excellent calibration. Decision curve analysis also demonstrated a high net benefit across a wide range of probability thresholds.

CONCLUSION

mpMRI combined with age, PSA, PV, and DRE can help predict the probability of PCa and csPCa in patients who underwent a repeat systematic prostate biopsy after a previous negative biopsy. The two nomograms may aid the decision-making process in men with prior benign histology before the performance of repeat prostate biopsy.

Prostate Specific Antigen Density as a Predictor of Clinically Significant Prostate Cancer When the Prostate Specific Antigen is in the Diagnostic Gray Zone: Defining the Optimum Cutoff Point Stratified by Race and Body Mass Index

PURPOSE

We assessed the predictive value of prostate specific antigen density to detect clinically significant prostate cancer, defined as prostate cancer grade group 2 or greater, in a series of men undergoing prostate biopsy with prostate specific antigen 4 to 10 ng/ml. We sought to define an optimum cutoff point for prostate specific antigen density and assess how race and body mass index affects prostate specific antigen density performance.

MATERIALS AND METHODS

We analyzed data on 2,162 men, of whom 56% were African American, with serum prostate specific antigen 4 to 10 ng/ml who underwent prostate biopsy. We compared the AUC between prostate specific antigen and prostate specific antigen density to predict clinically significant and any prostate cancer vs no cancer. We calculated the negative predictive value of prostate specific antigen density cutoff points ranging from 0.05 to 0.15 by every 0.01 step. We a priori defined the optimal cutoff point of prostate specific antigen density as a negative predictive value of 95% and tested whether the cutoff was sensitive to body mass index and race by comparing the negative predictive value across strata.

RESULTS

Median prostate specific antigen was 5.6 ng/ml (IQR 4.8-7) and median prostate specific antigen density was 0.15 ng/ml/cc (IQR 0.1-0.22). Prostate specific antigen density improved the performance of prostate specific antigen to detect significant cancer (AUC 0.58 to 0.68) and any cancer (AUC 0.55 to 0.69, each p <0.001). We identified a prostate specific antigen density cutoff point of less than 0.08 ng/ml/cc with a 96% negative predictive value for grade group 2 or greater. This was largely unchanged among different races and body mass indexes.

CONCLUSIONS

Regardless of race or body mass index men with prostate specific antigen density less than 0.08 were unlikely to harbor grade group 2 or greater disease when prostate specific antigen was 4 to 10 ng/ml. If validated, prostate specific antigen density is a simple inexpensive and available tool that can be used to identify men who can likely forego prostate biopsies, thus reducing the over detection and morbidity of unnecessary biopsies.

Cancer screening in the United States, 2018: A review of current American Cancer Society guidelines and current issues in cancer screening

Each year, the American Cancer Society publishes a summary of its guidelines for early cancer detection, data and trends in cancer screening rates from the National Health Interview Survey, and select issues related to cancer screening. In this 2018 update, we also summarize the new American Cancer Society colorectal cancer screening guideline and include a clarification in the language of the 2013 lung cancer screening guideline. CA Cancer J Clin 2018;68:297-316. © 2018 American Cancer Society.

Development and internal validation of PI-RADs v2-based model for clinically significant prostate cancer

BACKGROUND

Our objective is to build a model based on Prostate Imaging Reporting and Data System version 2 (PI-RADs v2) and assess its accuracy by internal validation.

METHODS

Patients who took prostate biopsy from 2014 to 2015 were retrospectively collected to compose training cohort according to the inclusion criteria and patients in 2016 composing validation cohort. Diagnostic performance was evaluated by analyzing the area under the curve (AUC), calibration curves, and decision curves.

RESULTS

Of the 441 patients involved, the clinically significant prostate cancer (csPCa) detection rate were 40.6% (114/281) and 43.8% (70/160) in the training and validation cohort, respectively. Meanwhile, PCa detection rate were 50.2% (141/281) and 53.8% (86/160). Age, prostate-specific antigen density (PSAD)*10 and PI-RADs v2 score composed the model for PCa (model 1) and csPCa (model 2). The area under the curve of models 1 and 2 was 0.870 (95% CI 0.827-0.912) and 0.753 (95% CI 0.717-0.828) in the training cohort, while 0.845 (95% CI 0.786-0.904) and 0.834 (95% CI 0.787-0.882) in the validation cohort. Both models illustrated good calibration, and decision curve analyses showed good performance in predicting PCa or csPCa when the threshold was 0.35 or above.

CONCLUSIONS

The model based on age, PSAD*10 and PI-RADs v2 score showed internally validated high predictive value for both PCa and csPCa. It could be used to improve the diagnostic performance of suspicious PCa.

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.

Beyond the PSA test: How to better stratify a patient's risk of prostate cancer

The primary method of screening for and detecting prostate cancer is the prostate-specific antigen (PSA) test. Although this test is very prostate-specific, it is not cancer-specific; conditions other than prostate cancer can cause an elevated PSA. Many efforts have been made to discover more specific tests and methods beyond the PSA. This article describes several literature-supported tests and methods to better stratify a man's risk of having prostate cancer.

Testosterone Therapy in Men With Hypogonadism: An Endocrine Society Clinical Practice Guideline

OBJECTIVE

To update the "Testosterone Therapy in Men With Androgen Deficiency Syndromes" guideline published in 2010.

PARTICIPANTS

The participants include an Endocrine Society-appointed task force of 10 medical content experts and a clinical practice guideline methodologist.

EVIDENCE

This evidence-based guideline was developed using the Grading of Recommendations, Assessment, Development, and Evaluation approach to describe the strength of recommendations and the quality of evidence. The task force commissioned two systematic reviews and used the best available evidence from other published systematic reviews and individual studies.

CONSENSUS PROCESS

One group meeting, several conference calls, and e-mail communications facilitated consensus development. Endocrine Society committees and members and the cosponsoring organization were invited to review and comment on preliminary drafts of the guideline.

CONCLUSIONS

We recommend making a diagnosis of hypogonadism only in men with symptoms and signs consistent with testosterone (T) deficiency and unequivocally and consistently low serum T concentrations. We recommend measuring fasting morning total T concentrations using an accurate and reliable assay as the initial diagnostic test. We recommend confirming the diagnosis by repeating the measurement of morning fasting total T concentrations. In men whose total T is near the lower limit of normal or who have a condition that alters sex hormone-binding globulin, we recommend obtaining a free T concentration using either equilibrium dialysis or estimating it using an accurate formula. In men determined to have androgen deficiency, we recommend additional diagnostic evaluation to ascertain the cause of androgen deficiency. We recommend T therapy for men with symptomatic T deficiency to induce and maintain secondary sex characteristics and correct symptoms of hypogonadism after discussing the potential benefits and risks of therapy and of monitoring therapy and involving the patient in decision making. We recommend against starting T therapy in patients who are planning fertility in the near term or have any of the following conditions: breast or prostate cancer, a palpable prostate nodule or induration, prostate-specific antigen level > 4 ng/mL, prostate-specific antigen > 3 ng/mL in men at increased risk of prostate cancer (e.g., African Americans and men with a first-degree relative with diagnosed prostate cancer) without further urological evaluation, elevated hematocrit, untreated severe obstructive sleep apnea, severe lower urinary tract symptoms, uncontrolled heart failure, myocardial infarction or stroke within the last 6 months, or thrombophilia. We suggest that when clinicians institute T therapy, they aim at achieving T concentrations in the mid-normal range during treatment with any of the approved formulations, taking into consideration patient preference, pharmacokinetics, formulation-specific adverse effects, treatment burden, and cost. Clinicians should monitor men receiving T therapy using a standardized plan that includes: evaluating symptoms, adverse effects, and compliance; measuring serum T and hematocrit concentrations; and evaluating prostate cancer risk during the first year after initiating T therapy.

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

Background

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

Methods

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

Results

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

Conclusions

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

Head-to-head comparison of prostate cancer risk calculators predicting biopsy outcome

Background

Multivariable risk calculators (RCs) predicting prostate cancer (PCa) aim to reduce unnecessary workup (e.g., MRI and biopsy) by selectively identifying those men at risk for PCa or clinically significant PCa (csPCa) (Gleason ≥7). The lack of an adequate comparison makes choosing between RCs difficult for patients, clinicians and guideline developers. We aim to perform a head-to-head comparison of seven well known RCs predicting biopsy outcome.

Methods

Our study comprised 7,119 men from ten independent contemporary cohorts in Europe and Australia, who underwent prostate biopsy between 2007 and 2015. We evaluated the performance of the ERSPC RPCRC, Finne, Chun, ProstataClass, Karakiewicz, Sunnybrook, and PCPT 2.0 (HG) RCs in predicting the presence of any PCa and csPCa. Performance was assessed by discrimination, calibration and net benefit analyses.

Results

A total of 3,458 (48%) PCa were detected; 1,784 (25%) men had csPCa. No particular RC stood out predicting any PCa: pooled area under the ROC-curve (AUC) ranged between 0.64 and 0.72. The ERSPC RPCRC had the highest pooled AUC 0.77 (95% CI: 0.73–0.80) when predicting csPCa. Decision curve analysis (DCA) showed limited net benefit in the detection of csPCa, but that can be improved by a simple calibration step. The main limitation is the retrospective design of the study.

Conclusions

No particular RC stands out when predicting biopsy outcome on the presence of any PCa. The ERSPC RPCRC is superior in identifying those men at risk for csPCa. Net benefit analyses show that a multivariate approach before further workup is advisable.

Prostate Cancer Markers

Diagnostic biomarkers derived from blood, urine, or prostate tissue provide additional information beyond clinical calculators to determine the risk of detecting high-grade prostate cancer. Once diagnosed, multiple markers leverage prostate cancer biopsy tissue to prognosticate clinical outcomes, including adverse pathology at radical prostatectomy, disease recurrence, and prostate cancer mortality; however the clinical utility of some outcomes to patient decision making is unclear. Markers using tissue from radical prostatectomy specimens provide additional information about the risk of biochemical recurrence, development of metastatic disease, and subsequent mortality beyond existing multivariable clinical calculators (the use of a marker to simply sub-stratify risk groups such as the NCCN groups is of minimal value). No biomarkers currently available for prostate cancer have been prospectively validated to be predict an improved clinical outcome for a specific therapy based on the test result; however, further research and development of these tests may produce a truly predictive biomarker for prostate cancer treatment.

The molecular biology of prostate cancer: current understanding and clinical implications

BACKGROUND

With continuous progress over the past few decades in understanding diagnosis, treatment, and genetics, much has been learned about the prostate cancer-diagnosed genome.

METHODS

A comprehensive MEDLINE® and Google scholar literature search was conducted using keyword variations relating to the genetics of prostate cancer such as chromosomal alterations, androgen receptor, castration-resistant, inheritance, polymorphisms, oncogenes, metastasis, biomarkers, and immunotherapy.

RESULTS

Traditionally, androgen receptors (AR) have been the focus of research. Recently, identification of recurrent chromosomal alterations that lead to either multiplication of regions (gain-of-function) or deletion of regions (loss-of-function) has opened the door to greater genetic accessibility. These chromosomal aberrations lead to variation in copy number and gene expression. Some of these chromosomal alterations are inherited, while others undergo somatic mutations during disease progression. Inherited gene mutations that make one susceptible to prostate cancer have been identified with familial-linked studies. Somatic genes that progress tumorigenesis have also been identified. Research on the molecular biology of prostate cancer has characterized these genes into tumor suppressor genes or oncogenes. Additionally, genome-wide assay studies have identified many high-risk single-nucleotide polymorphisms recurrent throughout the prostate cancer-diagnosed genome. Castration-resistant prostate cancer is the most aggressive form of prostate cancer, and its research has elucidated many types of mutations associated with AR itself, including enhanced expression and amplification, point mutations, and alternative splicing. Understanding the molecular biology of prostate cancer has permitted more accurate identification using advanced biomarkers and therapy for aggressive forms using immunotherapy.

CONCLUSIONS

An age-related disease, prostate cancer commands profound attention. With increasing life expectancy and the continuous pursuit of it, prostate cancer is a powerful obstacle best defeated using targeted therapies specifically designed for the unique molecular profile of the malignancy.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Association Between Lead Time and Prostate Cancer Grade: Evidence of Grade Progression from Long-term Follow-up of Large Population-based Cohorts Not Subject to Prostate-specific Antigen Screening

BACKGROUND

Lead time (LT) is of key importance in early detection of cancer, but cannot be directly measured. We have previously provided LT estimates for prostate cancer (PCa) using archived blood samples from cohorts followed for many years without screening.

OBJECTIVE

To determine the association between LT and PCa grade at diagnosis to provide an insight into whether grade progresses or is stable over time.

DESIGN, SETTING, AND PARTICIPANTS

The setting was three long-term epidemiologic studies in Sweden including men not subject to prostate-specific antigen (PSA) screening. The cohort included 1041 men with PSA of 3-10 ng/ml at blood draw and subsequently diagnosed with PCa with grade data available.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Multivariable logistic regression was used to predict high-grade (Gleason grade group ≥2 or World Health Organization grade 3) versus low-grade PCa at diagnosis in terms of LT, defined as the time between the date of elevated PSA and the date of PCa diagnosis with adjustment for cohort and age.

RESULTS AND LIMITATIONS

The probability that PCa would be high grade at diagnosis increased with LT. Among all men combined, the risk of high-grade disease increased with LT (odds ratio 1.13, 95% confidence interval [CI] 1.10-1.16; p<0.0001), with no evidence of differences in effect by age group or cohort. Higher PSA predicted shorter LT by 0.46 yr (95% CI 0.28-0.64; p<0.0001) per 1 ng/ml increase in PSA. However, there was no interaction between PSA and grade, suggesting that the longer LT for high-grade tumors is not simply related to age. Limitations include the assumption that men with elevated PSA and subsequently diagnosed with PCa would have had biopsy-detectable PCa at the time of PSA elevation.

CONCLUSIONS

Our data support grade progression, whereby following a prostate over time would reveal transitions from benign to low-grade and then high-grade PCa.

PATIENT SUMMARY

Men with a longer lead time between elevated prostate-specific antigen and subsequent prostate cancer diagnosis were more likely to have high-grade cancers at diagnosis.

Prevention of Prostate Cancer Morbidity and Mortality: Primary Prevention and Early Detection

More than any other cancer, prostate cancer screening with the prostate-specific antigen (PSA) tests increases the risk a man will have to face a diagnosis of prostate cancer. The best evidence from screening trials suggests a small but finite benefit from prostate cancer screening in terms of prostate cancer-specific mortality, about 1 fewer prostate cancer death per 1000 men screened over 10 years. The more serious harms of prostate cancer screening, such as erectile dysfunction and incontinence, result from cancer treatment with surgery or radiation, particularly for men whose PSA-detected cancers were never destined to cause morbidity or mortality.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Role of mpMRI of the prostate in screening for prostate cancer

Prostate cancer screening offers the opportunity to significantly reduce morbidity and mortality from this disease. Currently, serum prostate-specific antigen (PSA) testing is the most widely used screening modality. However, PSA testing continues to have low positive and negative predictive value leading to unnecessary invasive prostate biopsy while missing patients with aggressive forms of the disease. Magnetic resonance imaging (MRI) has been gaining an increasingly large role in the management of patients with early stage prostate cancer including diagnosis in patients with abnormal PSA levels, monitoring of patients on active surveillance, and staging prior to definitive interventions. MRI-based prostate cancer risk assessment has been shown to better distinguish between clinically-significant and insignificant tumors than PSA testing alone or from nomograms. Preliminary data indicate that, among unselected patients, MRI outperforms PSA in the identification of patients with clinically significant prostate cancer. Further work is needed to examine the role of mpMRI in prostate cancer screening.

Variation in Use of Prostate Biopsy Following Changes in Prostate Cancer Screening Guidelines

PURPOSE

Prostate biopsy rates have paralleled decreasing prostate specific antigen screening rates since 2012. We hypothesized that biopsy rates and the change in rates since 2012 would vary considerably across hospital referral regions.

MATERIALS AND METHODS

Using Medicare data from 2012 through 2014 we identified prostate biopsies performed by physicians who performed 11 or more biopsies annually. We calculated annual biopsy rates and changes in rates from 2012 to 2014 across 306 hospital referral regions. We performed multivariable regression adjusting for factors associated with annual biopsy rates (eg percent of patients older than 75 who were screened with prostate specific antigen and percent of the population that was African American). We also estimated adjusted prostate biopsy rates and changes with time across regions.

RESULTS

We identified 395,993 biopsies. The overall rates decreased from 11.68 biopsies per 1,000 men in 2012 to 10.23 per 1,000 in 2014 (-12.4%, p = 0.11). Biopsy rates were higher in regions in which a greater percentage of the population was African American (β = 0.810, 95% CI 0.235-1.384, p = 0.006), ambulatory surgical centers were available where biopsy could be performed (β = 0.892, 95% CI 0.108-1.676, p = 0.026) and prostate specific antigen testing occurred more frequently (β = 2.462, 95% CI 1.153-3.771, p <0.001). There was marked geographic variation in the adjusted average biopsy rate (median adjusted rate 9.08 biopsies per 1,000 men, IQR 7.65-10.76) and in the change in biopsy rates with time (median adjusted rate change -1.49 biopsies per 1,000 men, IQR -1.94--1.22 per 1,000).

CONCLUSIONS

Since 2012 there has been considerable geographic variation in the performance of prostate biopsies as well as changes with time after prostate specific antigen recommendations changed. Characterizing the role of unmeasured patient and physician level factors is crucial to optimize the use and minimize the harms of prostate biopsy.

Re-examining Prostate-specific Antigen (PSA) Density: Defining the Optimal PSA Range and Patients for Using PSA Density to Predict Prostate Cancer Using Extended Template Biopsy

OBJECTIVE

To compare the predictive accuracy of prostate-specific antigen (PSA) density vs PSA across different PSA ranges and by prior biopsy status in a prospective cohort undergoing prostate biopsy.

MATERIALS AND METHODS

Men from a prospective trial underwent an extended template biopsy to evaluate for prostate cancer at 26 sites throughout the United States. The area under the receiver operating curve assessed the predictive accuracy of PSA density vs PSA across 3 PSA ranges (<4 ng/mL, 4-10 ng/mL, >10 ng/mL). We also investigated the effect of varying the PSA density cutoffs on the detection of cancer and assessed the performance of PSA density vs PSA in men with or without a prior negative biopsy.

RESULTS

Among 1290 patients, 585 (45%) and 284 (22%) men had prostate cancer and significant prostate cancer, respectively. PSA density performed better than PSA in detecting any prostate cancer within a PSA of 4-10 ng/mL (area under the receiver operating characteristic curve [AUC]: 0.70 vs 0.53, P < .0001) and within a PSA >10 mg/mL (AUC: 0.84 vs 0.65, P < .0001). PSA density was significantly more predictive than PSA in detecting any prostate cancer in men without (AUC: 0.73 vs 0.67, P < .0001) and with (AUC: 0.69 vs 0.55, P < .0001) a previous biopsy; however, the incremental difference in AUC was higher among men with a previous negative biopsy. Similar inferences were seen for significant cancer across all analyses.

CONCLUSION

As PSA increases, PSA density becomes a better marker for predicting prostate cancer compared with PSA alone. Additionally, PSA density performed better than PSA in men with a prior negative biopsy.

Prostate Cancer Screening

OBJECTIVE

To review the current state of prostate cancer screening and future directions.

DATA SOURCES

Nursing, medical and scientific literature related to prostate cancer screening, and national and international professional recommendations.

CONCLUSION

Prostate cancer screening has been a topic of robust discussion for a number of years. Research continues to examine novel options for prostate cancer screening to either replace or compliment the prostate specific antigen test, but require additional validation before they will be widely accepted into clinical practice.

IMPLICATIONS FOR NURSING PRACTICE

As new data emerges and professional organizations update their recommendations, it is important for oncology nurses to keep abreast of the latest developments to educate patients.

Diffusion-weighted endorectal MR imaging at 3T for prostate cancer: correlation with tumor cell density and percentage Gleason pattern on whole mount pathology

OBJECTIVE

To determine if tumor cell density and percentage of Gleason pattern within an outlined volumetric tumor region of interest (TROI) on whole-mount pathology (WMP) correlate with apparent diffusion coefficient (ADC) values on corresponding TROIs outlined on pre-operative MRI.

METHODS

Men with biopsy-proven prostate adenocarcinoma undergoing multiparametric MRI (mpMRI) prior to prostatectomy were consented to this prospective study. WMP and mpMRI images were viewed using 3D Slicer and each TROI from WMP was contoured on the high b-value ADC maps (b0, 1400). For each TROI outlined on WMP, TCD (tumor cell density) and the percentage of Gleason pattern 3, 4, and 5 were recorded. The ADC_mean, ADC_{10th percentile}, ADC_{90th percentile}, and ADC_ratio were also calculated in each case from the ADC maps using 3D Slicer.

RESULTS

Nineteen patients with 21 tumors were included in this study. ADC_mean values for TROIs were 944.8 ± 327.4 vs. 1329.9 ± 201.6 mm²/s for adjacent non-neoplastic prostate tissue (p < 0.001). ADC_mean, ADC_{10th percentile}, and ADC_ratio values for higher grade tumors were lower than those of lower grade tumors (mean 809.71 and 1176.34 mm²/s, p = 0.014; 10th percentile 613.83 and 1018.14 mm²/s, p = 0.009; ratio 0.60 and 0.94, p = 0.005). TCD and ADC_mean (ρ = -0.61, p = 0.005) and TCD and ADC_{10th percentile} (ρ = -0.56, p = 0.01) were negatively correlated. No correlation was observed between percentage of Gleason pattern and ADC values.

CONCLUSION

DWI MRI can characterize focal prostate cancer using ADC_ratio, ADC_{10th percentile}, and ADC_mean, which correlate with pathological tumor cell density.

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.

Development and External Validation of the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer: Comparison with Two Western Risk Calculators in an Asian Cohort

PURPOSE

We developed the Korean Prostate Cancer Risk Calculator for High-Grade Prostate Cancer (KPCRC-HG) that predicts the probability of prostate cancer (PC) of Gleason score 7 or higher at the initial prostate biopsy in a Korean cohort (http://acl.snu.ac.kr/PCRC/RISC/). In addition, KPCRC-HG was validated and compared with internet-based Western risk calculators in a validation cohort.

MATERIALS AND METHODS

Using a logistic regression model, KPCRC-HG was developed based on the data from 602 previously unscreened Korean men who underwent initial prostate biopsies. Using 2,313 cases in a validation cohort, KPCRC-HG was compared with the European Randomized Study of Screening for PC Risk Calculator for high-grade cancer (ERSPCRC-HG) and the Prostate Cancer Prevention Trial Risk Calculator 2.0 for high-grade cancer (PCPTRC-HG). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots.

RESULTS

PC was detected in 172 (28.6%) men, 120 (19.9%) of whom had PC of Gleason score 7 or higher. Independent predictors included prostate-specific antigen levels, digital rectal examination findings, transrectal ultrasound findings, and prostate volume. The AUC of the KPCRC-HG (0.84) was higher than that of the PCPTRC-HG (0.79, p<0.001) but not different from that of the ERSPCRC-HG (0.83) on external validation. Calibration plots also revealed better performance of KPCRC-HG and ERSPCRC-HG than that of PCPTRC-HG on external validation. At a cut-off of 5% for KPCRC-HG, 253 of the 2,313 men (11%) would not have been biopsied, and 14 of the 614 PC cases with Gleason score 7 or higher (2%) would not have been diagnosed.

CONCLUSIONS

KPCRC-HG is the first web-based high-grade prostate cancer prediction model in Korea. It had higher predictive accuracy than PCPTRC-HG in a Korean population and showed similar performance with ERSPCRC-HG in a Korean population. This prediction model could help avoid unnecessary biopsy and reduce overdiagnosis and overtreatment in clinical settings.

Magnetic Resonance Imaging Provides Added Value to the Prostate Cancer Prevention Trial Risk Calculator for Patients With Estimated Risk of High-grade Prostate Cancer Less Than or Equal to 10

OBJECTIVE

To determine the added value of prostate magnetic resonance imaging (MRI) to the Prostate Cancer Prevention Trial risk calculator.

METHODS

Between January 2012 and December 2015, 339 patients underwent prostate MRI prior to biopsy at our institution. MRI was considered positive if there was at least 1 Prostate Imaging Reporting and Data System 4 or 5 MRI suspicious region. Logistic regression was used to develop 2 models: biopsy outcome as a function of the (1) Prostate Cancer Prevention Trial risk calculator alone and (2) combined with MRI findings.

RESULTS

When including all patients, the Prostate Cancer Prevention Trial with and without MRI models performed similarly (area under the curve [AUC] = 0.74 and 0.78, P = .06). When restricting the cohort to patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%, the model with MRI outperformed the Prostate Cancer Prevention Trial alone model (AUC = 0.69 and 0.60, P = .01). Within this cohort of patients, there was no significant difference in discrimination between models for those with previous negative biopsy (AUC = 0.61 vs 0.63, P = .76), whereas there was a significant improvement in discrimination with the MRI model for biopsy-naïve patients (AUC = 0.72 vs 0.60, P = .01).

CONCLUSION

The use of prostate MRI in addition to the Prostate Cancer Prevention Trial risk calculator provides a significant improvement in clinical risk discrimination for patients with estimated risk of high-grade (Gleason ≥7) prostate cancer ≤10%. Prebiopsy prostate MRI should be strongly considered for these patients.

Novel biomarkers for the detection of prostate cancer

Prostate-specific antigen (PSA) is widely used as a biomarker in the detection of prostate cancer and for decision making regarding treatment options, response to therapy, and clinical follow-up. Despite its widespread use, it is well recognised that PSA has suboptimal performance as a screening tool due to poor specificity, resulting in high negative biopsy rates and potential ‘over-diagnosis’ and ‘over-treatment’ of clinically insignificant cancers. In particular, PSA does not reliably distinguish either cancer from benign prostatic conditions, or ‘clinically significant’ from ‘indolent cancers’, and it is inaccurate in predicting disease burden and response to treatment. There is an urgent demand for novel biomarkers to address these clinical needs. This article provides an update on the novel candidate biomarkers in development, which have shown potential for improving the detection of clinically significant cases of this malignancy.

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.

Determination of the Role of Negative Magnetic Resonance Imaging of the Prostate in Clinical Practice: Is Biopsy Still Necessary?

OBJECTIVE

To assess the negative predictive value (NPV) of multiparametric magnetic resonance imaging (mpMRI) for detection of prostate cancer (PCa) in routine clinical practice and to identify characteristics of patients for whom mpMRI fails to detect high-grade (Gleason score ≥7) disease.

MATERIALS AND METHODS

We reviewed our prospectively maintained database of consecutive men who received prostate mpMRI at our institution, interpreted by a clinical practice of academic radiologists. Between January 2012 and December 2015, 84 men without any magnetic resonance imaging suspicious regions according to prior institutional classification, or with Prostate Imaging Reporting and Data System (PI-RADS) 1-2 lesions according to the PI-RADS system, underwent standard template transrectal ultrasound (TRUS)-guided prostate biopsy. Using these biopsy results, we calculated the NPV of mpMRI for the detection of PCa and identified patient risk factors for having a Gleason score ≥7 PCa on biopsy.

RESULTS

High-grade PCa (Gleason score ≥7) was found on TRUS biopsy in 10.3% of biopsy-naive patients (NPV=89.7%), 16.7% of patients with previous negative biopsy (NPV=83.3%), and 13.3% of patients on active surveillance (NPV=86.6%). On multivariate analysis, the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) estimated risk for high-grade PCa (as a continuous variable) was a significant predictor for high-grade PCa on biopsy (odds ratio 1.01, P < .01).

CONCLUSION

Men with negative mpMRIs interpreted in a routine clinical setting have a significant risk of harboring Gleason score ≥7 PCa on a standard 12-region template biopsy, independent of indication. Standard template TRUS prostate biopsy should still be recommended for patients with negative mpMRI, particularly those with elevated PCPTRC estimated risk of high-grade PCa.

Detection of High Grade Prostate Cancer among PLCO Participants Using a Prespecified 4-Kallikrein Marker Panel

PURPOSE

We assessed the performance of a 4-kallikrein panel with and without microseminoprotein-β to predict high grade (Gleason 7+/Gleason Grade Group 2+) prostate cancer on biopsy in a multiethnic cohort from PLCO (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial).

MATERIALS AND METHODS

Levels of free, intact, total prostate specific antigen, human kallikrein-2 and microseminoprotein-β were measured while blinded to outcomes in cryopreserved serum from men in the intervention arm of PLCO. Marker levels of 946 men, of whom 100 were African American, were incorporated into a prespecified statistical model to predict high grade prostate cancer on biopsy.

RESULTS

The detection of high grade prostate cancer in 94 men (10%) was enhanced by the 4-kallikrein panel with an AUC of 0.79 compared to 0.73 for PCPTRC (Prostate Cancer Prevention Trial Risk Calculator), representing a 0.060 increase (95% CI 0.032-0.088, p <0.01). Additionally, the AUC increased from 0.79 to 0.81 when microseminoprotein-β was added to the 4-kallikrein panel. In African American men, the 4-kallikrein panel model also enhanced high grade prostate cancer detection over that of prostate specific antigen (AUC 0.80 vs 0.67). As an illustration of clinical implications, using 1 cutoff point for biopsy (6% risk of high grade prostate cancer) with the 4-kallikrein panel model would have eliminated unnecessary biopsies in 420 per 1,000 men (42%) while detecting high grade prostate cancer in 83 of 93 (88%).

CONCLUSIONS

In a multiethnic United States population, the 4-kallikrein panel demonstrated improved risk discrimination for high grade prostate cancer over conventional clinical variables (age, prostate specific antigen and digital rectal examination) as well as PCPTRC.

What is the best way not to treat prostate cancer?

INTRODUCTION

Selective treatment approaches for prostate cancer (PCa) are warranted given the highly varied nature of the disease and the consequences associated with definitive therapy.

MATERIALS AND METHODS

We present a stepwise overview of strategies optimized to not treat PCa, ranging from improved screening practices that seek to maximize the yield at initial diagnosis, as well as refinements to clinical risk prediction and the performance of active surveillance.

RESULTS

Improved adherence to screening guidelines offering simplistic, rational practice recommendations are poised to improve the performance of early detection strategies. In addition, measures to improve the quality of PCa screening would include greater integration of novel markers with higher specificity for clinically significant disease, in an effort to stem the tide of over-diagnosis and consequential overtreatment of low-grade tumors. For men diagnosed with PCa, the use of validated, multi-variable risk stratification stands to offer greater certainty in initial management choices: consideration of active surveillance for those with low-risk status, and definitive therapy for men with intermediate and high-risk features. We review the efficacy and nature of active surveillance protocols, and offer a context for refinements that may be anticipated with future study.

CONCLUSIONS

The question of how best to not treat prostate cancer is often more complex than policies of universal treatment, yet is integral to minimize morbidity of over-treatment in patients with low-risk tumors. An array of refined risk stratification instruments, biomarkers, and genomic assays seek to improve the confidence both prior to, and following diagnosis.

Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies

BACKGROUND

Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value.

METHODS

Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer.

RESULTS

Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy.

CONCLUSIONS

Low DNA-methylation levels in PCa-negative biopsies led to a NPV of 96% for high-grade cancer. The risk score, comprising DNA-methylation intensity and traditional clinical risk factors, improved the identification of men with high-grade cancer, with a maximum avoidance of unnecessary repeat biopsies. This risk score resulted in better patient risk stratification and significantly outperformed current risk prediction models such as PCPTRC and PSA. The risk score could help to identify patients with histopathologically negative biopsies harboring high-grade PCa. Prostate 76:1078-1087, 2016. © 2016 The Authors. The Prostate Published by Wiley Periodicals, Inc.

Serial Percent Free Prostate Specific Antigen in Combination with Prostate Specific Antigen for Population Based Early Detection of Prostate Cancer

PURPOSE

We characterized the diagnostic properties of serial percent free prostate specific antigen in relation to prostate specific antigen in a multiethnic, multiracial cohort of healthy men.

MATERIALS AND METHODS

A total of 6,982 percent free prostate specific antigen and prostate specific antigen measurements were obtained from participants in a greater than 12-year Texas screening study comprising 1,625 men who never underwent biopsy, 497 who underwent 1 or more biopsies negative for prostate cancer and 61 diagnosed with prostate cancer. We evaluated the ROC AUC of percent free prostate specific antigen and the proportion of patients with fluctuating values across multiple visits determined according to 2 thresholds (less than 15% vs 25%). The proportion of cancer cases in which percent free prostate specific antigen indicated a positive test before prostate specific antigen greater than 4 ng/ml did and the number of negative biopsies that would have been spared by negative percent free prostate specific antigen test results were calculated.

RESULTS

Percent free prostate specific antigen fluctuated around its threshold of less than 25% (less than 15%) in 38.3% (78.1%), 42.2% (20.9%), and 11.4% (25.7%) of patients never biopsied, and with negative and positive biopsies, respectively. At the same thresholds, percent free prostate specific antigen tested positive earlier than prostate specific antigen in 71.4% and 34.2% of cancer cases, respectively. Among men with multiple negative biopsies and PSA greater than 4 ng/ml, percent free PSA would have tested negative in 31.6% and 65.8%, respectively.

CONCLUSIONS

Percent free prostate specific antigen should accompany prostate specific antigen testing to potentially spare unnecessary biopsies or detect cancer earlier. When near the threshold, both tests should be repeated due to commonly observed fluctuation.

Age and Prostate-Specific Antigen Level Prior to Diagnosis Predict Risk of Death from Prostate Cancer

A single early prostate-specific antigen (PSA) level has been correlated with a higher likelihood of prostate cancer diagnosis and death in younger men. PSA testing in older men has been considered of limited utility. We evaluated prostate cancer death in relation to age and PSA level immediately prior to prostate cancer diagnosis. Using the Veterans Affairs database, we identified 230,081 men aged 50-89 years diagnosed with prostate cancer and at least one prior PSA test between 1999 and 2009. Prostate cancer-specific death over time was calculated for patients stratified by age group (e.g., 50-59 years, through 80-89 years) and PSA range at diagnosis (10 ranges) using Kaplan-Meier methods. Risk of 10-year prostate cancer mortality across age and PSA was compared using log-rank tests with a Bonferroni adjustment for multiple testing. 10.5% of men diagnosed with prostate cancer died of cancer during the 10-year study period (mean follow-up = 3.7 years). Higher PSA values prior to diagnosis predict a higher risk of death in all age groups (p < 0.0001). Within the same PSA range, older age groups are at increased risk for death from prostate cancer (p < 0.0001). For PSA of 7-10 ng/mL, cancer-specific death, 10 years after diagnosis, increased from 7% for age 50-59 years to 51% for age 80-89 years. Men older than 70 years are more likely to die of prostate cancer at any PSA level than younger men, suggesting prostate cancer remains a significant problem among older men (even those aged 80+) and deserves additional study.

Prostate health index (PHI) and prostate-specific antigen (PSA) predictive models for prostate cancer in the Chinese population and the role of digital rectal examination-estimated prostate volume

PURPOSE

To investigate PSA- and PHI (prostate health index)-based models for prediction of prostate cancer (PCa) and the feasibility of using DRE-estimated prostate volume (DRE-PV) in the models.

METHODS

This study included 569 Chinese men with PSA 4-10 ng/mL and non-suspicious DRE with transrectal ultrasound (TRUS) 10-core prostate biopsies performed between April 2008 and July 2015. DRE-PV was estimated using 3 pre-defined classes: 25, 40, or 60 ml. The performance of PSA-based and PHI-based predictive models including age, DRE-PV, and TRUS prostate volume (TRUS-PV) was analyzed using logistic regression and area under the receiver operating curves (AUC), in both the whole cohort and the screening age group of 55-75.

RESULTS

PCa and high-grade PCa (HGPCa) was diagnosed in 10.9 % (62/569) and 2.8 % (16/569) men, respectively. The performance of DRE-PV-based models was similar to TRUS-PV-based models. In the age group 55-75, the AUCs for PCa of PSA alone, PSA with DRE-PV and age, PHI alone, PHI with DRE-PV and age, and PHI with TRUS-PV and age were 0.54, 0.71, 0.76, 0.78, and 0.78, respectively. The corresponding AUCs for HGPCa were higher (0.60, 0.70, 0.85, 0.83, and 0.83). At 10 and 20 % risk threshold for PCa, 38.4 and 55.4 % biopsies could be avoided in the PHI-based model, respectively.

CONCLUSIONS

PHI had better performance over PSA-based models and could reduce unnecessary biopsies. A DRE-assessed PV can replace TRUS-assessed PV in multivariate prediction models to facilitate clinical use.

Unfavorable Intermediate-Risk Prostate Cancer and the Odds of Upgrading to Gleason 8 or Higher at Prostatectomy

BACKGROUND

Some men with unfavorable intermediate-risk prostate cancer (PC) have occult disease with a Gleason score of 8 or higher unrecognized on biopsy because of a sampling error that would change management to long from short course androgen-deprivation therapy in conjunction with radiotherapy. Identifying such men could improve outcomes.

PATIENTS AND METHODS

The study cohort consisted of 136 consecutive men with unfavorable intermediate-risk PC who underwent radical prostatectomy (RP) between 2005 and 2008. We performed logistic regression analysis to identify clinical factors associated with upgrading to a Gleason score of 8 or higher at RP.

RESULTS

Fourteen percent of the men were upgraded to a Gleason score of 8 or higher PC at RP. Both increasing prostate-specific antigen (PSA) (adjusted odds ratio, 1.98; 95% confidence interval, 1.19, 3.30; P = .01) and greatest percentage core length (GPC) (adjusted odds ratio, 1.11; 95% confidence interval, 1.03, 1.19; P < .01) were significantly associated with upgrading. A significant interaction between PSA and GPC was observed (P = .01). Specifically, men with low PSA (< 5 ng/mL) and those with larger GPC (> 70%) were significantly more likely to have a Gleason score of 8 or higher at RP compared to men with low PSA and GPC of 70% or less (35% vs. 0%; P = .01), whereas the same was not true among men with PSA levels ≥ 5 ng/mL (16% vs. 9%; P = .36).

CONCLUSION

In men with unfavorable intermediate-risk PC, a multiparametric magnetic resonance imaging could be considered when the PSA is low and the percentage core length high to identify occult Gleason score 8 or higher disease and change management from short to long course androgen-deprivation therapy and radiotherapy.

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.

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

OBJECTIVE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests

Many decisions in medicine involve trade-offs, such as between diagnosing patients with disease versus unnecessary additional testing for those who are healthy. Net benefit is an increasingly reported decision analytic measure that puts benefits and harms on the same scale. This is achieved by specifying an exchange rate, a clinical judgment of the relative value of benefits (such as detecting a cancer) and harms (such as unnecessary biopsy) associated with models, markers, and tests. The exchange rate can be derived by asking simple questions, such as the maximum number of patients a doctor would recommend for biopsy to find one cancer. As the answers to these sorts of questions are subjective, it is possible to plot net benefit for a range of reasonable exchange rates in a “decision curve.” For clinical prediction models, the exchange rate is related to the probability threshold to determine whether a patient is classified as being positive or negative for a disease. Net benefit is useful for determining whether basing clinical decisions on a model, marker, or test would do more good than harm. This is in contrast to traditional measures such as sensitivity, specificity, or area under the curve, which are statistical abstractions not directly informative about clinical value. Recent years have seen an increase in practical applications of net benefit analysis to research data. This is a welcome development, since decision analytic techniques are of particular value when the purpose of a model, marker, or test is to help doctors make better clinical decisions.

Biomarkers in localized prostate cancer

Biomarkers can improve prostate cancer diagnosis and treatment. Accuracy of prostate-specific antigen (PSA) for early diagnosis of prostate cancer is not satisfactory, as it is an organ- but not cancer-specific biomarker, and it can be improved by using models that incorporate PSA along with other test results, such as prostate cancer antigen 3, the molecular forms of PSA (proPSA, benign PSA and intact PSA), as well as kallikreins. Recent reports suggest that new tools may be provided by metabolomic studies as shown by preliminary data on sarcosine. Additional molecular biomarkers have been identified by the use of genomics, proteomics and metabolomics. We review the most relevant biomarkers for early diagnosis and management of localized prostate cancer.

CLINICAL EVALUATION OF AN EPIGENETIC ASSAY TO PREDICT MISSED CANCER IN PROSTATE BIOPSY SPECIMENS

Approximately 1 million prostate biopsies are performed yearly in the United States, with only ~25% resulting in prostate cancer diagnosis. However, ~40% of men receive multiple biopsies for fear of cancer being missed. DNA hypermethylation is ideally suited for early disease detection and could be used to prevent unnecessary biopsies. Men with low-risk epigenetic signatures may forego subsequent biopsy and potential complications. A meta-analysis of two validation studies was conducted to gain additional insight into the benefits for patient risk stratification. In the Methylation Analysis to Locate Occult Cancer (MATLOC) study a negative predictive value of 90% was obtained, which represents a significant improvement over standard of care. This was confirmed in the Detection of Cancer Using Methylated Events in Negative Tissue (DOCUMENT) study (88% negative predictive value), which was designed to validate the performance in an independent cohort. The epigenetic assay, in combination with other known risk factors, may help reduce unnecessary repeat prostate biopsies and identify men at highest risk of harboring occult high-grade prostate cancer.