Development of Improved Nomogram for Prediction of Outcome of Initial Prostate Biopsy Using Readily Available Clinical Information

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

68Ga-PSMA PET/CT-based multivariate model for highly accurate and noninvasive diagnosis of clinically significant prostate cancer in the PSA gray zone

BACKGROUND

The prostate-specific antigen (PSA) has been widely used in screening and early diagnosis of prostate cancer (PCa). However, in the PSA grey zone of 4-10 ng/ml, the sensitivity and specificity for diagnosing PCa are limited, resulting in considerable number of unnecessary and invasive prostate biopsies, which may lead to potential overdiagnosis and overtreatment. We aimed to predict clinically significant PCa (CSPCa) by combining the maximal standardized uptake value (SUVmax) based on 68Ga‑PSMA PET/CT and clinical indicators in men with gray zone PSA levels.

METHODS

81 patients with suspected PCa based on increased serum total PSA (TPSA) levels of 4 - 10 ng/mL who underwent transrectal ultrasound/magnetic resonance imaging (MRI)/PET fusion-guided biopsy were enrolled. Among them, patients confirmed by histopathology were divided into the CSPCa group and the non-CSPCa group, and data on PSA concentration, prostate volume (PV), PSA density (PSAD), free PSA (FPSA)/TPSA, Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) score, 68Ga-PSMA PET/CT imaging evaluation results and SUVmax were compared. Multivariate logistic regression analysis was performed to identify the independent predictors for CSPCa, thereby establishing a predictive model based on SUVmax that was evaluated by analyzing the receiver operating characteristic (ROC) curve and decision curve analysis.

RESULTS

Compared to non-CSPCa, CSPCa patients had smaller PVs (median, 31.40 mL), lower FPSA/TPSA (median, 0.12), larger PSADs (median, 0.21 ng/mL2) and higher PI-RADS scores (P < 0.05). The prediction model comprising 68Ga-PSMA PET/CT maximal standardized uptake value, PV and FPSA/TPSA had the highest AUC of 0.927 compared with that of other predictors alone (AUCs of 0.585 for PSA, 0.652 for mpMRI and 0.850 for 68Ga-PSMA PET/CT). The diagnostic sensitivity and specificity of the prediction model were 86.21% and 86.54%, respectively.

CONCLUSION

Given the low diagnostic accuracy of regular PSA tests, a new prediction model based on the 68Ga-PSMA PET/CT SUVmax, PV and FPSA/TPSA was developed and validated, and this model could provide a more satisfactory predictive accuracy for CSPCa. This study provides a noninvasive prediction model with high accuracy for the diagnosis of CSPCa in the PSA gray zone, thus may be better avoiding unnecessary biopsy procedures.

Ultrasound-based radiomics score for pre-biopsy prediction of prostate cancer to reduce unnecessary biopsies

BACKGROUND

Patients undergoing prostate biopsies (PBs) suffer from low positive rates and potential risk for complications. This study aimed to develop and validate an ultrasound (US)-based radiomics score for pre-biopsy prediction of prostate cancer (PCa) and subsequently reduce unnecessary PBs.

METHODS

Between December 2015 and March 2018, 196 patients undergoing initial transrectal ultrasound (TRUS)-guided PBs were retrospectively enrolled and randomly assigned to the training or validation cohort at a ratio of 7:3. A total of 1044 radiomics features were extracted from grayscale US images of each prostate nodule. After feature selection through the least absolute shrinkage and selection operator (LASSO) regression model, the radiomics score was developed from the training cohort. The prediction nomograms were developed using multivariate logistic regression analysis based on the radiomics score and clinical risk factors. The performance of the nomograms was assessed and compared in terms of discrimination, calibration, and clinical usefulness.

RESULTS

The radiomics score consisted of five selected features. Multivariate logistic regression analysis demonstrated that the radiomics score, age, total prostate-specific antigen (tPSA), and prostate volume were independent factors for prediction of PCa (all p < 0.05). The integrated nomogram incorporating the radiomics score and three clinical risk factors reached an area under the curve (AUC) of 0.835 (95% confidence interval [CI], 0.729-0.941), thereby outperforming the clinical nomogram which based on only clinical factors and yielded an AUC of 0.752 (95% CI, 0.618-0.886) (p = 0.04). Both nomograms showed good calibration. Decision curve analysis indicated that using the integrated nomogram would add more benefit than using the clinical nomogram.

CONCLUSION

The radiomics score was an independent factor for pre-biopsy prediction of PCa. Addition of the radiomics score to the clinical nomogram shows incremental prognostic value and may help clinicians make precise decisions to reduce unnecessary PBs.

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.

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

The aim of this study was to construct a nomogram for predicting prostate cancer (PCa) in patients with PSA ≤ 20 ng/mL at initial biopsy.The patients with PSA ≤ 20 ng/mL who underwent prostate biopsy were retrospectively included in this study. The nomogram was developed based on predictors for PCa, which were assessed by multivariable logistic regression analysis. The receiver operating characteristic curve, calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.This retrospective study included 691 patients, who were divided into training set (505 patients) and validation set (186 patients). The nomogram was developed based on the multivariable logistic regression model, including age, total PSA, free PSA, and prostate volume. It had a high area under the curve of 0.857, and was well verified in validation set. Calibration plots and DCA further validated its discrimination and potential clinical benefits. Applying the cut-off value of 15%, our nomogram would avoid 42.5% of unnecessary biopsies while miss only 4.4% of PCa patients.The nomogram provided high predictive accuracy for PCa in patients with PSA ≤ 20 ng/mL at initial biopsy, which could be used to avoid the unnecessary biopsies in clinical practice.

A non-randomized prospective study on the diagnostic performance of perineal prostatic biopsy, directed via diffusion nuclear resonance, in patients with suspected prostate cancer and previous negative transrectal prostate biopsy

BACKGROUND

A definition of the best strategy is necessary to optimize the follow-up of patients with previous negative transrectal guided ultrasound biopsy (TRUS-GB) and the persistence of raised prostate-specific antigen (PSA).The purpose of this study was to evaluate the prostate cancer (PCa) diagnostic rate of targeted transperineal ultrasound guided biopsy (TPUS-GB) with cognitive multiparametric magnetic resonance imaging (mpMRI) registration with concurrent systematic biopsy in patients with previous negative systematic TRUS-GB and persistently elevated PSA levels.

MATERIALS AND METHODS

In this prospective study conducted at the University Infanta Sofia Hospital from April 2016 to November 2017, patients with one previous negative systematic TRUS-GB and persistently high PSA levels were referred for mpMRI prostate scans. All patients underwent systematic TPUS-GB and those patients with suspicious findings on mpMRI scans, Pirads 3 and 4-5, underwent a subsequent cognitive guidance mpMRI-TPUS-GB.

RESULTS

In total, 71 patients were included in this study. Suspicious findings on mpMRI scans prior to TPUS-GB were found in 50 patients (70.4%). 16 patients were diagnosed with prostate cancer (22.5%), of whom 14 (87.5%) had a mpMRI scan with Pirads 3 or Pirads 4-5. Patients with Pirads 3, 4 or 5 showed negative results in almost all cores taken by concurrent systematic TPUS-GB.

CONCLUSIONS

Cognitive mpMRI-TPUS fusion biopsy is a useful tool to diagnose PCa in patients with previous negative prostate biopsy. The samples obtained from the suspicious areas in the mpMRI detect more cases of intermediate and high risk PCa compared to the samples obtained at random or from non-suspicious areas.

Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging/transrectal ultrasound fusion-derived data

Background

Urologists face a dilemma when deciding whether prostate biopsy is required for patients with prostate-specific antigen (PSA) levels in the grey zone (4 to 10 ng/mL).

Methods

We retrospectively analyzed data from consecutive patients with PSA levels in grey zone, who underwent targeted multiparametric magnetic resonance imaging (MP-MRI)/transrectal ultrasound (TRUS) fusion biopsy with elastography between November 2017 and December 2019 in our hospital. The patientse data including age, PSA, fPSA (free PSA), fPSA/PSA, PSA density (PSAD), prostate volume, elastography Q-analysis score (EQS), and prostate imaging-reporting and data system (PI-RADS) score were collected. The nomogram was built using logistic regression and the final cohort of patients was randomly divided into a training cohort (70%) and a validation cohort (30%) by R software. The models were evaluated by receiver operating characteristic curve (ROC) analysis and calibration curve analysis. The nomogram was constructed from the best model.

Results

The final study cohort consisted of 155 patients (training cohort, 109 patients; validation cohort, 46 patients) with PSA in the grey zone, of which 36 patients were pathologically diagnosed with PCa. The EQS model, -EQS model, +EQS model were built. The +EQS model that consisted of fPSA/PSA, EQS, and PI-RADS score had the best PCa diagnostic accuracy (development and validation, 0.783 and 0.781) and probability score (development and validation, 0.939 vs. 0.622). The new nomogram based on this model was constructed, in which fPSA/PSA ratio had the largest impact, followed by PI-RADS and EQS.

Conclusions

Elastography and pre-biopsy MP-MRI has clinical significance for patients with PSA in the grey zone. The new nomogram, which is based on pre biopsy data including serological analysis, PI-RADS score, and EQS, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Predictive factors of prostate cancer diagnosis with PSA 4.0-10.0 ng/ml in a multi-ethnic Asian population, Malaysia

OBJECTIVES

To identify the associated factors determining prostate cancer detection using transrectal ultrasound (TRUS)-guided prostate biopsy, within a multi-ethnic Malaysian population with prostate specific antigen (PSA) between 4.0 and 10.0 ng/ml.

METHODS

Study subjects included men with initial PSA between 4.0 and 10.0 ng/ml that have undergone 12-core TRUS-guided prostate biopsy between 2009 and 2016. The prostate cancer detection rate was calculated, while potential factors associated with detection were investigated via univariable and multivariable analysis.

RESULTS

A total of 617 men from a multi-ethnic background encompassing Chinese (63.5%), Malay (23.1%) and Indian (13.3%) were studied. The overall cancer detection rate was 14.3% (88/617), which included cancers detected at biopsy 1 (first biopsy), biopsy 2 (second biopsy with previous negative biopsy) and biopsy ≥ 3 (third or more biopsies with prior negative biopsies). Indian men displayed higher detection rate (23.2%) and increased risk of prostate cancer development (OR 1.85, 95% CI 1.03-3.32, p < 0.05), compared to their Malay (9.8%) and Chinese (14.0%) counterparts. Multivariable analysis revealed that ethnicity and PSA density (PSAD) are independent factors associated with overall prostate cancer detection rate. A unit increase of PSAD reflected an increase in PSA after controlling for prostate volume.

CONCLUSION

Prostate cancer detection in Malaysia is comparatively lower. Our study suggests that ethnicity and PSA density should be considered when recommending first or repeat TRUS-guided prostate biopsy for prostate cancer detection in a multi-ethnic Malaysian population.

Prediction of prostate cancer by deep learning with multilayer artificial neural network

INTRODUCTION

To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated.

METHODS

A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model.

RESULTS

With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6.

CONCLUSIONS

ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

Prediction of Prostate Cancer Risk Among Men Undergoing Combined MRI-targeted and Systematic Biopsy Using Novel Pre-biopsy Nomograms That Incorporate MRI Findings

OBJECTIVE

To develop nomograms that predict the probability of overall prostate cancer (PCa) and clinically significant PCa (Gleason ≥7) on magnetic resonance imaging (MRI)-targeted, and combined MRI-targeted and systematic, prostate biopsy.

MATERIALS AND METHODS

From June 2012 to August 2014, magnetic resonance imaging to ultrasound fusion-targeted prostate biopsy was performed on 464 men with suspicious regions identified on pre-biopsy 3T MRI along with systematic 12 core biopsy. Logistic regression modeling was used to evaluate predictors of overall and clinically significant PCa, and corresponding nomograms were generated for men who were not previously biopsied or had 1 or more prior negative biopsies. Models were created with 70% of a randomly selected training sample and bias-corrected using bootstrap resampling. The models were then validated with the remaining 30% testing sample pool.

RESULTS

A total of 459 patients were included for analysis (median age 66 years, prostate-specific antigen [PSA] 5.2 ng/mL, prostate volume 49 cc). Independent predictors of PCa on targeted and systematic prostate biopsy were PSA density, age, and MRI suspicion score. PCa probability nomograms were generated for each cohort using the predictors. Bias-corrected areas under the receiver-operating characteristic curves for overall and clinically significant PCa detection were 0.82 (0.78) and 0.91 (0.84) for men without prior biopsy and 0.76 (0.65) and 0.86 (0.87) for men with a prior negative biopsy in the training (testing) samples.

CONCLUSION

PSA density, age, and MRI suspicion score predict PCa on combined MRI-targeted and systematic biopsy. Our generated nomograms demonstrate high diagnostic accuracy and may further aid in the decision to perform biopsy in men with clinical suspicion of PCa.

Correlation between Chromosome 9p21 Locus Deletion and Prognosis in Clinically Localized Prostate Cancer

Background

Some studies have reported that deletions at chromosome arm 9p occur frequently and represent a critical step in carcinogenesis of some neoplasms. Our aim was to evaluate the deletion of locus 9p21 and chromosomes 3, 7 and 17 in localized prostate cancer (PC) and correlate these alterations with prognostic factors and biochemical recurrence after surgery.

Methods

We retrospectively evaluated surgical specimens from 111 patients with localized PC who underwent radical prostatectomy. Biochemical recurrence was defined as a prostate-specific antigen (PSA) >0.2 ng/mL and the mean postoperative follow-up was 123 months. The deletions were evaluated using fluorescence in situ hybridization with centromeric and locus-specific probes in a tissue microarray containing 2 samples from each patient. We correlated the occurrence of any deletion with pathological stage, Gleason score, ISUP grade group, PSA and biochemical recurrence.

Results

We observed a loss of any probe in only 8 patients (7.2%). The most common deletion was the loss of locus 9p21, which occurred in 6.4% of cases. Deletions of chromosomes 3, 7 and 17 were observed in 2.3%, 1.2% and 1.8% patients, respectively. There was no correlation between chromosome loss and Gleason score, ISUP, PSA or stage. Biochemical recurrence occurred in 83% cases involving 9p21 deletions. Loss of 9p21 locus was significantly associated with time to recurrence (p = 0.038).

Conclusions

We found low rates of deletion in chromosomes 3, 7 and 17 and 9p21 locus. We observed that 9p21 locus deletion was associated with worse prognosis in localized PC treated by radical prostatectomy.

Integrated predictive model for prostatic cancer using clinical, laboratory and ultrasound data

OBJECTIVE

to develop a predictive model to estimate the probability of prostate cancer prior to biopsy.

METHODS

from September 2009 to January 2014, 445 men underwent prostate biopsy in a radiology service. We excluded from the study patients with diseases that could compromise the data analysis, who had undergone prostatic resection or used 5-alpha-reductase inhibitors. Thus, we selected 412 patients. Variables included in the model were age, prostate specific antigen (PSA), digital rectal examination, prostate volume and abnormal sonographic findings. We constructed Receiver Operating Characteristic (ROC) curves and calculated the areas under the curve, as well as the model's Positive Predictive Value (PPV) .

RESULTS

of the 412 men, 155 (37.62%) had prostate cancer (PC). The mean age was 63.8 years and the median PSA was 7.22ng/ml. In addition, 21.6% and 20.6% of patients had abnormalities on digital rectal examination and image suggestive of cancer by ultrasound, respectively. The median prostate volume and PSA density were 45.15cm3 and 0.15ng/ml/cm3, respectively. Univariate and multivariate analyses showed that only five studied risk factors are predictors of PC in the study (p<0.05). The PSA density was excluded from the model (p=0.314). The area under the ROC curve for PC prediction was 0.86. The PPV was 48.08% for 95%sensitivity and 52.37% for 90% sensitivity.

CONCLUSION

the results indicate that clinical, laboratory and ultrasound data, besides easily obtained, can better stratify the risk of patients undergoing prostate biopsy.

Evaluation of expressed prostatic secretion and serum using surface-enhanced Raman spectroscopy for the noninvasive detection of prostate cancer, a preliminary study

Surface-enhanced Raman spectroscopy (SERS) involving expressed prostatic secretion (EPS) and serum was investigated; the objective was to determine if this approach could distinguish prostate cancer from benign prostatic hyperplasia. A total of 120 SERS spectra for EPS and 96 spectra for serum were gathered from patients within a prospective contemporary biopsy cohort. Significant differences in spectra between prostate cancer and benign prostatic hyperplasia were tentatively assigned to component changes in EPS and serum samples. Principal component analysis and linear discriminate analysis were utilized to evaluate the spectral data for EPS and serum, to build diagnostic algorithms. The leave-one-out cross-validation method was used to validate the diagnostic algorithms; it revealed diagnostic sensitivities of 75% and 60%, specificities of 75% and 76.5%, and accuracies of 75% and 68% for EPS and serum, respectively. The results suggest that EPS and serum SERS analysis could be a potential tool for prostate cancer detection.

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.

External validation of a PCA-3-based nomogram for predicting prostate cancer and high-grade cancer on initial prostate biopsy

INTRODUCTION

The aim of this study was to externally validate a previously developed PCA3-based nomogram for the prediction of prostate cancer (PCa) and high-grade (intermediate and/or high-grade) prostate cancer (HGPCa) at the time of initial prostate biopsy.

METHODS

A retrospective review was performed on a cohort of 336 men from a large urban academic medical center. All men had serum PSA <20 ng/ml and underwent initial transrectal ultrasound-guided prostate biopsy with at least 10 cores sampling for suspicious exam and/or elevated PSA. Covariates were collected for the nomogram and included age, ethnicity, family history (FH) of PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and abnormal finding on digital rectal exam (DRE). These variables were used to test the accuracy (concordance index) and calibration of a previously published PCA3 nomogram.

RESULTS

Biopsy confirms PCa and HGPCa in 51.0% and 30.4% of validation patients, respectively. This differed from the original cohort in that it had significantly more PCa and HGPCA (51% vs. 44%, P = 0.019; and 30.4% vs. 19.1%, P < 0.001). Despite the differences in PCa detection the concordance index was 75% and 77% for overall PCa and HGPCa, respectively. Calibration for overall PCa was good.

CONCLUSIONS

This represents the first external validation of a PCA3-based prostate cancer predictive nomogram in a North American population. Prostate 76:1019-1023, 2016. © 2016 Wiley Periodicals, Inc.

PCA3-based nomogram for predicting prostate cancer and high grade cancer on initial transrectal guided biopsy

BACKGROUND

To develop a validated prostate cancer antigen 3 (PCA3) based nomogram that predicts likelihood of overall prostate cancer (PCa) and intermediate/high grade prostate cancer (HGPCa) in men pursuing initial transrectal prostate biopsy (TRUS-PBx).

METHODS

Data were collected on 3,675 men with serum prostate specific antigen level (PSA) ≤ 20 ng/ml who underwent initial prostate biopsy with at least 10 cores sampling at time of the biopsy. Two logistic regression models were constructed to predict overall PCa and HGPCa incorporating age, race, family history (FH) of PCa, PSA at diagnosis, PCA3, total prostate volume (TPV), and digital rectal exam (DRE).

RESULTS

One thousand six hundred twenty (44%) patients had biopsy confirmed PCa with 701 men (19.1%) showing HGPCa. Statistically significant predictors of overall PCa were age (P < 0.0001, OR. 1.51), PSA at diagnosis (P < 0.0001, OR.1.95), PCA3 (P < 0.0001, OR.3.06), TPV (P < 0.0001, OR.0.47), FH (P = 0.003, OR.1.32), and abnormal DRE (P = 0.001, OR. 1.32). While for HGPCa, predictors were age (P < 0.0001, OR.1.77), PSA (P < 0.0001, OR.2.73), PCA3 (P < 0.0001, OR.2.26), TPV (P < 0.0001, OR.0.4), and DRE (P < 0.0001, OR.1.53). Two nomograms were reconstructed for predicted overall PCa probability at time of initial biopsy with a concordance index of 0.742 (Fig. 1), and HGPCa with a concordance index of 0.768 (Fig. 2).

CONCLUSIONS

Our internally validated initial biopsy PCA3 based nomogram is reconstructed based on a large dataset. The c-index indicates high predictive accuracy, especially for high grade PCa and improves the ability to predict biopsy outcomes.

Prevalence and Risk Factors of Prostate Cancer in Chinese Men with PSA 4-10 ng/mL Who Underwent TRUS-Guided Prostate Biopsy: The Utilization of PAMD Score

Purpose. To elucidate the characteristics and risk factors for positive biopsy outcomes in Chinese patients with prostate specific antigen (PSA) 4–10 ng/mL and develop a risk-stratification score model. Methods. The data of 345 patients who underwent transrectal ultrasound-guided prostate biopsy between 2011 and 2013 was retrospectively analyzed. Digital rectal examination (DRE), prostate volume (PV), magnetic resonance imaging (MRI), and smoking status were also collected. Positive biopsy outcomes were defined as prostate cancer (PCa) and high grade PCa (HGPCa, Gleason Score ≥ 7). Results. The median PSA was 7.15 (IQR 5.91–8.45) ng/mL. Overall 138 patients (40.0%) were shown to have PCa, including 100 patients (29.0%) with HGPCa. Smaller PV, elder age, MRI results, and positive DRE were proved to be predictive factors for positive biopsy outcomes in both univariate and multivariate analysis. We developed a “PAMD” score which combined the four factors to categorize patients into three risk groups, and the model performed good predictive sensitivity and specificity. Conclusion. The prevalence of prostate cancer in Chinese patients with PSA 4–10 ng/mL was 40%, including 29% patients with high grade disease. DRE, age, MRI, and PV were predictive factors for positive biopsy outcomes, and the PAMD score model could be utilized for risk-stratification and decision-making.

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy

Background

PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups.

Methods

Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference’s nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves.

Results

We detect 28 % of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20 % at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95 %:0.68–0.79) and 0.786 for HGPCa (C.I.95 %:0.71–0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40 % could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31 % for the threshold probability of 40 %.

Conclusions

PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40 % should be counseled to undergo an IBx if opportunistic screening is required.

The development of nomograms for stratification of men at risk of prostate cancer prior to prostate biopsy

A main limitation of early prostate cancer (PCa) detection due to elevated PSA levels is caused by the low specificity of PSA, which is associated with a high proportion of men detected with nonmalignant findings at first or subsequent prostate biopsy (PBX). Multivariate prediction models, such as nomograms, have been developed, providing a more accurate method to prospectively determine the risk of a positive PBX. Combining established clinical risk factors with novel diagnostic markers of PCa appears promising to further improve predictive accuracy estimates. Ideally, these nomograms should be capable of identifying PCa at PBX without missing men with high-grade PCa, and preventing a significant proportion of men without, or with insignificant, PCa from undergoing PBX. The intention is to reduce disease morbidity and mortality by detecting significant PCa at an early stage, and at the same time to avoid overdiagnosis as well as overintervention.

Pentraxin 3: a novel biomarker for predicting progression from prostatic inflammation to prostate cancer

Pentraxin-3 (PTX3) is a member of the pentraxin family of innate immune regulators, which includes C-reactive protein (CRP). PTX3 has been implicated in angiogenesis, proliferation, and immune escape in cancer. In the present study, we evaluated PTX3 tissue expression and serum concentration as a biomarker to discriminate prostatic inflammation and benign prostatic hyperplasia (BPH) from prostate cancer, and to determine whether PTX3 status may predict progression from BPH to prostate cancer. We analyzed 40 patients with biopsy-proven BPH who underwent a second prostate biopsy 12 to 36 months later when they were diagnosed with prostate cancer or inflammation/BPH (n = 20 patients each group). Furthermore, we evaluated PTX3 serum concentrations in an independent set of patients with biopsy-proven inflammation/BPH (n = 61) and prostate cancer (n = 56). We found reduced PTX3 tissue expression in patients with prostatic inflammation/BPH compared with patients who developed prostate cancer. In the latter group, there was an increase in PTX3 tissue expression between the first and second prostate biopsy. PTX3 serum levels were also higher in patients with prostate cancer than in patients with inflammation/BPH. In contrast, there was no difference in serum PSA or CRP levels in these two groups. ROC curve analysis confirmed the reliability of PTX3 serum levels in predicting prostate cancer development, identifying a cutoff value of 3.25 ng/mL with a sensitivity and a specificity of 89.3% and 88.5%, respectively. In summary, our results encourage further evaluation of PTX3 as a tissue biopsy and blood-borne biomarker to discriminate BPH from prostate cancer.

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.

Development, validation and comparison of two nomograms predicting prostate cancer at initial 12-core biopsy

AIM

Our aim was to establish, validate and compare two nomograms in an Iranian population for the first time using clinical, laboratory and transrectal ultrasonography (TRUS) findings for predicting prostate cancer at initial biopsy.

METHODS

Data were collected on a total of 916 men referred for an initial prostate biopsy in our center in a 7-year period. Variables analyzed included age, prostate-specific antigen (PSA), free/total PSA (%fPSA), digital rectal examination (DRE) findings, prostate volume (PV) and presence of hypoechoic lesion on TRUS. Univariate logistic regression models were fitted to test cancer predictors. Two multivariate logistic regression models were fitted to create nomograms. Both models were internally validated. Calibration of nomograms was assessed graphically. The area under the receiver operating characteristic curve (AUC) was calculated as a scale of discrimination and predictive accuracy and also used to compare models.

RESULTS

Prostate cancer was detected in 221/669 (33%) men. Based on univariate logistic regression, all of variables except DRE were significant predictors of prostate cancer, with highest AUC for PV (AUC 0.696, 95% CI 0.653-0.738).AUC of nomogram with and without TRUS findings and PSA alone were 0.791, 0.721 and 0.624, respectively. In internal validation, both nomograms had acceptable calibration plots.

CONCLUSION

Our nomogram based on age, DRE, PSA, %fPSA and TRUS finding was significantly more accurate in predicting initial prostate biopsy outcome in men.

Nomogram for prediction of prostate cancer with serum prostate specific antigen less than 10 ng/mL

Although prostate-specific antigen (PSA) is a very useful screening tool, prostate biopsy is still necessary to confirm prostate cancer (PCA). However, it is reported that PSA is associated with a high false-positive rate and prostate biopsy also has various procedure-related complications. Therefore, the authors have devised a nomogram, which can be used to estimate the risk of PCA, using available clinical data for men with a serum PSA less than 10 ng/mL. Prostate biopsies were obtained from 2,139 patients from January 1998 to March 2011. Of them, 1,171 patients with a serum PSA less than 10 ng/mL were only included in this study. Patient age, PSA, free PSA, prostate volume, PSA density and percent free PSA ratio were analyzed. Among 1,171 patients, 255 patients (21.8%) were diagnosed as PCA. Multivariate analyses showed that patient age, prostate volume, PSA and percent free PSA had statistically significant relationships with PCA (P < 0.05) and were used as nomogram predictor variables. The area under the (ROC) curve for all factors in a model predicting PCA was 0.759 (95% CI, 0.716-0.803).

Correlation between beta1 integrin expression and prognosis in clinically localized prostate cancer

Integrins are transmembrane glycoprotein receptors that regulate cell-matrix interactions, thus functioning as sensors from the environment. They also act as cell adhesion molecules that are responsible for the maintenance of the normal epithelial phenotype. Some studies have reported a correlation between carcinogenesis and changes in integrin expression, especially β1 integrin, however its role in prostate cancer (PC) is unclear. The aim of our study was to evaluate the expression of β1 integrin in localized PC and to correlate the pattern of expression with recurrence after surgical treatment. Methods For this case-control study, we retrospectively selected surgical specimens from 111 patients with localized PC who underwent radical prostatectomy. Recurrence was defined as a PSA level exceeding 0.2 ng/mL after surgery, and the median follow-up was 123 months. Integrin expression was evaluated by immunohistochemistry in a tissue microarray containing two samples from each tumor. We employed a semiquantitative analysis and considered a case as positive when the expression was strong and diffusely present.

RESULTS

There was a loss of 11 cases during the tissue micro array assembling. β1 expression was positive in 79 of the 100 evaluated cases (79%). The univariate and multivariate analyses showed that the negative expression of β1 integrin was associated with biochemical recurrence (p = 0.047) and time to recurrence after radical prostatectomy (p = 0.023). When β1 was negative, the odds ratio for recurrence was 2.78 times higher than that observed in the positive cases [OR = 2.78, p = 0.047, IC 95% (1.01-7.66)].

CONCLUSIONS

The loss of β1 integrin immune expression was correlated with biochemical recurrence in patients treated with radical prostatectomy for localized PC.

Testing for the recurrent HOXB13 G84E germline mutation in men with clinical indications for prostate biopsy

PURPOSE

The G84E variant of HOXB13 was recently found to be associated with a significantly increased risk of prostate cancer in a case control study. We estimated the prevalence of this mutation in a clinical population of men at risk for prostate cancer who were scheduled to undergo prostate biopsy.

MATERIALS AND METHODS

We prospectively collected clinical information and DNA samples from men who underwent diagnostic prostate biopsy between June 2005 and October 2011. We genotyped samples for HOXB13 G84E using the MassARRAY® system. We determined the prevalence of the G84E variant in the overall cohort, among patients with a positive family history and among men age 55 years or younger.

RESULTS

A total of 1,175 subjects underwent biopsy, of whom 948 had a DNA sample for analysis. The G84E variant was detected in 4 patients (prevalence 0.42%, 95% CI 0.12-1.08), of whom 3 had prostate cancer on biopsy. None of 301 patients with a positive family history (prevalence 0.00%, 95% CI 0.00-1.22) and 1 of 226 patients age 55 years or younger tested positive (prevalence 0.44%, 95% CI 0.01-2.44).

CONCLUSIONS

The HOXB13 G84E variant is rare in this cohort, even among those with a positive family history. Our findings question the utility of testing for this variant among unselected men presenting for a diagnostic prostate biopsy.

Outcomes and trends of prostate biopsy for prostate cancer in Chinese men from 2003 to 2011

Background

Prostate-specific antigen (PSA) screening is growing in popularity in China, but its impact on biopsy characteristics and outcomes are poorly understood.

Objective

Our objective was to characterize prostate biopsy outcomes and trends in Chinese men over a 10-year period, since the increasing use of PSA tests.

Methods

All men (n = 1,650) who underwent prostate biopsy for PCa at Huashan Hospital, Shanghai, China from 2003–2011 were evaluated. Demographic and clinical information was collected for each patient, including age, digital rectal examination (DRE), transrectal ultrasound (prostate volume and nodule), total prostate-specific antigen (tPSA) levels and free PSA ratio (fPSA/tPSA) prior to biopsy. Prostate biopsy was performed using six cores before October 2007 or ten cores thereafter. Logistic regression and multivariate analysis were used to evaluate our data.

Results

The overall positive rate of prostate biopsy for PCa was 47% and the rate decreased significantly over the years from 74% in 2003 to 33% in 2011 (P-trend = 0.004) . Age at diagnosis was slightly increased (P-trend = 0.04) while fPSA/tPSA was significantly decreased (P-trend = 1.11×10-5). A statistically significant trend was not observed for tPSA levels, prostate volume, or proportion of positive nodule. The model including multiple demographic and clinical variables (i.e., age, DRE, tPSA, fPSA/tPSA and transrectal ultrasound results) (AUC = 0.93) statistically outperformed models that included only PSA (AUC = 0.85) or fPSA/tPSA (AUC = 0.66) to predict PCa risks (P<0.05). Similar results were observed in a subgroup of men whose tPSA levels were lower than 20 ng/mL (AUC = 0.87, vs. AUC of tPSA  = 0.62, P<0.05).

Conclusions

Detection rates of PCa and high-grade PCa among men that underwent prostate biopsy at the institution has decreased significantly in the past 10 years, likely due to increasing use of PSA tests. Predictive performance of demographic and clinical variables of PCa was excellent. These variables should be used in clinics to determine the need for prostate biopsy.

Investigation of optimal prostate biopsy schemes for Chinese patients with different clinical characteristics

PURPOSE

To investigate the optimal schemes of prostate biopsy according to prostate volume (PV), age and transrectal ultrasound (TRUS) status in Chinese men.

METHODS

923 consecutive patients who underwent initial TRUS-guided systematic 12-core prostate biopsy (12PBx) were enrolled in this study. The 12PBx was obtained by overlapping of conventional sextant, lateral base, mid-gland of peripheral zone and apex. Each sample was individually marked and inked before fixation. Patients were divided into 8 subgroups on the basis of independent risk factors investigated using logistic regression model. Subsequently, 12PBx was defined as self-control for the analysis of biopsy schemes (6-, 8- and 10PBx) on individual core basis. The prostate cancer detection rates (CDRs) of 6-, 8-, 10- and 12PBx were compared for each individual subgroup.

RESULTS

The 12PBx detected 253 (27.4%) cases of prostate cancer (PCa), of which 67.2, 47.1 and 61.3% were located in the base, mid-gland and apex, respectively. Multivariate analysis indicated that age, TRUS status and PV were independent risk factors for PCa detection. CDR increased with increasing biopsy cores. However, for patients with age ≥65 years, positive TRUS and PV <38.5 cm(3), CDR of 8PBx (30.6%) was similar to 10PBx (32.2%) and 12PBx (32.2%); for patients with age ≥65 years, negative TRUS and PV <38.5 cm(3) or ones with age ≥65 years, positive TRUS and PV ≥38.5 cm(3), 10PBx was as effective as 12PBx in detecting PCa (27.8, 27.5 vs. 28.9, 29.3%, respectively).

CONCLUSION

Age, TRUS status and PV were independent risk factors for PCa detection. Traditional sextant biopsy is not recommended. 8-, 10-, or 12PBx as an individual biopsy scheme might be adopted according to these risk factors for Chinese patients.

Initial prostate biopsy: development and internal validation of a biopsy-specific nomogram based on the prostate cancer antigen 3 assay

BACKGROUND

Urinary prostate cancer antigen 3 (PCA3) assay in combination with established clinical risk factors improves the identification of men at risk of harboring prostate cancer (PCa) at initial biopsy (IBX).

OBJECTIVE

To develop and validate internally the first IBX-specific PCA3-based nomogram that allows an individual assessment of a man's risk of harboring any PCa and high-grade PCa (HGPCa).

DESIGN, SETTING, AND PARTICIPANTS

Clinical and biopsy data including urinary PCA3 score of 692 referred IBX men at risk of PCa were collected within two prospective multi-institutional studies.

INTERVENTION

IBX (≥ 10 biopsy cores) with standard risk factor assessment including prebiopsy urinary PCA3 measurement.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

PCA3 assay cut-off thresholds were investigated. Regression coefficients of logistic risk factor analyses were used to construct specific sets of PCA3-based nomograms to predict any PCa and HGPCa at IBX. Accuracy estimates for the presence of any PCa and HGPCa were quantified using area under the curve of the receiver operator characteristic analysis and compared with a clinical model. Bootstrap resamples were used for internal validation. Decision curve analyses quantified the clinical net benefit related to the novel PCA3-based IBX nomogram versus the clinical model.

RESULTS AND LIMITATIONS

Any PCa and HGPCa were diagnosed in 46% (n=318) and 20% (n=137), respectively. Age, prostate-specific antigen, digital rectal examination, prostate volume, and PCA3 were independent predictors of PCa at IBX (all p<0.001). The PCA3-based IBX nomograms significantly outperformed the clinical models without PCA3 (all p<0.001). Accuracy was increased by 4.5-7.1% related to PCA3 inclusion. When applying nomogram-derived PCa probability thresholds ≤ 30%, only a few patients with HGPCa (≤ 2%) will be missed while avoiding up to 55% of unnecessary biopsies. External validation of the PCA3-based IBX-specific nomogram is warranted.

CONCLUSIONS

The internally validated PCA3-based IBX-specific nomogram outperforms a clinical prediction model without PCA3 for the prediction of any PCa, leading to the avoidance of unnecessary biopsies while missing only a few cases of HGPCa. Our findings support the concepts of a combination of novel markers with established clinical risk factors and the superiority of decision tools that are specific to a clinical scenario.