A graphical device to represent the outcomes of a logistic regression analysis

Development of prediction models based on risk scores for clinically significant prostate cancer on MRI/TRUS fusion biopsy

BACKGROUND

The implementation of population screening for prostate cancer has increased the number of patients with biochemical suspicion. Prediction models may reduce the number of unnecessary biopsies by identifying patients who benefit the most from them. Our aim is to develop a prediction model that is easily applicable in patients with suspicion of prostate cancer in the urology clinic setting to avoid unnecessary biopsies.

METHODS

We developed prediction models based on risk scores for the detection of prostate cancer and clinically significant prostate cancer using the TRIPOD guidelines. For this, we conducted an observational and retrospective review of computerised medical records of 204 patients undergoing prostate fusion biopsy between 2018 and 2021. We also reviewed other prediction models for prostate cancer including radiological parameters and targeted sampling of suspicious lesions.

RESULTS

A total of 204 patients underwent a biopsy, 138 were diagnosed of prostate cancer, and from them, 60 of clinically significant prostate cancer. Multivariate regression and random forest analysis were performed. Age, PSA density, diameter of the index lesions and PIRADS score on MRI were identified as predictors with an Area Under the Curve ranging between 0.71 and 0.80 and acceptable calibration results. Risk scores may avoid between 21.7% and 48.1% of biopsies.

CONCLUSION

Our prediction models are characterised by ease of use and may reduce unnecessary biopsies with satisfactory discrimination and calibration results while bringing benefits to the healthcare system and patients.

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms

OBJECTIVES

Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.

MATERIAL AND METHODS

One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.

RESULTS

Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001).

CONCLUSIONS

Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment.

CLINICAL RELEVANCE STATEMENT

For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure.

KEY POINTS

The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.

Pilot study for generating and assessing nomograms and decision curves analysis to predict clinically significant prostate cancer using only spatially registered multi-parametric MRI

Background

Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor's eccentricity (shape), signal-to-clutter ratio (SCR), and volume.

Purpose

Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer.

Methods

This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor's eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as "Clinically Significant" ("Insignificant"). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance.

Results

Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating "all," or "none." Training/test sets achieved comparable AUC but with higher p-values.

Conclusions

Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS.

Early prediction of prostate cancer risk in younger men using polygenic risk scores and electronic health records

BACKGROUND

Prostate cancer (PCa) screening is not routinely conducted in men aged 55 and younger, although this age group accounts for more than 10% of cases. Polygenic risk scores (PRSs) and patient data applied toward early prediction of PCa may lead to earlier interventions and increased survival. We have developed machine learning (ML) models to predict PCa risk in men 55 and under using PRSs combined with patient data.

METHODS

We conducted a retrospective study on 91,106 male patients aged 35-55 using the UK Biobank database. Five gradient boosting models were developed and validated utilizing routine screening data, PRSs, additional clinical data, or combinations of the three.

RESULTS

Combinations of PRSs and patient data outperformed models that utilized PRS or patient data only, and the highest performing models achieved an area under the receiver operating characteristic curve of 0.788. Our models demonstrated a substantially lower false positive rate (35.4%) in comparison to standard screening using prostate-specific antigen (60%-67%).

CONCLUSION

This study provides the first preliminary evidence for the use of PRSs with patient data in a ML algorithm for PCa risk prediction in men aged 55 and under for whom screening is not standard practice.

Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines

This article presents a new deep cross-output knowledge transfer approach based on least-squares support vector machines, called DCOT-LS-SVMs. Its aim is to improve the generalizability of least-squares support vector machines (LS-SVMs) while avoiding the complicated parameter tuning process that occurs in many kernel machines. The proposed approach has two significant characteristics: 1) DCOT-LS-SVMs is inspired by a stacked hierarchical architecture that combines several layer-by-layer LS-SVMs modules. The module that forms the higher layer has additional input features that consider the predictions from all previous modules and 2) cross-output knowledge transfer is used to leverage knowledge from the predictions of the previous module to improve the learning process in the current module. With this approach, the model's parameters, such as a tradeoff parameter C and a kernel width δ , can be randomly assigned to each module in order to greatly simplify the learning process. Moreover, DCOT-LS-SVMs is able to autonomously and quickly decide the extent of the cross-output knowledge transfer between adjacent modules through a fast leave-one-out cross-validation strategy. In addition, we present an imbalanced version of DCOT-LS-SVMs, called IDCOT-LS-SVMs, given that imbalanced datasets are common in real-world scenarios. The effectiveness of the proposed approaches is demonstrated through a comparison with five comparative methods on UCI datasets and with a case study on the diagnosis of prostate cancer.

Individualised non-contrast MRI-based risk estimation and shared decision-making in men with a suspicion of prostate cancer: protocol for multicentre randomised controlled trial (multi-IMPROD V.2.0)

INTRODUCTION

European Association of Urology and UK National Institute for Health and Care Excellence guidelines recommend that all men with suspicions of prostate cancer should undergo prebiopsy contrast enhanced, that is, multiparametric prostate MRI. Subsequent prostate biopsies should also be performed if MRI is positive, that is, Prostate Imaging-Reporting and Data System (PI-RADS) scores 3-5. However, several retrospective post hoc analyses have shown that this approach still leads to many unnecessary biopsy procedures. For example, 88%-96% of men with PI-RADS, three findings are still diagnosed with clinically non-significant prostate cancer or no cancer at all.

METHODS AND ANALYSIS

This is a prospective, randomised, controlled, multicentre trial, being conducted in Finland, to demonstrate non-inferiority in clinically significant cancer detection rates among men undergoing prostate biopsies post-MRI and men undergoing prostate biopsies post-MRI only after a shared decision based on individualised risk estimation. Men without previous diagnosis of prostate cancer and with abnormal digital rectal examination findings and/or prostate-specific antigen between 2.5 ug/L and 20.0 ug/L are included. We aim to recruit 830 men who are randomised at a 1:1 ratio into control (all undergo biopsies after MRI) and intervention arms (the decision to perform biopsies is based on risk estimation and shared decision-making). The primary outcome of the study is the proportion of men with clinically significant prostate cancer (Gleason 4+3 prostate cancer or higher). We will also compare the overall biopsy rate, benign biopsy rate and the detection of non-significant prostate cancer between the two study groups.

ETHICS AND DISSEMINATION

The study (protocol V.2.0, 4 January 2021) was approved by the Ethics Committee of the Hospital District of Southwest Finland (IORG number: 0001744, IBR number: 00002216; trial number: 99/1801/2019). Participants are required to provide written informed consent. Full reports of this study will be submitted to peer-reviewed journals, mainly urology and radiology.

TRIAL REGISTRATION NUMBER

NCT04287088; the study is registered at ClinicalTrials.gov.

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.

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

OBJECTIVE

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

DESIGN

Systematic review.

DATA SOURCES

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

ELIGIBILITY

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

DATA EXTRACTION AND SYNTHESIS

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

RESULTS

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

CONCLUSION

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

External validation of Cormio nomogram for predicting all prostate cancers and clinically significant prostate cancers

PURPOSE

Recently, the Cormio et al. nomogram has been developed to predict prostate cancer (PCa) and clinically significant PCa using benign prostatic obstruction parameters. The aim of the present study was to externally validate the nomogram in a multicentric cohort.

METHODS

Between 2013 and 2019, patients scheduled for ultrasound-guided prostate biopsy were prospectively enrolled at 11 Italian institutions. Demographic, clinical and histological data were collected and analysed. Discrimination and calibration of Cormio nomogram were assessed with the receiver operator characteristics (ROC) curve and calibration plots. The clinical net benefit of the nomogram was assessed with decision curve analysis. Clinically significant PCa was defined as ISUP grade group > 1.

RESULTS

After accounting for inclusion criteria, 1377 patients were analysed. 816/1377 (59%) had cancer at final pathology (574/816, 70%, clinically significant PCa). Multivariable analysis showed age, prostate volume, DRE and post-voided residual volume as independent predictors of any PCa. Discrimination of the nomogram for cancer was 0.70 on ROC analysis. Calibration of the nomogram was excellent (p = 0.94) and the nomogram presented a net benefit in the 40-80% range of probabilities. Multivariable analysis for predictors of clinically significant PCa found age, PSA, prostate volume and DRE as independent variables. Discrimination of the nomogram was 0.73. Calibration was poor (p = 0.001) and the nomogram presented a net benefit in the 25-75% range of probabilities.

CONCLUSION

We confirmed that the Cormio nomogram can be used to predict the risk of PCa in patients at increased risk. Implementation of the nomogram in clinical practice will better define its role in the patient's counselling before prostate biopsy.

A Magnetic Resonance Imaging-Based Prediction Model for Prostate Biopsy Risk Stratification

Importance

Multiparametric magnetic resonance imaging (MRI) in conjunction with MRI-transrectal ultrasound (TRUS) fusion-guided biopsies have improved the detection of prostate cancer. It is unclear whether MRI itself adds additional value to multivariable prediction models based on clinical parameters.

Objective

To determine whether an MRI-based prediction model can reduce unnecessary biopsies in patients with suspected prostate cancer.

Design, Setting, and Participants

Patients underwent MRI, MRI-TRUS fusion-guided biopsy, and 12-core systematic biopsy in 1 session. The development cohort used to derive the prediction model consisted of 400 patients from 1 institution enrolled between May 14, 2015, and August 31, 2016, and the validation cohort included 251 patients from 2 independent institutions who underwent biopsies between April 1, 2013, and June 30, 2016, at 1 institution and between July 1, 2015, and October 31, 2016, at the other institution. The MRI model included MRI-derived parameters in addition to clinical variables. Area under the curve of receiver operating characteristic curves and decision curve analysis were performed.

Main Outcomes and Measures

Risk of clinically significant prostate cancer on biopsy, defined as a Gleason score of 3 + 4 or higher in at least 1 biopsy core.

Results

Overall, 193 (48.3%) of the 400 patients in the development cohort (mean [SD] age at biopsy, 64.3 [7.1] years) and 96 (38.2%) of the 251 patients in the validation cohort (mean [SD] age at biopsy, 64.9 [7.2] years) had clinically significant prostate cancer, defined as a Gleason score greater than or equal to 3 + 4. By applying the model to the external validation cohort, the area under the curve increased from 64% to 84% compared with the baseline model (P < .001). At a risk threshold of 20%, the MRI model had a lower false-positive rate than the baseline model (46% [95% CI, 32%-66%] vs 92% [95% CI, 70%-100%]), with only a small reduction in the true-positive rate (89% [95% CI, 85%-96%] vs 99% [95% CI, 89%-100%]). Eighteen of 100 fewer biopsies could have been performed, with no increase in the number of patients with missed clinically significant prostate cancers.

Conclusions and Relevance

The inclusion of MRI-derived parameters in a risk model could reduce the number of unnecessary biopsies while maintaining a high rate of diagnosis of clinically significant prostate cancers.

Diagnosis of prostate cancer in a Chinese population by using machine learning methods

An early diagnosis of prostate cancer (PC) is key for the successful treatment. Although invasive prostate biopsies can provide a definitive diagnosis, the number of biopsies should be reduced to avoid side effects and risks especially for the men with the low risk of cancer. Therefore, an accurate model is in need to predict PC with the aim of reducing unnecessary biopsies. In this study, we developed predictive models using four machine learning methods including Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Artificial Neural Network (ANN) and Random Forest (RF) to detect PC cases using available prebiopsy information. The models were constructed and evaluated on a cohort of 1625 Chinese men with prostate biopsies from Hong Kong hospital. All the models have the excellent performances in detecting significant PC cases, with ANN achieving the highest accuracy of 0.9527 and the AUC value of 0.9755. RF outperformed the other three methods in classifying benign, significant and insignificant PC cases, with an accuracy of 0.9741 and a F1 score of 0.8290.

Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions

Background and purpose

Individualized risk-adapted algorithms in prostate cancer (PCa) diagnosis using predictive prebiopsy variables in addition to prostate-specific antigen value may result in a considerable reduction of unnecessary systematic biopsies. Multi-parametric magnetic resonance imaging (mpMRI) has emerged as a secondary prediction tool that can further improve the detection of clinically significant prostate cancer (csPCa). This review explores the performance of new MRI risk models for indicating a biopsy for prostate cancer diagnosis.

Results and considerations

The area under the receiver-operating characteristic curve for detecting csPCa varies between 0.64 and 0.91 in biopsy-naïve men, and between 0.78 and 0.93 in men with a previous negative biopsy. The utility of multivariate risk prediction tools including MRI suspicion scores as an extra input parameter has the potential to avoid a notable number of biopsies and detection of clinically insignificant PCa at a low price of missing some csPCa. The trade-off depends on the risk threshold that is chosen. In biopsy-naïve men a net benefit was obtained at a risk threshold of above 10% for csPCa in most MRI risk prediction models. All constructed MRI risk models used (referral) patient cohorts with high prevalence of csPCa. Using more representative cohorts from daily clinical screening, net benefit may attenuate at lower risk thresholds. Strengths and limitations of these models are discussed.

Future directions

To assess their wider applicability, in-depth analysis of mpMRI predictive qualities should be further investigated, in combination with required external validation of these models in a multicenter setting with large prospective datasets.

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 Cancer in Primary Care

Prostate cancer is a common malignancy seen worldwide. The incidence has risen in recent decades, mainly fuelled by more widespread use of prostate-specific antigen (PSA) testing, although prostate cancer mortality rates have remained relatively static over that time period. A man's risk of prostate cancer is affected by his age and family history of the disease. Men with prostate cancer generally present symptomatically in primary care settings, although some diagnoses are made in asymptomatic men undergoing opportunistic PSA screening. Symptoms traditionally thought to correlate with prostate cancer include lower urinary tract symptoms (LUTS), such as nocturia and poor urinary stream, erectile dysfunction and visible haematuria. However, there is significant crossover in symptoms between prostate cancer and benign conditions affecting the prostate such as benign prostatic hypertrophy (BPH) and prostatitis, making it very challenging to distinguish between them on the basis of symptoms. The evidence for the performance of PSA in asymptomatic and symptomatic men for the diagnosis of prostate cancer is equivocal. PSA is subject to false positive and false negative results, affecting its clinical utility as a standalone test. Clinicians need to counsel men about the risks and benefits of PSA testing to inform their decision-making. Digital rectal examination (DRE) by primary care clinicians has some evidence to show discrimination between benign and malignant conditions affecting the prostate. Patients referred to secondary care for diagnostic testing for prostate cancer will typically undergo a transrectal or transperineal biopsy, where a number of samples are taken and sent for histological examination. These biopsies are invasive procedures with side effects and a risk of infection and sepsis, and alternative tests such as multiparametric magnetic resonance imaging (mpMRI) are currently being trialled for their accuracy and safety in diagnosing clinically significant prostate cancer.

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.

eHealth and mHealth in prostate cancer detection and active surveillance

eHealth and mobile health (mHealth) offer patients, healthcare providers, researchers, and policy makers new potential to improve wellness, practice prevention and reduce suffering from diseases. While the eHealth market is growing to an expected US $26 billion, its potential in the field of Urology is still underused. Research has shown that currently only 176 apps (of the 300,000 medical apps available) were found in the Apple App Store and Google Play Store, of which 20 were prostate cancer related. Three good examples of eHealth/mHealth applications are the Rotterdam Prostate Cancer Risk Calculator (RPCRC) website and app, the Prostate cancer Research International Active Surveillance (PRIAS) website and the Follow MyPSA app for men on active surveillance for prostate cancer: they are tools with a clear vision that offer true added value in daily clinical practice and which positively influence healthcare beyond borders. To increase the uptake of eHealth applications in the coming years, it is important to involve professionals in their design and development, and to guarantee the safety and privacy of its users and their data.

On cribriform prostate cancer

The management of newly diagnosed prostate cancer is challenging because of its heterogeneity in histology, genetics and clinical outcome. The clinical outcome of patients with Gleason score 7 prostate cancer varies greatly. Improving risk assessment in this group is of particular interest, as Gleason score 7 prostate cancer on biopsy is an important clinical threshold for active treatment. Architecturally, four Gleason grade 4 growth patterns are recognized: ill-formed, fused, glomeruloid and cribriform. The aim of this review is to describe the role of cribriform growth in prostate cancer with respect to diagnosis, prognosis and molecular pathology. Secondly, we will discuss clinical applications for cribriform prostate cancer and give recommendations for future research.

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts

OBJECTIVES

To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age.

DESIGN

Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa.

SETTING

Multiple institutions that were members of international PRACTICAL consortium.

PARTICIPANTS

All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men.

MAIN OUTCOME MEASURES

Prediction with hazard score of age of onset of aggressive cancer in validation set.

RESULTS

In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score.

CONCLUSIONS

Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Individualized Approach to Cancer Screening in Older Adults

The primary goal of cancer screening is early detection of cancer to reduce cancer-specific mortality and morbidity. The benefits of screening in older adults are uncertain due to paucity of evidence. Extrapolating data from younger populations, evidence suggests that the benefit occurs years later from the time of initial screening and therefore may not be applicable in those older adults with limited life expectancy. Contrast this with the harms of screening, which are more immediate and increase with age and comorbidities. An individualized approach to cancer screening takes these factors into consideration, allowing for thoughtful decision making for older adults.

Are prostate biopsies necessary for all patients 75years and older?

PURPOSE

To develop nomograms predicting prostate cancer (PCa) and high-grade PCa (HGPCa) in the elderly population.

METHODS

We reviewed the data of patients aged 75years and older who underwent first-time prostate biopsy and multiparametric magnetic resonance imaging (mpMRI). The nomograms were developed based on multivariate analysis and evaluated. We performed the external validation and calibration of the risk calculators from the European Randomized Study of Screening for Prostate Cancer (ERSPC) and the Prostate Cancer Prevention Trial (PCPT).

RESULTS

The present study included 302 subjects with a median age of 78years (range: 75-91years). Overall, 225 and 129 subjects were diagnosed with PCa and HGPCa (Gleason score≥4+3), respectively. The ratio of free-to-total PSA, prostate-specific antigen density (PSAD), transrectal ultrasound (TRUS), and Prostate Imaging Reporting and Data System (PI-RADS) were used to develop the PCa-predicting nomogram, and PSAD, TRUS, and PI-RADS were used to develop the HGPCa-predicting nomogram. The area under the curve (AUC) values of PCa-predicting and HGPCa-predicting nomograms were 0.90 and 0.87. The ERSPC calculator had acceptable external calibration and validation outcomes. We recommended a cut-off probability of 42% for PCa-predicting nomogram when used in healthy older men to achieve a sensitivity of 95.6%, and a cut-off probability of 73% for HGPCa-predicting nomogram when used in vulnerable older men to achieve a specificity of 98.3%.

CONCLUSIONS

The present nomograms could help discriminate patients with PCa from healthy elder adults for standard treatment, and discriminate patients with HGPCa from vulnerable elder adults for modified treatment. External validation is expected.

A comparison of 3 on-line nomograms with the detection of primary circulating prostate cells to predict prostate cancer at initial biopsy

INTRODUCTION

The use of nomograms which include the PSA may improve the predictive power of obtaining a prostate biopsy (PB) positive for cancer. We compare the use of three on-line nomagrams with the detection of primary malignant circulating prostate cells (CPCs) to predict the results of an initial PB in men with suspicion of prostate cancer.

METHODS AND PATIENTS

Consecutive men with suspicion of prostate cancer underwent a 12 core TRUS prostate biopsy; age, total serum PSA, percent free PSA, family history, ethnic origin and prostate ultrasound results were used for risk assessment using the online nomograms. Mononuclear cells were obtained by differential gel centrifugation from 8ml of blood and CPCs were identified using double immunomarcation with anti-PSA and anti-P504S. A CPC was defined as a cell expressing PSA and P504S and defined as negative/positive. Biopsies were classified as cancer/no-cancer. Areas under the curve (AUC) for each parameter were calculated and compared and diagnostic yields were calculated.

RESULTS

1,223 men aged>55 years participated, 467 (38.2%) had a biopsy positive for cancer of whom 114/467 (24.4%) complied with the criteria for active observation. Area under the curve analysis showed CPC detection to be superior (p<0.001), avoiding 57% of potential biopsies while missing 4% of clinically significant prostate cancers.

CONCLUSIONS

The CPC detection was superior to the nomograms in predicting the presence of prostate cancer at initial biopsy; its high negative predictive value potentially reduces the number of biopsies while missing few significant cancers, being superior to the nomograms in this aspect. Being a positive/negative test the detection of CPCs avoids defining a cutoff value which may differ between populations.

Cost-Effectiveness Comparison of Imaging-Guided Prostate Biopsy Techniques: Systematic Transrectal Ultrasound, Direct In-Bore MRI, and Image Fusion

OBJECTIVE

Three commonly used prostate biopsy approaches are systematic transrectal ultrasound guided, direct in-bore MRI guided, and image fusion guided. The aim of this study was to calculate which strategy is most cost-effective.

MATERIALS AND METHODS

A decision tree and Markov model were developed to compare cost-effectiveness. Literature review and expert opinion were used as input. A strategy was deemed cost-effective if the costs of gaining one quality-adjusted life year (incremental cost-effectiveness ratio) did not exceed the willingness-to-pay threshold of €80,000 (≈$85,000 in January 2017). A base case analysis was performed to compare systematic transrectal ultrasound- and image fusion-guided biopsies. Because of a lack of appropriate literature regarding the accuracy of direct in-bore MRI-guided biopsy, a threshold analysis was performed.

RESULTS

The incremental cost-effectiveness ratio for fusion-guided biopsy compared with systematic transrectal ultrasound-guided biopsy was €1386 ($1470) per quality-adjusted life year gained, which was below the willingness-to-pay threshold and thus assumed cost-effective. If MRI findings are normal in a patient with clinically significant prostate cancer, the sensitivity of direct in-bore MRI-guided biopsy has to be at least 88.8%. If that is the case, the incremental cost-effectiveness ratio is €80,000 per quality-adjusted life year gained and thus cost-effective.

CONCLUSION

Fusion-guided biopsy seems to be cost-effective compared with systematic transrectal ultrasound-guided biopsy. Future research is needed to determine whether direct in-bore MRI-guided biopsy is the best pathway; in this study a threshold was calculated at which it would be cost-effective.

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

BACKGROUND

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

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

RESULTS AND LIMITATIONS

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

CONCLUSIONS

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

PATIENT SUMMARY

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

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.

Improving prediction models with new markers: a comparison of updating strategies

Background

New markers hold the promise of improving risk prediction for individual patients. We aimed to compare the performance of different strategies to extend a previously developed prediction model with a new marker.

Methods

Our motivating example was the extension of a risk calculator for prostate cancer with a new marker that was available in a relatively small dataset. Performance of the strategies was also investigated in simulations. Development, marker and test sets with different sample sizes originating from the same underlying population were generated. A prediction model was fitted using logistic regression in the development set, extended using the marker set and validated in the test set. Extension strategies considered were re-estimating individual regression coefficients, updating of predictions using conditional likelihood ratios (LR) and imputation of marker values in the development set and subsequently fitting a model in the combined development and marker sets. Sample sizes considered for the development and marker set were 500 and 100, 500 and 500, and 100 and 500 patients. Discriminative ability of the extended models was quantified using the concordance statistic (c-statistic) and calibration was quantified using the calibration slope.

Results

All strategies led to extended models with increased discrimination (c-statistic increase from 0.75 to 0.80 in test sets). Strategies estimating a large number of parameters (re-estimation of all coefficients and updating using conditional LR) led to overfitting (calibration slope below 1). Parsimonious methods, limiting the number of coefficients to be re-estimated, or applying shrinkage after model revision, limited the amount of overfitting. Combining the development and marker set using imputation of missing marker values approach led to consistently good performing models in all scenarios. Similar results were observed in the motivating example.

Conclusion

When the sample with the new marker information is small, parsimonious methods are required to prevent overfitting of a new prediction model. Combining all data with imputation of missing marker values is an attractive option, even if a relatively large marker data set is available.

Electronic supplementary material

The online version of this article (doi:10.1186/s12874-016-0231-2) contains supplementary material, which is available to authorized users.

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.

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.

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.

A Preliminary Study of the Ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for Predicting High-Grade Prostate Cancer

INTRODUCTION

To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT). By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4).

MATERIAL AND METHODS

Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves.

RESULTS

Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPC [25-75 percentile: 25-80.5%] vs. 16% [P 25-75: 8-26.5%] for non-HGPC; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%.

CONCLUSIONS

The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4KsT is a good classification model as a whole, followed by ERSPC-RC 4 and PCPTRC 2.0. The clinical utility curves help suggest cutoff points for clinical decisions: 9% for 4KsT and 3% for ERSPC-RC 4. This preliminary study should be interpreted with caution due to its limited sample size.

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

OBJECTIVE

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

PATIENTS AND METHODS

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

RESULTS

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

CONCLUSION

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

Risk calculators and updated tools to select and plan a repeat biopsy for prostate cancer detection

Millions of men each year are faced with a clinical suspicion of prostate cancer (PCa) but the prostate biopsy fails to detect the disease. For the urologists, how to select the appropriate candidate for repeat biopsy is a significant clinical dilemma. Traditional risk-stratification tools in this setting such as prostate-specific antigen (PSA) related markers and histopathology findings have met with limited correlation with cancer diagnosis or with significant disease. Thus, an individualized approach using predictive models such as an online risk calculator (RC) or updated biomarkers is more suitable in counseling men about their risk of harboring clinically significant prostate cancer. This review will focus on the available risk-stratification tools in the population of men with prior negative biopsies and persistent suspicion of PCa. The underlying methodology and platforms of the available tools are reviewed to better understand the development and validation of these models. The index patient is then assessed with different RCs to determine the range of heterogeneity among various RCs. This should allow the urologists to better incorporate these various risk-stratification tools into their clinical practice and improve patient counseling.

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

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

Prostate cancer nomograms: a review of their use in cancer detection and treatment

As prostate cancer treatment discussions have grown more complex, increasing numbers of nomograms to guide decision-making have been found in the literature. Such nomograms can influence every step in the prostate cancer therapeutic process, from determining the need for biopsy to the need for adjuvant therapy. With a properly counseled patient who is aware of the limitations of nomograms, such tools assist in the shared decision-making that characterizes modern informed consent.

The role of biomarkers in the assessment of prostate cancer risk prior to prostate biopsy: which markers matter and how should they be used?

Prostate cancer (PCa) screening has been substantially influenced by the clinical implementation of serum prostate-specific antigen (PSA). In this context, improvement of early PCa detection and stage migration as well as reduced PCa mortality were achieved, and up-to-date PSA represents the gold standard biomarker of PCa diagnosis together with clinical findings. Nonetheless, PSA shows weakness in discriminating between malign and benign prostatic disease or indolent and aggressive cancers. As a result, the expansion of PSA screening is extensively debated with regard to overdetection and ultimately overtreatment, keeping in mind that PCa is still the third leading cause of cancer-specific mortality in the Western male population. Consequently, today's task is to increase the accuracy of PCa detection and furthermore to allow stratification for indolent PCa that might permit active surveillance and to filter out aggressive cancers necessitating treatment. Thus, novel biomarkers, especially in combination with approved clinical risk factors (e.g., age or family history of PCa), within multivariable prediction models carry the potential to improve many aspects of PCa diagnosis and to enable risk classification in clinical practice. Multivariable models lead to superior accuracy for PCa prediction instead of the use of a single risk factor. The aim of this article was to present an overview of known risk factors for PCa together with new promising blood- and urine-based biomarkers and their application within risk models that may allow risk stratification for PCa prior to prostate biopsy. Risk models may optimize PCa detection and classification with regard to improved PCa risk assessment and avoidance of unnecessary prostate biopsies.

Mobile application-based Seoul National University Prostate Cancer Risk Calculator: development, validation, and comparative analysis with two Western risk calculators in Korean men

Objectives

We developed a mobile application-based Seoul National University Prostate Cancer Risk Calculator (SNUPC-RC) that predicts the probability of prostate cancer (PC) at the initial prostate biopsy in a Korean cohort. Additionally, the application was validated and subjected to head-to-head comparisons with internet-based Western risk calculators in a validation cohort. Here, we describe its development and validation.

Patients and Methods

As a retrospective study, consecutive men who underwent initial prostate biopsy with more than 12 cores at a tertiary center were included. In the development stage, 3,482 cases from May 2003 through November 2010 were analyzed. Clinical variables were evaluated, and the final prediction model was developed using the logistic regression model. In the validation stage, 1,112 cases from December 2010 through June 2012 were used. SNUPC-RC was compared with the European Randomized Study of Screening for PC Risk Calculator (ERSPC-RC) and the Prostate Cancer Prevention Trial Risk Calculator (PCPT-RC). The predictive accuracy was assessed using the area under the receiver operating characteristic curve (AUC). The clinical value was evaluated using decision curve analysis.

Results

PC was diagnosed in 1,240 (35.6%) and 417 (37.5%) men in the development and validation cohorts, respectively. Age, prostate-specific antigen level, prostate size, and abnormality on digital rectal examination or transrectal ultrasonography were significant factors of PC and were included in the final model. The predictive accuracy in the development cohort was 0.786. In the validation cohort, AUC was significantly higher for the SNUPC-RC (0.811) than for ERSPC-RC (0.768, p<0.001) and PCPT-RC (0.704, p<0.001). Decision curve analysis also showed higher net benefits with SNUPC-RC than with the other calculators.

Conclusions

SNUPC-RC has a higher predictive accuracy and clinical benefit than Western risk calculators. Furthermore, it is easy to use because it is available as a mobile application for smart devices.

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.

Cost-effectiveness of magnetic resonance (MR) imaging and MR-guided targeted biopsy versus systematic transrectal ultrasound-guided biopsy in diagnosing prostate cancer: a modelling study from a health care perspective

BACKGROUND

The current diagnostic strategy using transrectal ultrasound-guided biopsy (TRUSGB) raises concerns regarding overdiagnosis and overtreatment of prostate cancer (PCa). Interest in integrating multiparametric magnetic resonance imaging (MRI) and magnetic resonance-guided biopsy (MRGB) into the diagnostic pathway to reduce overdiagnosis and improve grading is gaining ground, but it remains uncertain whether this image-based strategy is cost-effective.

OBJECTIVE

To determine the cost-effectiveness of multiparametric MRI and MRGB compared with TRUSGB.

DESIGN, SETTING, AND PARTICIPANTS

A combined decision tree and Markov model for men with elevated prostate-specific antigen (>4 ng/ml) was developed. Input data were derived from systematic literature searches, meta-analyses, and expert opinion.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Quality-adjusted life years (QALYs) and health care costs of both strategies were modelled over 10 yr after initial suspicion of PCa. Probabilistic and threshold analyses were performed to assess uncertainty.

RESULTS AND LIMITATIONS

Despite uncertainty around the presented cost-effectiveness estimates, our results suggest that the MRI strategy is cost-effective compared with the standard of care. Expected costs per patient were € 2423 for the MRI strategy and € 2392 for the TRUSGB strategy. Corresponding QALYs were higher for the MRI strategy (7.00 versus 6.90), resulting in an incremental cost-effectiveness ratio of € 323 per QALY. Threshold analysis revealed that MRI is cost-effective when sensitivity of MRGB is ≥ 20%. The probability that the MRI strategy is cost-effective is around 80% at willingness to pay thresholds higher than € 2000 per QALY.

CONCLUSIONS

Total costs of the MRI strategy are almost equal with the standard of care, while reduction of overdiagnosis and overtreatment with the MRI strategy leads to an improvement in quality of life.

PATIENT SUMMARY

We compared costs and quality of life (QoL) of the standard "blind" diagnostic technique with an image-based technique for men with suspicion of prostate cancer. Our results suggest that costs were comparable, with higher QoL for the image-based technique.

Biomarkers in prostate cancer surveillance and screening: past, present, and future

The use of biomarkers for prostate cancer (PCa) screening, detection, and prognostication have revolutionized the diagnosis and management of the disease. Current clinical practice has been driven largely by the utilization of prostate-specific antigen (PSA). The lack of specificity of PSA for PCa has led to both unnecessary biopsies and overdiagnosis of indolent cancers. The recent controversial recommendation by the United States Preventive Services Task Force against PCa screening has highlighted the need for novel clinically useful biomarkers. We review the literature on PCa biomarkers in serum, urine, and tissue. While these markers show promise, none seems poised to replace PSA, but rather may augment it. Further validation and consideration of how these novel markers improve clinical outcome is necessary. The discovery of new genetic markers shows promise in stratifying men with aggressive PCa.