A novel nomogram combined PIRADS v2 and neutrophil-to-lymphocyte ratio to predict the risk of clinically significant prostate cancer in men with PSA < 10 ng/ml at first biopsy

ObjectiveTo determine whether Prostate Imaging-Reporting and Data System version 2 (PIRADS v2) and neutrophil-to-lymphocyte ratio(NLR) improve the detection of clinically significant prostate cancer(csCaP) in men with prostate-specific antigen (PSA) <10 ng/ml at first biopsy.MethodsUnivariable and multivariable binary logistic regression analysis were used to screen for independent risk factors of csCaP. The multivariable model based on the risk factors was to build the nomogram predicting csCaP and assessed by receiver operator characteristic curve analysis, calibration plot, and decision curve analysis.ResultsThis retrospective study included 335 men with PSA < 10 ng/ml who underwent initial biopsy. A total of 78 (23.3%) men had csCaP. The nomogram was built based on the multivariable model including age, digital rectal examination, free prostate-specific antigen, PIRADS v2, and NLR. It had high area under the curve of 0.876 and was well calibrated in internal validation. Decision curve analysis also demonstrated that it would improve the prediction of csCaP.ConclusionPIRADS v2 and NLR improve the detection of csCaP in men with PSA < 10 ng/ml at first biopsy. Due to lack of external validation, relatively small cohort and homogenous population, the study has several limitations. Despite of this, the nomogram based on our study is a promising tool for patients to understand their risk of csCaP and for urologists to make clinical decisions.     

External validation and comparison of magnetic resonance imaging-based predictive models for clinically significant prostate cancer

PurposeSeveral multiparametric magnetic resonance imaging (mpMRI)-based models have been developed with significant improvements in diagnostic accuracy for clinically significant prostate cancer (csCaP), but lack proper external validation. We therefore sought to externally validate and compare all published mpMRI-based csCaP risk prediction models in an independent Asian population.Patients and MethodsA total of 449 men undergoing combined transperineal fusion-targeted/systematic prostate biopsy at our specialist center between 2015 to 2019 were retrospectively analyzed. csCaP was defined as lesions with ISUP (International Society of Urological Pathology) grade group ≥2. The performance of 6 mpMRI-based risk models (MRI-ERSPC-3/4, Distler, Radtke, Mehralivand, van Leeuwen and He) were evaluated in terms of discrimination, calibration and clinical utility, using area under the receiver operating characteristic curve (AUC), calibration curves and decision curve analyses.ResultsA total of 202 (45%) subjects were diagnosed with csCaP. All models demonstrated excellent accuracy with AUCs ranging from 0.75 to 0.86, and most significantly outperformed mpMRI PIRADSv2.0 (Prostate Imaging Reporting and Data System version 2.0) alone. The models by Mehralivand and He showed good calibration to our validation population, with respective intercepts of -0.08 and -0.84. All models were nevertheless recalibrated to the csCaP prevalence in our population for analysis. Decision curve analysis showed that above a threshold probability of 10%, all mpMRI-based models demonstrated superior net benefit compared to mpMRI PIRADSv2.0 or a biopsy-all-men strategy. The van Leeuwen model had the greatest net benefit, avoiding 39% of unnecessary biopsies while missing only 4% of csCaP, at a threshold probability of 15%.ConclusionsThe mpMRI-based risk models demonstrate excellent discrimination and clinical utility and are easy to apply in practice, suggesting that individualized risk-based approaches can be considered over mpMRI alone to avoid unnecessary biopsies.     

Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer

Study Type – Prognosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? Nomograms are available that combine clinical and biopsy findings to predict the probability of pathologically insignificant prostate cancer in patients with clinically low‐risk disease. Based on data from patients with Gleason score 6, clinical stage ≤ T2a and PSA <20 ng/ml, our group developed the first nomogram models for predicting insignificant prostate cancer that incorporated clinical data, detailed biopsy data and findings from MRI or MRI/MRSI (BJU Int. 2007;99(4):786–93). When tested retrospectively, these MR models performed significantly better than standard clinical models with and without detailed biopsy data. We prospectively validated the previously published MR‐based nomogram models in a population of patients with Gleason score 6, clinical stage ≤ T2a and PSA <10 ng/ml. Based on data from this same population, we also developed two new models for predicting insignificant prostate cancer that combine MR findings and clinical data without detailed biopsy data. Upon initial testing, the new MR models performed significantly better than a clinical model lacking detailed biopsy data.

OBJECTIVES

• ? To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results.
• ? We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data. Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low‐risk disease who are choosing between active surveillance and definitive therapy.

PATIENTS AND METHODS

• ? In total, 181 low‐risk PCa patients (clinical stage T1c–T2a, prostate‐specific antigen level <10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery.
• ? For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa).
• ? Insignificant PCa was defined on surgical pathology.
• ? There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+. Prediction accuracy was assessed using areas under receiver–operator characteristic curves.

RESULTS

• ? At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients.
• ? For both readers, all magnetic resonance models performed significantly better than the base clinical model (P ≤ 0.05 for all) and similarly to the more comprehensive clinical model.

CONCLUSIONS

• ? Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated.
• ? All MR‐inclusive models performed significantly better than the base clinical model.

     

Prediction of clinically significant prostate cancer after negative prostate biopsy: The current value of microscopic findings

ObjectiveTo assess the current ability of atypical small acinar proliferation (ASAP), multifocal high-grade prostatic intraepithelial neoplasia (mHGPIN), HGPIN with atypia (PINATYP) and other non-malignant lesions to predict clinically significant prostate cancer (csPCa) in repeat prostate biopsies.MethodsThis retrospective study analyzed 377 repeat prostate biopsies, carried out between 2.014 and 2.017, and excluding those with previous PCa or 5-alpha reductase inhibitors treatment. ASAP, mHGPIN, PINATYP, prostatic atrophy, prostatic hyperplastic atrophy, proliferative inflammatory atrophy (PIA), chronic prostatitis, acute prostatitis, or granulomatous prostatitis, were prospectively reported after 12-core transrectal ultrasound (TRUS) systematic negative previous biopsies. 3T-multiparametric magnetic resonance imaging (mpMRI) was performed previous repeat biopsies. At least 2-core TRUS targeted biopsies of Prostate Imaging-Reporting and Data Systemv2 lesions ≥3, and/or 12-core TRUS systematic biopsy were performed in repeat prostate biopsies. The main outcome measurements were csPCa detection, which was defined when the International Society of Uro-Pathology group grade >1 and avoided biopsies. After logistic regression analysis the most efficient model was selected, nomogram was designed with internal validation, and clinical utility was analyzed.ResultsNormal benign tissue alone was present in less than 2% of previous negative biopsies. mHGPIN (39.7%), ASAP (4.3%) and PINATYP (3.7%) failed to predict csPCa risk in repeat biopsies. The finding of PIA (38.2%) associated with a decreased the risk of csPCa with an Odd ratio of 0.54 (95% confidence interval: 0.31–0.95), P= 0.031. The area under the curve, to predict csPCa, of mpMRI was 0.736, increasing up to 0.860 (95% confidence internal:0.82–0.90) when PSA density, age, digital rectal examination, and differential PSA between biopsies and PIA finding were integrated in a predictive model. At 6% threshold, more than 20% of repeat prostate biopsies were saved without missing csPCa.ConclusionCurrently, mHGPIN in negative prostate biopsy seems not able to predict the risk of future csPCa. The low incidence of ASAP and PINATYP, in our series, did not allow us to draw conclusions. PIA finding associated with a reduced risk of csPCa, and it could be integrated in a useful based-mpMRI predictive nomogram.     

Reducing unnecessary biopsies while detecting clinically significant prostate cancer including cribriform growth with the ERSPC Rotterdam risk calculator and 4Kscore

Introduction

The use of risk calculators predicting clinically significant prostate cancer (csCaP) on biopsy reduces unnecessary biopsies and overdiagnosis of indolent disease compared to a Prostate Specific Antigen (PSA) strategy. Updating these tools using more specific outcome measures and contemporary predictors could potentially lead to further reductions. Our objective was to assess clinical impact of the 4 kallikrein (4K) score, the Rotterdam Prostate Cancer Risk Calculator (RPCRC), and the combination of both for predicting csCaP based on the latest International Society of Urological Pathology grading system and cribriform growth pattern.

Using prostate-specific antigen screening and nomograms to assess risk and predict outcomes in the management of prostate cancer

We review the role of prostate-specific antigen (PSA) and the importance of patient education in the management of prostate cancer, based on discussions held at a European symposium on managing prostate cancer. Although PSA is the most widely used serum marker for detecting prostate cancer and for monitoring treatment responses, its use as a diagnostic marker is controversial due to concerns of over-diagnosis and low specificity. PSA isoforms, as well as PSA doubling time, might improve the specificity for earlier prostate cancer detection and can be used as surrogate markers for treatment efficacy. Patients can differ considerably in the importance they place on health-related quality of life aspects and fear of cancer progression. Consequently, there needs to be active, educated discussion of risk and outcomes between physicians and patients. Risk assessment tools, e.g. validated nomograms, enable clinicians to improve their decision analysis and form the basis for subsequent discussion of treatment options between the physician and patient, thereby enabling informed consent and appropriate decision-making.     

Use of nomograms to predict the risk of disease recurrence after definitive local therapy for prostate cancer

The generally indolent nature of prostate cancer, as well as the impact that treatment can have on quality of life (QOL) and cancer control, makes the decision analysis difficult for patients facing the task of selecting a treatment for clinically localized disease. Instruments to aid patients and their physicians in this decision analysis are needed. Nomograms are instruments that predict outcomes using specific clinical parameters. Nomograms use algorithms that incorporate several variables to calculate the predicted probability that a patient will achieve a particular clinical end point. Nomograms tend to outperform both clinical experts and predictive models using methods of risk grouping. We briefly outline the uses and limitations of nomograms, principles of nomogram construction, and the available models for predicting the progression-free probability after local definitive therapy with radical prostatectomy, external-beam radiotherapy, or brachytherapy. There is a need for additional nomograms that predict outcomes after salvage therapy, as well other clinical end points, including QOL-adjusted survival.     

Biomarkers for detection of clinically significant prostate cancer: contemporary clinical data and future directions

Use of serum prostate-specific antigen (PSA) testing for early detection of prostate cancer appears to reduce cancer-specific mortality. Due to the limited specificity of PSA for clinically significant [Grade Group (GG) ≥2] cancer, however, screening carries substantial risks, including frequent unnecessary prostate biopsies and overdetection of non-aggressive cancers. To that end, serum and urine biomarkers with improved specificity for GG ≥2 cancer have been proposed for clinical use following PSA. In the current article, we present clinical validation data for five such biomarkers: PHI, 4Kscore, SelectMDx, ExoDx, and MPS. For all studies, we specify the study population (overall biopsy referral vs. pre-specified PSA ranges), previous biopsy status (biopsy-naïve vs. previous negative biopsy), and the proportion of subjects diagnosed with GG ≥2 cancer. Outcomes include test performance characteristics: sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Published data were used to compute the number of unnecessary biopsies avoided and number of GG ≥2 cancers missed if the biomarker had been used clinically to select for prostate biopsy. The evidence review is preceded by a primer on these and other clinically-relevant summary statistics.