Predicting Prostate Biopsy Research Articles

MRI-based nomogram for the prediction of prostate cancer diagnosis: A multi-centre validated patient–physician decision tool

Objective: To update and externally validate a magnetic resonance imaging (MRI)-based nomogram for predicting prostate biopsy outcomes with a multi-centre cohort. Materials and methods: Prospective data from five UK-based centres were analysed. All men were biopsy naïve. Those with missing data, no MRI, or prostate-specific antigen (PSA) > 30 ng/mL were excluded. Logistic regression analysis was used to confirm predictors of prostate cancer outcomes including MRI-PIRADS (Prostate Imaging Reporting and Data System) score, PSA density, and age. Clinically significant disease was defined as International Society of Urological Pathology (ISUP) Grade Group ⩾ 2 (Gleason grade ⩾ 7). Biopsy strategy included transrectal and transperineal approaches. Nomograms were produced using logistic regression analysis results. Results: A total of 506 men were included in the analysis with median age 66 (interquartile range (IQR) = 60–69). Median PSA was 6.6 ng/mL (IQR = 4.72–9.26). PIRADS ⩾ 3 was reported in 387 (76.4%). Grade Group ⩾ 2 detection was 227 (44.9%) and 318 (62.8%) for any cancer. Performance of the MRI-based nomogram was an area under curve (AUC) of 0.84 (95% confidence interval (CI) = 0.81–0.88) for Grade Group ⩾ 2% and 0.85 (95% CI = 0.82–0.88) for any prostate cancer. Conclusion: We present external validation of a novel MRI-based nomogram in a multi-centre UK-based cohort, showing good discrimination in identifying men at high risk of having clinically significant disease. These findings support this risk calculator use in the prostate biopsy decision-making process. Level of evidence: 2c

Prospective Validation of Pentraxin-3 as a Novel Serum Biomarker to Predict the Risk of Prostate Cancer in Patients Scheduled for Prostate Biopsy.

Simple SummaryPTX3 could be a potential biomarker for PCa. To predict anyPCa and csPCa biomarker PTX3 was evaluated before PBx. Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. PTX3 outperformed other variables in predicting both anyPCa and csPCa. Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results.Purpose: To test and internally validate serum Pentraxin-3 (PTX3) levels as a potential PCa biomarker to predict prostate biopsy (PBx) results. Materials and Methods: Serum PSA and serum PTX3 were prospectively assessed in patients scheduled for PBx at our Institution due to increased serum PSA levels or abnormal digital rectal examination. Uni- and multivariable logistic regression analysis, area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA), were used to test the accuracy of serum PTX3 in predicting anyPCa and clinically significant PCa (csPCa) defined as Gleason Grade (GG) ≥ 2. Results: Among the 455 eligible patients, PCa was detected in 49% and csPCa in 25%. During univariate analysis, PTX3 outperformed other variables in predicting both anyPCa and csPCa. The addition of PTX3 to multivariable models based on standard clinical variables, significantly increased each model’s predictive accuracy for anyPCa (AUC from 0.73 to 0.82; p < 0.001) and csPCa (AUC from 0.79 to 0.83; p < 0.001). At DCA, PTX3, and PTX3, density showed higher net benefit than PSA and PSA density and increased the net benefit of multivariable models in deciding when to perform PBx. Conclusions: Serum PTX3 levels might be of clinical utility in predicting prostate biopsy results. Should our findings be confirmed, this novel reflex test could be used to reduce the number and burden of unnecessary prostate biopsies.

Combined Utility of 68Ga-Prostate-specific Membrane Antigen Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging in Predicting Prostate Biopsy Pathology

Background68Gallium-labelled prostate-specific membrane antigen positron emission tomography (68Ga-PSMA-11 PET) is a valuable staging tool, but its utility in characterising primary prostate cancer remains unclear. The maximum standardised uptake value (SUVmax) is a quantification measure of highest radiotracer uptake within PET-avid lesions. ObjectiveTo assess the utility of SUVmax in detecting clinically significant prostate cancer (csPCa) on biopsy alone and in combination with multiparametric magnetic resonance imaging (mpMRI). Design, setting, and participantsThis was a retrospective analysis of 200 men who underwent 68Ga-PSMA-11 PET/CT, mpMRI, and transperineal template prostate biopsy between 2016 and 2018. Outcome measurements and statistical analysisThe primary and secondary outcomes were detection of grade group (GG) 3–5 and GG 2–5 prostate cancer, respectively. We used the Mann-Whitney U test to compare SUVmax by GG, and calculated sensitivity and specificity for csPCa detection via 68Ga-PSMA-11 PET/CT, mpMRI, and both. Multivariable logistic regression analyses were used to identify predictors of csPCa on biopsy. Results and limitationsThe median SUVmax was greater for GG 3–5 tumours (6.40, interquartile range [IQR] 4.47–11.0) than for benign and GG 1–2 tumours (3.14, IQR 2.55–3.91; p < 0.001). The median SUVmax was greater for GG 3 (5.70, IQR 3.68–8.67) than for GG 2 (3.47, IQR 2.70–4.74; p < 0.001). For GG 3–5 disease, sensitivity was 86.5%, 95.9%, and 98.6%, and the negative predictive value (NPV) was 88.4%, 88.5%, and 93.3% using SUVmax ≥4, a Prostate Imaging-Reporting and Data System (PI-RADS) score of 3–5, and both, respectively. This combined model detected more GG 3–5 disease than mpMRI alone (98.6% vs 95.9%; p = 0.04). SUVmax was an independent predictor of csPCa for GG 3–5 disease only (odds ratio 1.27 per unit, 95% confidence interval 1.13–1.45). Our results are limited by the retrospective study design. ConclusionsGreater SUVmax on 68Ga-PSMA-11 PET/CT is associated with detection of GG 3–5 cancer on biopsy. The combination of PI-RADS score and SUVmax provides higher sensitivity and NPV than either alone. 68Ga-PSMA-11 PET/CT may be useful alongside mpMRI in improving risk stratification for localised disease. Patient summaryThe amount of a radioactive tracer taken up in the prostate during a type of scan called PET (positron emission tomography) can predict whether aggressive prostate cancer is likely to be found on biopsy. This may complement the more usual type of scan, MRI (magnetic resonance imaging), used to detect prostate cancer.

Adaptation of the prostate biopsy collaborative group risk calculator in patients with PSA less than 10ng/ml improves its performance.

The prostate biopsy collaborative group risk calculator (PBCGRC) is a newly developed risk estimator for predicting prostate biopsy outcomes. However, its clinical usefulness is still unknown within the so-called gray area of PSA values. This study aimed to determine whether updating the PBCGRC improves its predictive performance for predicting any-grade and high-grade (HG), defined as biopsy Gleason score ≥ 7, prostate cancer (PCa) in patients with prostate-specific antigen (PSA) less than 10ng/ml. The risk of any-grade and HGPCa was calculated using the PBCG risk calculation formulas updated by recalibration in the large, logistic recalibration and model revision. Predictive performances of the PBCGRC and the updated models were compared using discrimination, calibration, and clinical utility. Within the study sample of 526 patients, PCa was detected in 193 (36.7%), and 78 (14.8%) of them had HGPCa. According to the calibration curves, the PBCGRC overestimated the risk of PCa. Predictive accuracy of the revised model was higher [the area under the receiver-operating characteristic curve (AUCs), 65.4% and 70.2%] than that of the PBCGRC (AUCs, 60.4% and 64.3%) for any-grade and HGPCa. The net benefit was greater for model revision in comparison with the original model. The performance accuracy of PBCGRC for the prediction of any and HGPC in men undergoing prostate biopsy with PSA levels below 10ng/ml is suboptimal. The model revision resulted with significant improvement in model performance. However, external validation of the revised model is necessary before its routine use in clinical practice.

PD52-06 COMPUTATIONAL IMMUNOGENOMIC MODELING OF IMMUNOEVASIVE AGGRESSIVE PROSTATE CANCER

You have accessJournal of UrologyProstate Cancer: Markers II (PD52)1 Apr 2020PD52-06 COMPUTATIONAL IMMUNOGENOMIC MODELING OF IMMUNOEVASIVE AGGRESSIVE PROSTATE CANCER Howard Korman*, Kirk Wojno, Amin Kassis, Ricardo Henao, Jason Hafron, Kenneth Kernen, Geoffrey Erickson, Harry Stylli, Shrikant Mane, Angela Busta, Elyse Marriott, Rima Tinawi-Aljundi, Mathew Putzi, and Philip Kantoff Howard Korman*Howard Korman* More articles by this author , Kirk WojnoKirk Wojno More articles by this author , Amin KassisAmin Kassis More articles by this author , Ricardo HenaoRicardo Henao More articles by this author , Jason HafronJason Hafron More articles by this author , Kenneth KernenKenneth Kernen More articles by this author , Geoffrey EricksonGeoffrey Erickson More articles by this author , Harry StylliHarry Stylli More articles by this author , Shrikant ManeShrikant Mane More articles by this author , Angela BustaAngela Busta More articles by this author , Elyse MarriottElyse Marriott More articles by this author , Rima Tinawi-AljundiRima Tinawi-Aljundi More articles by this author , Mathew PutziMathew Putzi More articles by this author , and Philip KantoffPhilip Kantoff More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000954.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Peripheral blood CD14/CD2 RNA expression ratios have shown associations with prostate biopsy features. The objectives of this study are: 1) To develop a computational immunogenomic model of immunoevasive aggressive prostate cancer and validate this model on an independent data set, 2) determine if dimensionality reduction strategies are effective in minimizing model overfitting, 3) explore pathway associations and determine links with biopsy features, and 4) compare this optimized model with mainstream published models predicting biopsy features. METHODS: Peripheral blood samples from 696 urology patients between 40 and 75 years of age were obtained under IRB approval, prior to their scheduled prostate biopsy. Whole transcriptome RNA sequencing was performed on two purified cell types (CD14 and CD2) and RNA-sequencing expression levels were used to develop predictive models correlating to biopsy results using 50-fold cross validation. An independent validation set of 301 patients was used to confirm the results. Receiver Operating Characteristic (ROC) curves were created to compute p-values comparing the area under the curve (AUC) of individual predictive variables to the overall model, as well as to published mainstream models. RESULTS: In the discovery set the AUC of the ROC was 0.75 for positive versus negative biopsy results (BR), 0.82 for Gleason grade group (GG), 0.84 for number of positive biopsy needle cores (CP) of, 0.81 for maximum involvement of a core by tumor (MI), and 0.80 for the aggregate of the biopsy features (ABF). The degree of overfitting was estimated by the drop of performance seen in the independent validation set and was not statistically significant for any of the output variables. Our Model performs significantly better than mainstream models such as the PCPT and the PBCG risk calculators. Interesting pathway associations between tumor volume estimates and antigen processing, along with antigen peptide processing by MHC class II, are observed. CONCLUSIONS: A whole transcriptome RNA-seq algorithm based on clinical immunogenomic parameters determined by the analysis of peripheral blood samples from pre-biopsy prostate cancer patients predicts prostate biopsy features significantly better than established and widely used algorithms. The performance characteristics of this model have been validated on an independent data set with no significant overfitting or drop in performance noted. This study demonstrates the power of RNA expression patterns of pure populations of peripheral blood immune cells to predict prostate biopsy outcome. Source of Funding: OncoCell MDx © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e1091-e1092 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Howard Korman* More articles by this author Kirk Wojno More articles by this author Amin Kassis More articles by this author Ricardo Henao More articles by this author Jason Hafron More articles by this author Kenneth Kernen More articles by this author Geoffrey Erickson More articles by this author Harry Stylli More articles by this author Shrikant Mane More articles by this author Angela Busta More articles by this author Elyse Marriott More articles by this author Rima Tinawi-Aljundi More articles by this author Mathew Putzi More articles by this author Philip Kantoff More articles by this author Expand All Advertisement PDF downloadLoading ...

Detection of Prostate Cancer Antigen 3 and Prostate Cancer Susceptibility Candidate in Non-DRE Urine Improves Diagnosis of Prostate Cancer in Chinese Population.

Although prostate biopsy is the gold standard for the diagnosis of prostate cancer, it also leads to high incidence of negative biopsies and the diagnosis of clinically low-risk prostate cancer and the subsequent overtreatment. It remains an unmet need to discover new biomarkers in order to defer the unnecessary biopsies in clinical practice. In this study, we described a new method, LBXexo score, to measure the urine exosomal PCA3/PRAC expression from non-DRE urine as a noninvasive diagnosis to improve the detection rate in Chinese population with a low serum PSA level. First-voided urine samples were collected to isolate exosomes, and exosomal RNAs of PCA3 and PRAC were measured by quantitative reverse transcription PCR. A significant increase in exoPCA3/PRAC was observed in both any-grade and high-grade prostate cancer groups when compared with the biopsy-negative group. Receiver-operating characteristic curve analyses showed that the LBXexo score significantly improved diagnostic performance in predicting biopsy results, with AUCs of 0.723 (p=0.017) and 0.736 (p=0.038) for any-grade and high-grade (GS ≥ 7) prostate cancer, respectively. For high-grade cancer, LBXexo had the negative and positive predictive values of 100% and 27.59%, respectively, and could potentially avoid unnecessary biopsy. This is the first report in Chinese population that demonstrates the predictive value of the exosomal expression of PCA3 and PRAC derived from non-DRE urine in predicting prostate biopsy outcomes. It could be used in clinical practice to make a better informed biopsy decision and avoid unnecessary biopsies in Chinese population.

The importance of plasma arginine level and its downstream metabolites in diagnosing prostate cancer.

There is still no certain threshold value of prostate-specific antigen (PSA) for prostate cancer diagnosis. We aimed to investigate the predictive value of arginine and its metabolites for diagnosing prostate cancer in patients with PSA 4-10ng/ml and evaluate their usefulness as prognostic tumor markers. Seventy-eight patients with a mean age of 64.50 ± 5.49years were included in our prospective observational study between November 2016 and March 2017. They were divided into two equal groups according to the pathologic results of prostate biopsy (benign vs. malignant). Plasma arginine and ornithine levels were analyzed before biopsy by liquid chromatography-tandem mass spectrometry. ELISA was used for analyzing urinary diacetylspermine. In PSA-adjusted analysis, the malignant group had lower plasma arginine levels (p = 0.021) and arginine to ornithine ratio (AOR) (p = 0.010), but higher plasma ornithine levels (p = 0.012) and urinary diacetylspermine levels (p < 0.001) as compared with the benign group. While arginine (r = - 0.628, p < 0.001) and AOR (r = - 0.714, p < 0.001) were negatively correlated with D'Amico clinical classification (p < 0.001), ornithine (r = 0.659, p < 0.001) and diacetylspermine (r = 0.710, p < 0.001) were found to be positively correlated (p < 0.001). In multivariate analysis, ornithine [OR 3.264, 95% CI (1.045-10.196), p = 0.042] and diacetylspermine [OR 6.982, 95% CI (2.403-20.290), p < 0.001] were found to be more significant in detection of prostate cancer. Plasma arginine, ornithine, AOR and urinary diacetylspermine levels may be used as molecular markers to predict prostate biopsy outcomes in patients with PSA 4-10ng/ml. But according to our results, the use of ornithine and diacethylspermine prior to biopsy seems to be the most cost-effective diagnostic strategy.

9 - The diagnostic value of PSA density in predicting prostate biopsy outcomes

9 - The diagnostic value of PSA density in predicting prostate biopsy outcomes

Prostate Health Index (PHI) improves prostate cancer detection at initial biopsy in Taiwanese men with PSA 4–10 ng/mL

In this study, we aimed to validate the Prostate Health Index (PHI) for the detection of prostate cancer (PCa). We prospectively enrolled patients aged 50–75 years with a serum prostate specific antigen (PSA) level of 4–10 ng/mL undergoing transrectal biopsy of the prostate between April 2016 and May 2017. The primary outcome was the diagnostic performance of various PSA derivatives (total PSA, free PSA, %fPSA, p2PSA, %p2PSA, and PHI) to predict PCa. The secondary outcome was comparisons of PSA derivatives between patients with a Gleason score (GS) ≤6 and GS ≥7. PCa was diagnosed in 36 of 154 (23.4%) patients, and 26 (16.9%) had a GS ≥7. The areas under the receiver operating characteristic curves were significantly greater in %p 2PSA and PHI than in PSA (0.76 vs. 0.57, p = 0.015 and 0.77 vs. 0.57, p = 0.004, respectively). Patients with a GS ≥7PCa had marginally higher %p2PSA and PHI than those with a GS of 6 (17.8 vs. 12.73, p = 0.06; 46.58 vs. 31.55, p = 0.05). At a PHI cutoff value of 29.6, the sensitivity and specificity were 77.8% and 67.8% in detecting PCa, respectively. In addition, 57.1% of the patients avoided an unnecessary biopsy, while three patients (1.9%) with GS 7 PCa were missed. In conclusion, the ability of %p2PSA and PHI to predict prostate biopsy outcome was better than that of PSA and %fPSA in the initial biopsy in Taiwanese men with serum PSA between 4 and 10 ng/mL.

Can Prostate Imaging Reporting and Data System Version 2 reduce unnecessary prostate biopsies in men with PSA levels of 4-10ng/ml?

To explore the value of Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) for predicting prostate biopsy results in patients with prostate specific antigen (PSA) levels of 4-10ng/ml. We retrospectively reviewed multi-parameter magnetic resonance images from 528 patients with PSA levels of 4-10ng/ml who underwent transrectal ultrasound-guided prostate biopsies between May 2015 and May 2017. Among them, 137 were diagnosed with prostate cancer (PCa), and we further subdivided them according to pathological results into the significant PCa (S-PCa) and insignificant significant PCa (Ins-PCa) groups (121 cases were defined by surgical pathological specimen and 16 by biopsy). Age, PSA, percent free PSA, PSA density (PSAD), prostate volume (PV), and PI-RADS score were collected. Logistic regression analysis was performed to determine predictors of pathological results. Receiver operating characteristic curves were constructed to analyze the diagnostic value of PI-RADS v2 in PCa. Multivariate analysis indicated that age, PV, percent free PSA, and PI-RADS score were independent predictors of biopsy findings, while only PI-RADS score was an independent predictor of S-PCa (P < 0.05). The areas under the receiver operating characteristic curve for diagnosing PCa with respect to age, PV, percent free PSA, and PI-RADS score were 0.570, 0.430, 0.589 and 0.836, respectively. The area under the curve for diagnosing S-PCa with respect to PI-RADS score was 0.732. A PI-RADS score of 3 was the best cutoff for predicting PCa, and 4 was the best cutoff for predicting S-PCa. Thus, 92.8% of patients with PI-RADS scores of 1-2 would have avoided biopsy, but at the cost of missing 2.2% of the potential PCa cases. Similarly, 83.82% of patients with a PI-RADS score ≤ 3 would have avoided biopsy, but at the cost of missing 3.3% of the potential S-PCa cases. PI-RADS v2 could be used to reduce unnecessary prostate biopsies in patients with PSA levels of 4-10ng/ml.

Repeat multiparametric MRI in prostate cancer patients on active surveillance.

IntroductionThis study was conducted to describe the changes in repeat multiparametric MRI (mpMRI) occurring in prostate cancer (PCa) patients during active surveillance (AS), and to study possible associations between mpMRI-related parameters in predicting prostate biopsy (Bx) Gleason score (GS) upgrading >3+3 and protocol-based treatment change (TC).Materials and methodsThe study cohort consisted of 76 AS patients with GS 3+3 PCa and at least two consecutive mpMRIs of the prostate performed between 2006–2015. Patients were followed according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol and an additional mpMRI. The primary end points were GS upgrading (GU) (>3+3) in protocol-based Bxs and protocol-based TC.ResultsOut of 76 patients, 53 (69%) had progression (PIRADS upgrade, size increase or new lesion[s]), while 18 (24%) had radiologically stable disease, and 5 (7%) had regression (PIRADS or size decrease, disappearance of lesion[s]) in repeat mpMRIs during AS. PIRADS scores of 4–5 in the initial mpMRI were associated with GU (p = 0.008) and protocol-based TC (p = 0.009). Tumour progression on repeat mpMRIs was associated with TC (p = 0.045) but not with GU (p = 1.00). PIRADS scores of 4–5 predict GU (sensitivity 0.80 [95% confidence interval (CI); 0.51–0.95, specificity 0.62 [95% CI; 0.52–0.77]) with PPV and NPV values of 0.34 (95% CI; 0.21–0.55) and 0.93 (95% CI; 0.80–0.98), respectively.ConclusionmpMRI is a useful tool not only to select but also to monitor PCa patients on AS.

Prostate health index significantly reduced unnecessary prostate biopsies in patients with PSA 2-10 ng/mL and PSA >10 ng/mL: Results from a Multicenter Study in China.

The performance of prostate health index (phi) in predicting prostate biopsy outcomes has been well established for patients with prostate-specific antigen (PSA) values between 2 and 10 ng/mL. However, the performance of phi remains unknown in patients with PSA >10 ng/mL, the vast majority in Chinese biopsy patients. We aimed to assess the ability of phi to predict prostate cancer (PCa) and high-grade disease (Gleason Score ≥7) on biopsy in a Chinese population. This is a prospective, observational, multi-center study of consecutive patients who underwent a transrectal ultrasound guided prostate biopsy at four hospitals in Shanghai, China from August 2013 to December 2014. In the cohort of 1538 patients, the detection rate of PCa was 40.2%. phi had a significantly better predictive performance for PCa than total PSA (tPSA). The areas under the receiver operating characteristic curve (AUC) were 0.90 and 0.79 for phi and tPSA, respectively, P < 0.0001. A considerable proportion of patients in the cohort had PSAs >10 ng/mL (N = 838, 54.5%). The detection rates of PCa were 35.9% and 57.7% in patients with tPSA 10.1-20 and 20.1-50 ng/mL, respectively. The AUCs of phi (0.79 and 0.89, for these two groups, respectively) were also significantly higher than tPSA (0.57 and 0.63, respectively), both P < 0.0001. If a phi ≤35 was used as the cutoff, 599/1538 (39%) biopsies could have been avoided at a cost of missing small numbers of PCa patients: 49 (7.93%) PCa patients, including 18 (3.69%) high-grade tumors. Results from this study suggest that phi can be used to predict PCa and high-grade disease in Chinese men with high PSA levels (>10 ng/mL).

Urinary Biomarker Panel to Improve Accuracy in Predicting Prostate Biopsy Result in Chinese Men with PSA 4-10 ng/mL.

This study aims to evaluate the effectiveness and clinical performance of a panel of urinary biomarkers to diagnose prostate cancer (PCa) in Chinese men with PSA levels between 4 and 10 ng/mL. A total of 122 patients with PSA levels between 4 and 10 ng/mL who underwent consecutive prostate biopsy at three hospitals in China were recruited. First-catch urine samples were collected after an attentive prostate massage. Urinary mRNA levels were measured by quantitative real-time polymerase chain reaction (qRT-PCR). The predictive accuracy of these biomarkers and prediction models was assessed by the area under the curve (AUC) of the receiver-operating characteristic (ROC) curve. The diagnostic accuracy of PCA3, PSGR, and MALAT-1 was superior to that of PSA. PCA3 performed best, with an AUC of 0.734 (95% CI: 0.641, 0.828) followed by MALAT-1 with an AUC of 0.727 (95% CI: 0.625, 0.829) and PSGR with an AUC of 0.666 (95% CI: 0.575, 0.749). The diagnostic panel with age, prostate volume, % fPSA, PCA3 score, PSGR score, and MALAT-1 score yielded an AUC of 0.857 (95% CI: 0.780, 0.933). At a threshold probability of 20%, 47.2% of unnecessary biopsies may be avoided whereas only 6.2% of PCa cases may be missed. This urinary panel may improve the current diagnostic modality in Chinese men with PSA levels between 4 and 10 ng/mL.

Predicting Prostate Biopsy Outcomes: A Preliminary Investigation on Screening with Ultrahigh B-Value Diffusion-Weighted Imaging as an Innovative Diagnostic Biomarker.

BackgroundRoutine screening of prostate specific antigen (PSA) is no longer recommended because of a high rate of over-diagnosis of prostate cancer (PCa).ObjectiveTo evaluate the efficacy of diffusion-weighted magnetic resonance imaging (DW-MRI) for PCa detection, and to explore the clinical utility of ultrahigh b-value DW-MRI in predicting prostate biopsy outcomes.Methodology73 male patients were selected for the study. They underwent 3T MRI using T2WI conventional DW-MRI with b-value 1000 s/mm2, and ultrahigh b-value DW-MRI with b-values of 2000 s/mm2 and 3000 s/mm2. Two radiologists evaluated individual prostate gland images on a 5-point rating scale using PI-RADS, for the purpose of region-specific comparisons among modalities. Sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and likelihood ratios (LR) were investigated for each MRI modality. The area under the receiver operating characteristic (ROC) curve (AUC) was also calculated.ResultsResults showed the improved diagnostic value of ultrahigh b-value DWI-MRI for detection of PCa when compared to other b values and conventional MRI protocols. Sensitivity values for 3000 s/mm2 in both peripheral zone (PZ) and transition zone (TZ) were significantly higher than those observed with conventional DW-MRI—Specificity values for 3000 s/mm2 in the TZ were significantly higher than other b-value images, whereas specificity values using 3000 s/mm2 in the PZ were not significantly higher than 2000 s/mm2 images. PPV and NPV between 3000 s/mm2 and the other three modalities were significantly higher for both PZ and TZ images. The PLRs and NLRs of b-value 3000 s/mm2 DW-MRI in the PZ and TZ were also recorded. ROC analysis showed greater AUCs for the b value 3000 s/mm2 DWI than for the other three modalities.ConclusionsDW-MRI with a b-value of 3000 s/mm2 was found to be the most accurate and reliable MRI modality for PCa tumor detection and localization, particularly for TZ lesion discrimination. It may be stated that the b-value of 3000 s/mm2 is a novel, improved diagnostic biomarker with greater predictive accuracy for PCa prior to biopsy.

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

Background: Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa). To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa.Methods: Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies. We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine. Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7).Results: Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87. The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively. We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7). This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively. Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa. Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected.Conclusions: This model of a biomarker panel with algorithmic interpretation can be used as a “liquid biopsy” to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions.

Performance of the Prostate Health Index in predicting prostate biopsy outcomes among men with a negative digital rectal examination and transrectal ultrasonography.

The [-2]proPSA (p2PSA) and its derivatives, the p2PSA-to-free PSA ratio (%p2PSA), and the Prostate Health Index (PHI) have greatly improved discrimination between men with and without prostate cancer (PCa) in prostate biopsies. However, little is known about their performance in cases where a digital rectal examination (DRE) and transrectal ultrasonography (TRUS) are negative. A prospective cohort of 261 consecutive patients in China with negative DRE and TRUS were recruited and underwent prostate biopsies. A serum sample had collected before the biopsy was used to measure various PSA derivatives, including total prostate-specific antigen (tPSA), free PSA, and p2PSA. For each patient, the free-to-total PSA ratio (%fPSA), PSA density (PSAD), p2PSA-to-free PSA ratio (%p2PSA), and PHI were calculated. Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUC) and the biopsy rate at 91% sensitivity. The AUC scores within the entire cohort with respect to age, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI were 0.598, 0.751, 0.646, 0.789, 0.814, 0.808, and 0.853, respectively. PHI was the best predictor of prostate biopsy results, especially in patients with a tPSA of 10.1–20 ng ml−1. Compared with other markers, at a sensitivity of 91%, PHI was the most useful for determining which men did not need to undergo biopsy, thereby avoiding unnecessary procedures. The use of PHI could improve the accuracy of PCa detection by predicting prostate biopsy outcomes among men with a negative DRE and TRUS in China.

Urinary PCA3 to predict prostate cancer in a cohort of 1015 patients

Summary Aim To evaluate the performance of urinary PCA3 test to predict prostate biopsy outcome in a large French cohort. Patients and methods A urine sample was prospectively obtained from 1015 patients undergoing prostate biopsies to determine the PCA3 score. The predictive value of PCA3 was explored using receiver operating characteristic curve analysis (ROC), multivariable logistic regression analysis and decision curve analysis. Results The median PCA3 score was significantly higher in patients with positive biopsies. The PCA3 score AUC was 0.76 (0.73–0.79), significantly higher than that of PSA (0.55; 0.51–0.58). At the cutoff of 35, sensitivity was 68%, specificity 71%, positive and negative predictive values 67% and 71%, and accuracy 69%. Using multivariate analysis, PCA3 score appeared as an independent predictor of biopsy outcome and its addition to a base model including usual clinicobiological parameters resulted in a significant increase in predictive accuracy. At the cutoff of 20, about 1/2 of the ultimately unnecessary biopsies would have been avoided while ignoring 7% of cancers with Gleason score ≥ 7. PCA3 score did not correlate with Gleason score but did correlate with tumor volume (proportion of positive cores). Conclusion Urinary PCA3 is a useful test with high diagnostic performances for early prostate cancer diagnosis. Its correlation with cancer aggressiveness seems rather represented by a link to prostate volume than Gleason score. Level of evidence 5.

Test urinaire PCA3 et diagnostic du cancer prostatique : étude à partir de 1015 patients

To evaluate the performance of urinary PCA3 test to predict prostate biopsy outcome in a large French cohort. A urine sample was prospectively obtained in 1015 patients undergoing prostate biopsies to determine the PCA3 score. The predictive value of PCA3 was explored using receiver operating characteristic curve analysis (ROC), multivariable logistic regression analysis and decision curve analysis. The median PCA3 score was significantly higher in patients with positive biopsies. The PCA3 score AUC was 0.76 (0.73-0.79), significantly higher than that of PSA (0.55; 0.51-0.58). At the cut-off of 35, sensitivity was 68 %, specificity 71 %, positive and negative predictive values 67 % and 71 %, and accuracy 69 %. Using multivariate analysis, PCA3 score appeared as an independent predictor of biopsy outcome and its addition to a base model including usual clinico-biological parameters resulted in a significant increase in predictive accuracy. At the cut-off of 20, about 1/2 of the eventual useless biopsies would have been avoided while ignoring 7 % of cancers with Gleason score ≥ 7. PCA3 score did not correlate to Gleason score but correlated to tumor volume (proportion of invaded cores). Urinary PCA3 is a useful test with high diagnostic performance for early prostate cancer diagnosis. Its correlation with cancer aggressiveness seems rather represented by a link to prostate volume than Gleason score.

Prostate-specific antigen velocity is not better than total prostate-specific antigen in predicting prostate biopsy diagnosis

Objectives1.) Identify whether prostate-specific antigen velocity improves the ability to predict prostate biopsy diagnosis. 2.) Test whether there is an increase in the predictive capability of models when Gleason 7 prostate cancers are separated into a 3+4 and a 4+3 group. Design and methodsCalgary Laboratory Services' Clinical Laboratory Information System was searched for prostate biopsies reported between January 1, 2009 and December 31, 2013. Total prostate-specific antigen tests were recorded for each patient from January 1, 2007 to the most recent test before their recorded prostate biopsy. The data set was divided into the following three groups for comparison; benign, all prostate cancer and Gleason 7–10. The Gleason grade 7–10 group was further divided into 4+3 and 3+4 Gleason 7 prostate cancers. Prostate-specific antigen velocity was calculated using four different methods found in the literature. Receiver operator curves were used to assess operational characteristics of the tests. Results4622 men between the ages of 40–89 with a prostate biopsy were included for analysis. Combining prostate-specific antigen velocity with total prostate-specific antigen (AUC=0.570–0.712) resulted in small non-statistically significant changes to the area under the curve compared to the area under the curve of total prostate-specific antigen alone (AUC=0.572–0.699). There were marked increases in the area under curves when 3+4 and 4+3 Gleason 7 cancers were separated. ConclusionsProstate-specific antigen velocity does not add predictive value for prostate biopsy diagnosis. The clinical significance of the prostate specific antigen test can be improved by separating Gleason 7 prostate cancers into a 3+4 and 4+3 group.

Clinical utility of a novel urine-based gene fusion TTTY15-USP9Y in predicting prostate biopsy outcome

ObjectiveIn recent years, great effort has been made to explore new biomarkers for early detection of prostate cancer. Our previous study has demonstrated the high prevalence of TTTY15-USP9Y in prostate cancer samples from a Chinese population. Our aim was to evaluate the clinical utility of TTTY15-USP9Y in predicting the prostate biopsy outcome. Materials and methodsWe retrospectively examined the expression of TTTY15-USP9Y in 226 qualified urine sediment samples. Total RNA was extracted from the urine sediment by using TRIzol reagent, and complementary DNA was synthesized using TransPlex Complete Whole Transcriptome Amplification Kit (WTA2). Real-time quantitative polymerase chain reaction was performed to evaluate the expression of TTTY15-USP9Y and the prostate cancer–specific antigen (PSA) level. The TTTY15-USP9Y score was calculated as 2Ct(PSA)-Ct(TTTY15-USP9Y)× 1,000. ResultsThe TTTY15-USP9Y score was statistically significantly higher in men with positive biopsy outcome than in men with negative biopsy outcome (P<0.001). The area under the curve was 0.828 for the TTTY15-USP9Y score in the entire patient cohort. The TTTY15-USP9Y score׳s cutoff of 90.28 provided the optimal balance between sensitivity (84.0%) and specificity (77.5%). The combination of PSA level and the TTTY15-USP9Y score significantly improved the diagnostic performance of PSA level (P = 0.001). The TTTY15-USP9Y score alone was superior to PSA level, percent free PSA, and PSA density (serum PSA/prostate volume) in the subgroup of clinical interest (PSA level: 4–10ng/ml, gray zone). Univariable and multivariable logistic analyses indicated that TTTY15-USP9Y score, PSA level, age, and prostate volume were independent predictors of PCa. Adding the TTTY15-USP9Y score in the clinical base model (PSA level, age, and prostate volume) could bring a higher net benefit and reduce more unnecessary biopsies in the defined range of interest (10%–40% threshold probability). ConclusionIn conclusion, our study explored the potential utility of measuring the TTTY15-USP9Y score in post–digital rectal examination urine samples to predict biopsy outcome and provided the basis for the utility of this novel gene fusion in multicenter and large cohort studies.