Independent Predictor Of Prostate Cancer Research Articles

Investigation of the Effectiveness of Prostate Biopsy Density in Predicting Prostate Cancer Under Cognitive and Systematic Biopsy in Multi-Parametric Magnetic Resonance Imaging (mpMRI).

To explore the effectiveness of prostate biopsy density in predicting prostate cancer under cognitive and systematic biopsy mode in multi-parametric magnetic resonance imaging (mpMRI). A retrospective analysis was conducted on clinical data of 204 patients who were suspected of having prostate cancer with prostate-specific antigen (PSA) levels less than 50 ng mL-1 and underwent cognitive and systematic biopsy through the perineal approach in our hospital from 2022 to 2023. Univariate and multivariate logistic regression analyses were used to evaluate the odds ratios of prostate biopsy density and relevant clinical indicators. Logistic regression analysis was performed to establish a predictive model combining indicators with predictive value. The predictive value of each indicator and the new model was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). The detection rate of prostate cancer in the study population was 32.35%. Multivariate analysis showed that age, PSAD, PI-RADS 2.1 score, and prostate biopsy density were independent predictors of prostate cancer. The ROC curve analysis revealed an AUC of 0.707 (95% CI 0.625-0.790) for biopsy density, with a cutoff value of approximately 0.22 needle mL-1. The best predictive model consisted of age, PSAD, PI-RADS 2.1 score, and biopsy density, with an AUC of 0.857. Biopsy density is associated with the detection of prostate cancer, with a critical value of 0.22 needle mL-1. Combining biopsy density with other clinical indicators can significantly improve the ability to predict prostate cancer and avoid unnecessary prostate biopsy cores.

Diagnostic Efficiency of Pan-Immune-Inflammation Value to Predict Prostate Cancer in Patients with Prostate-Specific Antigen between 4 and 20 ng/mL.

To evaluate the predictive value of the pan-immune-inflammation value (PIV) and other systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), for prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) in patients with a prostate-specific antigen (PSA) value between 4 and 20 ng/mL. The clinical data of 319 eligible patients who underwent prostate biopsies in our hospital from August 2019 to June 2022 were retrospectively analyzed. CSPCa was defined as a "Gleason grade group of ≥2". A univariable logistic regression analysis and multivariable logistic regression analysis were conducted to analyze the association between the PIV, SII, MLR, and PCa/CSPCa. For the inflammatory indicators included in the multivariable logistic regression analysis, we constructed models by combining the separate inflammatory indicator and other significant predictors and compared the area under the curve (AUC). A nomogram based on the PIV for PCa was developed. We included 148 PCa patients (including 127 CSPCa patients) and 171 non-PCa patients in total. The patients with PCa were older, had higher MLR, SII, PIV, and total PSA (TPSA) values, consumed more alcohol, and had lower free/total PSA (f/T) values than the other patients. Compared with the non-CSPCa group, the CSPCa group had higher BMI, MLR, PIV, TPSA values, consumed more alcohol, and had lower f/T values. The univariable regression analysis showed that drinking history, higher MLR, PIV, and TPSA values, and lower f/T values were independent predictors of PCa and CSPCa. The AUC of the PIV in the multivariable logistic regression model was higher than those of the MLR and SII. In addition, the diagnostic value of the PIV + PSA for PCa was better than the PSA value. However, the diagnostic value for CSPCa was not significantly different from that of using PSA alone, while the AUC of the PIV + PSA was higher than the individual indicator of the PSA value. Our study suggests that for the patients who were diagnosed with PSA values between 4 and 20 ng/mL, the PIV and MLR are potential indicators for predicting PCa and CSPCa. In addition, our study indicates that the new inflammatory index PIV has clinical value in the diagnosis of PCa and CSPCa.

Clinical and prostate multiparametric magnetic resonance imaging findings as predictors of general and clinically significant prostate cancer risk: A retrospective single-center study.

To evaluate the predictive values of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2), prostate-specific antigen (PSA) level, PSA density (PSAD), digital rectal examination findings, and prostate volume, individually and in combination, for the detection of prostate cancer (PCa) in biopsy-naive patients. We retrospectively analyzed 630 patients who underwent transrectal systematic prostate biopsy following prostate multiparametric magnetic resonance imaging. A standard 12-core biopsy procedure was performed. Univariate and multivariate analyses were performed to determine the significant predictors of clinically significant cancer but not PCa. The median age, PSA level, and PSAD were 70 years, 8.6 ng/mL, and 0.18 ng/mL/mL, respectively. A total of 374 (59.4%) of 630 patients were biopsy-positive for PCa, and 241 (64.4%) of 374 were diagnosed with clinically significant PCa (csPCa). The PI-RADS v2 score and PSAD were independent predictors of PCa and csPCa. The PI-RADS v2 score of 5 regardless of the PSAD value, or PI-RADS v2 score of 4 plus a PSAD of <0.3 ng/mL/mL, was associated with the highest csPCa detection rate (36.1%-82.1%). Instead, the PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL yielded the lowest risk of csPCa. The combination of the PI-RADS v2 score and PSAD could prove to be a helpful and reliable diagnostic tool before performing prostate biopsies. Patients with a PI-RADS v2 score of <3 and PSAD of <0.3 ng/mL/mL could potentially avoid a prostate biopsy.

Assessing the Role of High-resolution Microultrasound Among Naïve Patients with Negative Multiparametric Magnetic Resonance Imaging and a Persistently High Suspicion of Prostate Cancer

BackgroundMultiparametric magnetic resonance imaging (mpMRI) is an invaluable diagnostic tool in the decision-making for prostate biopsies (PBx). However, a non-negligible proportion of patients with negative MRI (nMRI) may still harbour prostate cancer (PCa). ObjectiveTo assess whether microultrasound (micro-US) can help in substratifying the presence of PCa and clinically significant PCa (csPCa; ie, any Gleason score ≥7 PCa) in patients with nMRI despite a persistently high clinical suspicion of PCa. Design, setting, and participantsA total of 125 biopsy-naïve patients who underwent micro-US–guided PBx with the ExactVu system for a persistently high suspicion of PCa despite nMRI were prospectively enrolled. InterventionThe Prostate Risk Identification using micro-US (PRI-MUS) protocol was used to identify suspicious areas; PBx included targeted sampling of PRI-MUS ≥3 areas and systematic sampling. Outcome measurements and statistical analysisThe primary endpoint was the assessment of micro-US diagnostic accuracy in detecting csPCa. Secondary endpoints included determining the proportion of patients with nMRI who may avoid PBx after micro-US or transrectal US, presence of cribriform and intraductal patterns on biopsy core examination, predictors of csPCa in patients presenting with nMRI, and comparing micro-US–targeted and systematic PBx in identifying csPCa. Results and limitationsConsidering csPCa detection rate, micro-US showed optimal sensitivity and negative predictive value (respectively, 97.1% and 96.4%), while specificity and positive predictive value were 29.7% and 34.0%, respectively. Twenty-eight (22.4%) patients with a negative micro-US examination could have avoided PBx with one (2.9%) missed csPCa. Cribriform and intraductal patterns were found in 14 (41.2%) and four (11.8%) of csPCa patients, respectively. In multivariable logistic regression models, positive micro-US, age, digital rectal examination, and prostate-specific antigen density ≥0.15 emerged as independent predictors of PCa. Targeted and systematic sampling identified 33 (97.1%) and 26 (76.5%) csPCa cases, respectively. The main limitation of the current study is represented by its retrospective single-centre nature on an operator-dependent technology. ConclusionsMicro-US represents a valuable tool to rule out the presence of csPCa among patients with a persistent clinical suspicion despite nMRI. Patient summaryAccording to our results, microultrasound (micro-US) may represent an effective tool for the diagnosis of clinically significant prostate cancer in patients with negative magnetic resonance imaging (nMRI), providing high sensitivity and negative predictive value. Further randomised studies are needed to confirm the potential role of micro-US in the diagnostic pathway of patients with a persistent suspicion of prostate cancer despite nMRI.

The Detection of Prostate Cancer with Magnetic Resonance Imaging-Targeted Prostate Biopsies is Superior with the Transperineal vs the Transrectal Approach. A European Association of Urology-Young Academic Urologists Prostate Cancer Working Group Multi-Institutional Study.

Our aim was to evaluate whether transperineal (TP) MRI-targeted prostate biopsy (TBx) may improve the detection of clinically significant prostate cancer (csPCa), defined as International Society of Urological Pathology ≥2, in comparison to transrectal (TR) TBx. A multicenter retrospective cohort study comprising patients who underwent MRI-guided prostate biopsy was conducted. To address possible benefits of TP-TBx in the detection of prostate cancer (PCa) and csPCa, a cohort of patients undergoing TP-TBx were compared to patients undergoing TR-TBx. Multivariable logistic regression analyses were performed to assess predictors of PCa and csPCa detection. Overall, 1,936 and 3,305 patients who underwent TR-TBx vs TP-TBx at 10 referral centers were enrolled. The rate of PCa and csPCa diagnosed was higher for TP-TBx vs TR-TBx (64.0% vs 50%, p <0.01 and 49% vs 35%, p <0.01). At multivariable analysis adjusted for age, biopsy naïve/repeated biopsy, cT stage, Prostate Imaging-Reporting and Data System®, prostate volume, PSA, and number of biopsy cores targeted, TP-TBx was an independent predictor of PCa (odds ratio [OR] 1.37, 95% CI 1.08-1.72) and csPCa (1.19, 95% CI 1.12-1.50). When considering the approach according to the site of the index lesion, TP-TBx had a significantly higher likelihood than TR-TBx to detect csPCa in the apex (OR 4.81, 95% CI 1.03-6.27), transition/central zone (OR 2.67, 95% CI 1.42-5.00), and anterior zone (OR 5.62, 95% CI 1.74-8.13). The use of TP-TBx allows a better cancer grade definition and PCa risk assessment. This has important implication in the decision-making process and in patient counseling for further therapies.

PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone.

PurposeTo evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa).MethodsA total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa.ResultsA total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186).ConclusionThe combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.

Exploration of the Value of Combined UA, IL-6, and fPSA/tPSA in the Diagnosis of Prostate Cancer.

Objective To investigate the differences in uric acid (UA), interleukin-6 (IL-6), and free prostatic-specific antigen (fPSA)/total prostatic-specific antigen (tPSA) (F/T) between patients with and without prostate cancer (PCa) in order to discover the value of the three indicators in improving PCa diagnostic accuracy. Methods Patients with pathologically diagnosed PCa (PCa group, n = 25), patients with other benign prostate diseases (benign group, n = 25), and men who underwent normal physical examination (control group, n = 25) at the First Affiliated Hospital of Guangzhou University of Chinese Medicine between October 2020 and January 2021 were included. The serum UA, IL-6, and F/T levels of participants in the three groups were measured, and the measured data were statistically analyzed. Results There were statistically significant differences in IL-6 and F/T among the three groups (all P < 0.05), but there were no statistically significant differences in UA (P > 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) for the three indicators was, respectively, as follows: PCa group-benign group 0.5416, 0.6776, and 0.6832; PCa group-control group 0.5432, 0.9536, and 0.9887; and benign group-control group 0.5000, 0.8784, and 0.9456. Logistic regression analysis indicated that IL-6 and F/T were independent predictors of PCa, with AUCs of 0.6776 and 0.6832, respectively, and a combined accuracy of 72.0%. Conclusion These results suggest that IL-6 and F/T have a good detection effect for PCa screening. Compared with the detection of F/T alone, the combined detection of IL-6 and F/T can improve the diagnosis rate of PCa to a certain extent, providing effective guidance for the clinical diagnosis and treatment of patients. The value of UA needs to be further studied, and its feasibility in the diagnosis of PCa needs to be further explored.

Construction and Validation of a Clinical Predictive Nomogram for Improving the Cancer Detection of Prostate Naive Biopsy Based on Chinese Multicenter Clinical Data.

ObjectivesProstate biopsy is a common approach for the diagnosis of prostate cancer (PCa) in patients with suspicious PCa. In order to increase the detection rate of prostate naive biopsy, we constructed two effective nomograms for predicting the diagnosis of PCa and clinically significant PCa (csPCa) prior to biopsy.Materials and MethodsThe data of 1,428 patients who underwent prostate biopsy in three Chinese medical centers from January 2018 to June 2021 were used to conduct this retrospective study. The KD cohort, which consisted of 701 patients, was used for model construction and internal validation; the DF cohort, which consisted of 385 patients, and the ZD cohort, which consisted of 342 patients, were used for external validation. Independent predictors were selected by univariate and multivariate binary logistic regression analysis and adopted for establishing the predictive nomogram. The apparent performance of the model was evaluated via internal validation and geographically external validation. For assessing the clinical utility of our model, decision curve analysis was also performed.ResultsThe results of univariate and multivariate logistic regression analysis showed prostate-specific antigen density (PSAD) (P<0.001, OR:2.102, 95%CI:1.687-2.620) and prostate imaging-reporting and data system (PI-RADS) grade (P<0.001, OR:4.528, 95%CI:2.752-7.453) were independent predictors of PCa before biopsy. Therefore, a nomogram composed of PSAD and PI-RADS grade was constructed. Internal validation in the developed cohort showed that the nomogram had good discrimination (AUC=0.804), and the calibration curve indicated that the predicted incidence was consistent with the observed incidence of PCa; the brier score was 0.172. External validation was performed in the DF and ZD cohorts. The AUC values were 0.884 and 0.882, in the DF and ZD cohorts, respectively. Calibration curves elucidated greatly predicted the accuracy of PCa in the two validation cohorts; the brier scores were 0.129 in the DF cohort and 0.131 in the ZD cohort. Decision curve analysis showed that our model can add net benefits for patients. A separated predicted model for csPCa was also established and validated. The apparent performance of our nomogram for PCa was also assessed in three different PSA groups, and the results were as good as we expected.ConclusionsIn this study, we put forward two simple and convenient clinical predictive models comprised of PSAD and PI-RADS grade with excellent reproducibility and generalizability. They provide a novel calculator for the prediction of the diagnosis of an individual patient with suspicious PCa.

Serum Levels of the Cytokine TWEAK Are Associated with Metabolic Status in Patients with Prostate Cancer and Modulate Cancer Cell Lipid Metabolism In Vitro

Simple SummaryTWEAK is an inflammatory cytokine related to prostate cancer (PCa) progression that exerts its effects by engaging its cognate receptor Fn14. A soluble form of TWEAK (sTWEAK) has been detected in the PCa microenvironment. Altered levels of circulating sTWEAK are associated with aberrant glucose metabolism. We show that reduced serum levels of sTWEAK are associated with the metabolic status in patients with PCa and that the treatment of PC-3 cells with sTWEAK enhances the expression of genes related to lipid, but not to glucose, metabolism. sTWEAK also increases the lipid uptake and lipid accumulation in PC-3 cells. We corroborated that the observed effects were due to TWEAK/Fn14 engagement by silencing Fn14 expression, which attenuated the aberrant gene and protein expression. Additionally, we observed that the phosphorylation of ERK1/2 and AKT (ser473) were required for TWEAK/Fn14 actions. Thus, the contribution of the sTWEAK/Fn14 axis on PCa metabolism supports its potential as a therapeutic target for PCa.Soluble TWEAK (sTWEAK) has been proposed as a prognostic biomarker of prostate cancer (PCa). We found that reduced serum levels of sTWEAK, together with higher levels of prostate-specific antigen and a higher HOMA-IR index, are independent predictors of PCa. We also showed that sTWEAK stimulus failed to alter the expression of glucose transporter genes (SLC2A4 and SLC2A1), but significantly reduced the expression of glucose metabolism-related genes (PFK, HK1 and PDK4) in PCa cells. The sTWEAK stimulation of PC-3 cells significantly increased the expression of the genes related to lipogenesis (ACACA and FASN), lipolysis (CPT1A and PNPLA2), lipid transport (FABP4 and CD36) and lipid regulation (SREBP-1 and PPARG) and increased the lipid uptake. Silencing the TWEAK receptor (Fn14) in PC-3 cells confirmed the observed lipid metabolic effects, as shown by the downregulation of ACACA, FASN, CPT1A, PNPLA2, FABP4, CD36, SREBP-1 and PPARG expression, which was paralleled by a reduction of FASN, CPT1A and FABP4 protein expression. Specific-signaling inhibitor assays show that ERK1/2 and AKT (ser473) phosphorylation can regulate lipid metabolism-related genes in PCa cells, pointing to the AKT locus as a possible target for PCa. Overall, our data support sTWEAK/Fn14 axis as a potential therapeutic target for PCa.

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer.

ObjectiveTo reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).MethodsMRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).ResultsThe positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).ConclusionsWe found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Prostat Görüntüleme Raporlama ve Veri Sistemi-3 lezyonlarında prostat kanserini öngören temel faktörler

Objective: We aimed to investigate the role of the digital rectal examination, PSA density, regional location of the lesion and prostate size in predicting prostate cancer in Prostate Imaging and Data Reporting System (PI-RADS)-3 lesions. Material and Methods: A total of 236 patients with multiparametric MRI performed for clinical suspicion of prostate cancer and reported PI-RADS-3 enrolled between January 2016 and July 2019 in this retrospective study. The datas were extracted from the hospital’s electronic records, patient files and outpatient clinic records. Multiparametric MRI was performed patients to whom have elevated PSA level and/or suspicious digital rectal examination. Patients diagnosed with and without prostate cancer were compared in terms of age, PSA, PSA density, prostate size, pathological results, lesion localization and DRE findings. Results: One hundred thirty- independent predictor seven patients with an initial score of PI-RADS-3 were subjected to further analysis. Prostat cancer detection rate in overall and clinically significant prostate cancer detection rate was 26.2% and 4.3%, respectively. There was a significant difference regarding DRE findings (p=0.001) and PZ location of the lesion (p=0.005) between PCa and no PCa groups. Digital rectal examination (p=0.001) was an independent predictor of prostate cancer in multivariate logistic regression analysis. Conclusion: Digital rectal examination is a practical and important parameter in clarifying the suspicion of prostate cancer in PI-RADS-3 lesions. Keywords: prostatic neoplasms, digital rectal examination, multiparametric magnetic resonance imaging, image guided biopsy

Expression of anoctamin 7 in nuclear factor kappa B signal pathway and its clinical significance in prostate cancer

Objective To study the Expression of anoctamin 7 (ANO7) in the nuclear factor kappa B (NF-κB) signal pathway and its clinical significance in prostate cancer. Methods NF-κB signal pathway activated cell and animal model were constructed using tribbles homolog1 (TRIB1) overexpressed prostate cancer cell line. NF-κB signal pathway inhibited cell model was constructed using NF-κB signal pathway inhibitor. Western blotting and real-time quantitative polymerase chain reaction (Real-time PCR) and immunohistochemistry (IHC) were used to analyze the effect of NF-κB signal pathway on the regulation of ANO7 expression and the relationship between ANO7 expression and clinicopathological features in prostate cancer. Results ANO7 expression was up-regulated in the NF-κB signal pathway-activated cell and animal models. ANO7 expression was down-regulated in the NF-κB signal pathway-inhibited cell models. The high expression level of ANO7 was 45.8% in tumor tissues compared with 85.7% in adjacent non-cancerous tissues, with statistical significance (χ2=7.258, P<0.01). The expression of ANO7 was significantly correlated with Gleason score and pathological grade (χ2=19.797, 19.797, P<0.01). Similar results were obtained from The Cancer Genome Atlas (TCGA) and the Taylor database. Kaplan-Meier analysis found that ANO7 was significantly associated with the patients’prognosis in the Taylor database (P<0.01). Multivariate analysis found that ANO7 (P<0.05), Gleason score (P<0.01) and pathological grade (P<0.01) were independent predictors of prostate cancer using the Cox regression model in the cohort affecting prostate cancer prognosis. Conclusion NF-κB signal pathway regulates ANO7 expression in prostate cancer and the expression of ANO7 is closely related to prostate cancer. Key words: Anoctamin 7; Nuclear factor kappa B signal pathway; Prostate cancer; Prognosis

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer.

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

Comparison among different precursor prostatespecific antigen isoform derivatives on prostate cancer prediction in patients with serum prostatespecific antigen bellow 10 ng/ml

Introduction/Objective. The precursor prostate-specific antigen (proPSA) especially its isoform p2PSA is useful in the detection of prostate cancer (PCa). However, the prediction value of different p2PSA derivatives remains unclear. The aim of the study was to compare the performance of the p2PSA, percentage of p2PSA to free PSA (%p2PSA), prostate health index (Phi), and one prostate dimension-adjusted index, p2PSA density (p2PSAD), with each other for PCa prediction in patients with serum PSA 10 ng/ml or less. Methods. This prospective study included patients who had undergone ultrasound-guided prostate biopsies and p2PSA testing. The data about patients? clinicopathological characteristics were collected and %p2PSA, p2PSAD and Phi were calculated. Different aspect of predictive performance was assessed using the area under the receiver operating characteristic curve (AUC), the specificities at set sensitivities, and clinical utility using decision curve analyses (DCA). Results. PCa was diagnosed in 23 (32.4%) out of 71 patients. Results of multivariate analysis showed that only the Phi and digital rectal examination were independent predictors of PCa. The AUC of p2PSA, %p2PSA, p2PSAD and Phi were 76.2%, 81.5%, 88.7%, 89.6%, respectively. At pre-specified sensitivity of 90% and 95%, Phi demonstrated a greater specificity than the other p2PSA derivatives. Phi and p2PSAD lead to the higher net benefit in DCA. Conclusion. Compared with other p2PSA derivatives Phi is the most useful parameter for selection of the patients that do not need to be undergone to biopsy and thereby avoiding unnecessary procedures.

Improving the detection rate of prostate cancer in the gray zone of PI-RADS v2 and serum tPSA by using prostate-specific antigen-age volume.

To improve the detection of prostate cancer (PCa) by combining the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) and prostate-specific antigen–age volume (PSA–AV), especially among those in gray zone with PI-RADS v2 score 3 or serum total prostate-specific antigen (tPSA) 4 to 10 ng/mL.The 357 patients were enrolled in this study. The PI-RADS v2 scoring system was used to represent characteristics on multiparametric magnetic resonance imaging (mpMRI). PI-RADS v2 score 3 or tPSA 4 to 10 ng/mL were defined as the gray zone in detecting PCa. The formula equates to the patient age multiplied by the prostate volume, which is divided by the tPSA level. Univariate and multivariate analyses were done to ascertain significant predictors of prostate cancer.In all, 174 (48.7%) were benign prostatic hyperplasia, 183 (51.3%) had PCa. The results showed that PI-RADS v2, tPSA, and PSA–AV were significant independent predictors of prostate cancer. PI-RADS v2 score ≥4 could detect PCa with rate of 82.1%. Serum tPSA ≥10 ng/mL could detect PCa with rate of 66.2%, PSA density (PSAD) ≥0.15 ng/mL/cc with rate of 62.8%, and PSA–AV ≤250 with rate of 83.5%. Combining with PSA–AV ≤250, patients those with tPSA 4 to 10 ng/mL could improve the detection from 36.0% up to 81%, those with PI-RADS v2 score 3 from 28.6% up to 60.0%.PI-RADS v2 and PSA–AV are faithful variables for detecting PCa. And for patients, those in gray zones of PI-RADS v2 and tPSA, PSA–AV can improve detection rate of PCa.

Elevated Serum Sialic Acid Levels Predict Prostate Cancer As Well As Bone Metastases.

Objective: To evaluate the value of serum sialic acid (SA) in diagnosis of benign prostatic hyperplasia (BPH), prostate cancer (PCa), and bone metastases in PCa patients.Materials and Methods: Data from 540 patients who were newly diagnosed with PCa or BPH between November 2014 and March 2018 were retrospectively collected and analyzed. Pretreatment SA levels were compared across various groups, then, associations between SA levels and clinic parameters of patients were analyzed as well. Univariate and multivariate logistic regression analyses were further used to identify independent associations.Results: The mean SA levels in patients with PCa were significantly higher than with BPH (p = 0.013). Furthermore, PCa patients with bone metastases showed elevated SA levels compared with PCa without bone metastases (p < 0.001). A multivariate logistic regression model showed that: SA level > 52.35 mg/dL was identified to be independently associated with the diagnosis of PCa (HR = 1.645, p = 0.036), and SA level > 59 mg/dL was identified to be independent association with the presence of bone metastases in PCa patients (HR = 6.421, p = 0.012).Conclusions: Elevated SA level is an independent predictor of prostate cancer as well as its bone metastases. Therefore, SA level may be a promising diagnostic and prognostic biomarker for prostate cancer and bone metastases.

Cigarette smoking is not associated with prostate cancer diagnosis and aggressiveness: a cross sectional Italian study.

The aim of our study is to explore the association between cigarette smoking and the risk of prostate cancer (PCa) diagnosis as well as high grade disease in men undergoing prostate biopsies. From 2008 to 2016, we prospectively enrolled a consecutive series of men with no history of PCa undergoing prostate biopsies. Variables assessed the day of the biopsy included: Family history of PCa, age, body mass index (BMI), comorbidities, digital rectal examination (DRE), prostate volume, PSA, free PSA, smoking status, number of cigarette/day, smoking time, smoking starting age and smoking cessation. Smoking variables were evaluated as predictors of PCa and high grade PCa (Prognostic Grade Group ≥3) using adjusted logistic regression analyses. Overall 1080 patients were enrolled, out of them 499/1080/753 (46%) had never smoked; 168/1080/753 (16%) were smokers and 413/1080 (38%) were former smokers. Out of the 451/1080 (41%) patients with PCa 175/451 (39%) presented high grade disease. As expected family history, age, dyslipidemia, PSA, digital rectal examination and prostate volume were independent predictors of PCa. As well age, DRE, and PSA were independent predictors of high-grade disease. Smoking status (OR=0.983; CI: 0.689-1.402) and the associated variables were not independent predictors of PCa or high grade PCa (OR: 1.250; CI: 0.593-1.327). In our cross-sectional study, smoking is not predictive of PCa or high-grade disease. Further studies should evaluate if smoking could influence PCa diagnosis and aggressiveness.

Circulating Levels of Omentin, Leptin, VEGF, and HGF and Their Clinical Relevance with PSA Marker in Prostate Cancer.

Background Prostate cancer (PCa) is the first in terms of occurrence in Europe and second in Poland. The PCa risk factors include: genetic load, obesity, diet rich in fat, hypertriglyceridemia, and exposure to androgens. The prostate-specific antigen (PSA) level may be elevated in prostate cancer or other prostate disorders. Fat tissue secretes adipocytokines, which increase the risk of cancer development and metastasis. Objectives The aims of the study were to investigate the relationship between circulating levels of PSA, adipocytokines: omentin, leptin, hepatocyte growth factor (HGF), and vascular endothelial growth factor (VEGF) in serum obtained from patients with benign prostate hyperplasia (BPH) and prostate cancer (PCa). Methods Forty patients diagnosed with BPH and forty diagnosed with PCa were assessed for the purpose of the study. The concentrations of omentin, leptin, HGF, and VEGF were determined using enzyme-linked immunosorbent assays (EIA). Results PSA level was significantly higher in the PCa group than in BPH (18.2 versus 9 ng/mL, p < 0.01), while volume of prostate gland was significantly higher in the BPH group than in PCa (39.1 versus 31.1 cm3, p = 0.02). HGF, VEGF, omentin, and leptin concentrations were significantly higher in PCa group than in BPH (359.5 versus 294.9 pg/mL, p = 0.04; 179.3 versus 127.3 pg/mL, p < 0.01; 478.8 versus 408.3 ng/mL, p = 0.01; 15.7 versus 11.2 ng/mL, p = 0.02, resp.). The multiple logistic regression analysis demonstrated that only omentin and PSA levels were independent predictors of PCa in studied subjects. Conclusions PSA level as well as the level of omentin may be valuable markers of PCa with clinical significance, when compared to PSA.

Role of Holmium laser enucleation of the prostate to increase cancer detection rate in patients with gray-zone PSA level.

Even though the safety of the treatment for prostate cancer diagnosed by HoLEP has been reported, the diagnostic value of HoLEP for prostate cancer detection has not been confirmed. Therefore, we investigated the diagnostic potential of HoLEP for detecting prostate cancer. Between December 2009 and October 2015, 359 patients (median age, 70.9 years; range, 66.2-74.8) were treated simultaneously with HoLEP and transrectal prostate needle biopsy (TPNB). Of these, 199 patients with a normal digital rectal examination and serum PSA concentration between 3.5 and 10.0 ng/mL were included in the study. Univariate and multivariate logistic regression analyses were performed to identify the predictive factor for prostate cancer detected by HoLEP. Median PSA, prostate volume and PSA density were 4.97 ng/mL (range, 4.20-6.70), 57.40 gm (range, 43.67-77.80) and 0.09 ng/mL2 (range, 0.07-0.12), respectively. Prostate cancer (Gleason score ≥6) was detected in 46 cases (23.1%). Of these, 26 (56.5%) were detected by HoLEP pathology, 11 (23.9%) by TPNB pathology, and 9 (19.6%) by both. Univariate and multivariate logistic regression analyses were performed in 179 patients, including benign prostatic hyperplasia patients (N=153, 76.9%) and patients with cancer detected by HoLEP pathology. PSA density was identified as an independent predictor of prostate cancer detected by HoLEP in gray-zone PSA. HoLEP is a viable modality for detecting prostate cancer in selected cases. PSA density was an independent predictor of prostate cancer detected by HoLEP in gray-zone PSA.

Differences in inherited risk among relatives of hereditary prostate cancer patients using genetic risk score.

Family history assigns equivalent risk to all relatives based upon the degree of relationship. Recent genetic studies have identified single nucleotide polymorphisms (SNPs) that can be used to calculate a genetic risk score (GRS) to determine prostate cancer (PCa) risk. We sought to determine whether GRS can stratify PCa risk among individuals in families considered to be at higher risk due their family history of PCa. Family members with hereditary PCa were recruited and genotyped for 17 SNPs associated with PCa. A GRS was calculated for all subjects. Analyses compared the distribution of GRS values among affected and unaffected family members of varying relationship degrees. Data was available for 789 family members of probands including 552 affected and 237 unaffected relatives. Median GRSs were higher among first-degree relatives compared to second- and third-degree relatives. In addition, GRS values among affected first- and second-degree relatives were significantly higher than unaffected relatives (P = 0.042 and P = 0.016, respectively). Multivariate analysis including GRS and degree of relationship demonstrated that GRS was a significant and independent predictor of PCa (OR 1.52, 95%CI 1.15-2.01). GRS is an easy-to-interpret, objective measure that can be used to assess differences in PCa risk among family members of affected men. GRS allows for further differentiation among family members, providing better risk assessment. While prospective validation studies are required, this information can help guide relatives in regards to the time of initiation and frequency of PCa screening.