Underwent Prostate Biopsy Research Articles

A strategy to reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml-1 and PI-RADS 1-3.

We propose a strategy to reduce unnecessary prostate biopsies in Chinese patients with total prostate-specific antigen (tPSA) >10 ng ml-1 and Prostate Imaging Reporting and Data System (PI-RADS) scores between 1 and 3. Clinical data derived from 517 patients of The First Affiliated Hospital of USTC (Hefei, China) from January 2020 to December 2023 who met the screening criteria for the study were retrospectively collected. Independent predictors were identified via univariate and multivariate logistic regression analysis. The diagnostic capacity of clinical variables was evaluated using the receiver operating characteristic (ROC) curves and area under the curve (AUC). A prostate biopsy strategy was developed via risk stratification. Of the 517 patients, 17/348 (4.9%) with PI-RADS 1-2 were diagnosed with clinically significant prostate cancer (csPCa), and 27/169 (16.0%) patients with PI-RADS 3 were diagnosed with csPCa. The appropriate prostate-specific antigen density (PSAD) cut-off values were 0.45 ng ml-2 for PI-RADS 1-2 patients and 0.3 ng ml-2 for PI-RADS 3 patients. The appropriate prostate volume (PV) cut-off values were 40 ml for PI-RADS 1-2 patients and 50 ml for PI-RADS 3 patients. The prostate biopsy strategy based on PSAD and PV developed in this study can reduce unnecessary prostate biopsies in patients with tPSA >10 ng ml-1 and PI-RADS 1-3. In the study, 66.5% (344/517) patients did not need to undergo prostate biopsy, at the expense of missing only 1.7% (6/344) patients with csPCa.

Development and validation of biopsy free nomograms for predicting clinically significant prostate cancer in men with PI-RADS 4 and 5 lesions

To develop and validate biopsy-free nomograms to more accurately predict clinically significant prostate cancer (csPCa) in biopsy-naïve men with prostate imaging reporting and data system (PI-RADS) ≥ 4 lesions. A cohort of 931 patients with PI-RADS ≥ 4 lesions, undergoing prostate biopsies or radical prostatectomy from January 2020 to August 2023, was analyzed. Various clinical variables, including age, prostate-specific antigen (PSA) levels, prostate volume (PV), PSA density (PSAD), prostate health index (PHI), and maximum standardized uptake values (SUVmax) from PSMA PET-CT imaging, were assessed for predicting csPCa. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration plots, and decision-curve analyses, with internal validation. The foundational model (nomogram 1) encompassed the entire cohort, accurately predicting csPCa by incorporating variables such as age, PSAD, PV, PSA ratio variations, suspicious lesion location, and history of acute urinary retention (AUR). The AUC for csPCa prediction achieved by the foundational model was 0.918, with internal validation confirming reliability (AUC: 0.908). Advanced models (nomogram 2 and 3), incorporating PHI and PHI + PSMA SUVmax, achieved AUCs of 0.908 and 0.955 in the training set and 0.847 and 0.949 in the validation set, respectively. Decision analysis indicated enhanced biopsy outcome predictions with the advanced models. Nomogram 3 could potentially reduce biopsies by 92.41%, while missing only 1.53% of csPCa cases. In conclusion, the newly biopsy-free approaches for patients with PI-RADS ≥ 4 lesions represent a significant advancement in csPCa diagnosis in this high-risk population.

Phthalate exposure induces microRNA-5010/Nrf2-EGR1/GDF15 signaling expression in prostate cancer.

Phthalate exposure is linked to prostate enlargement through sex hormonal changes and oxidative stress. However, its role and action mechanism in prostate cancer remain unclear. This study examined two patient cohorts: 204 patients undergoing prostate biopsy (24 benign and 180 malignancies) and 85 with confirmed prostate cancer receiving robotic-assisted radical prostatectomy. Urine samples, collected with informed consent, were analyzed for urinary DEHP metabolites using HPLC-MS and ELISA. Patients with prostate cancer exhibited significantly higher urinary MEOHP and ΣDEHP metabolite levels than those who underwent benign biopsy (unpaired t-test, p = 0.027 and 0.039, respectively). MIR-5010 upregulation and MIR-205 downregulation were observed in two paired small RNA sequencing analyses (urine pellets of benign vs. malignant patients and PC3 cells without or with DEHP treatment), correlating with tumor staging in the TCGA prostate cancer cohort. Unlike MIR-205, a known tumor suppressor gene in prostate cancer, gene set enrichment analysis revealed that higher MIR-5010 expression was linked to increased Nrf-2 downstream signaling (enriched score: 0.35; p = 0.17). In vitro assays in prostate cancer cells showed that DEHP enhanced Nrf-2 protein expression and its downstream signaling molecules (i.e., SOD2, Heme oxygenase-1, and EGR-1) while increasing GDF15 mRNA expression via EGR-1 regulation in a dose- and time-dependent manner. Furthermore, urinary GDF15 levels were positively associated with urinary MEOHP and MEHP metabolites in the biopsy cohort (p = 0.0007 and 0.011, respectively) and with urinary oxidative stress marker 8-OHdG, aggressive marker VEGF, and CCL2/MCP-1 levels in the prostatectomy cohort (p = 0.0004, 0.006, and 0.0034, respectively). These findings suggest that phthalate exposure induces Nrf-2 and its downstream signaling (i.e., EGR-1/GDF-15) through microRNA regulation, contributing to prostate cancer aggressiveness.

Concordance with final pathology when transitioning from standard transrectal to cognitive targeted transperineal prostate biopsy.

Transrectal (TR) prostate biopsy is being increasingly abandoned in favour of a transperineal (TP) approach as well as a targeted biopsy only of the index lesion(s). It remains underreported how these changes could impact concordance at final pathology. We aimed to evaluate the impact of transitioning from standard transrectal (sTR) to cognitive targeted transperineal (cog-tTP) biopsy on final pathology including concordance and upgrading. Analysis of consecutive patients undergoing prostate biopsy and prostatectomy (RP) between January 2018 and May 2022 at a tertiary centre in Western Norway. There were 210 and 239 patients in the sTR and cog-tTP groups, respectively. The mean [IQR] number of biopsies decreased from 12 [4-12] to 3 [3-4] (p< 0.001). The overall rate of concordance between biopsy and final pathology was 64% in both groups (Table 3, Figure 1). 24% Twenty-four per cent (cog-tTP) versus 19% (sTR) had grade group (GG) upgrading, while 12% versus 17% were downgraded (p= 0.2). Regarding positive surgical margins (PSMs) that were >3 mm in extension, there were only 3.3% and 2.1% in the sTR and cog-tTP groups, respectively (p= 0.4). For surgical outcomes associated with RP, no differences in terms of postoperative complications between the groups were found (cog-tTP:10% vs. sTR:6%, p= 0.10). Transitioning from sTR biopsy to targeted cog-tTP biopsy does not compromise concordance at final pathology nor does it increase the risk of tumour upgrading.

Meta Analysis of Efficacy and Safety of Prostate Biopsy: A Comparison Between Transperineal and Transrectal Approach.

Improved prostate biopsy procedures have been developed to overcome traditional limitations, aiming to enhance cancer diagnosis accuracy. To assess the existing knowledge of the effectiveness and risks linked to transperineal (TP) vs. transrectal (TR) prostate biopsy. Approaches: a comprehensive search was carried out in PubMed, Embase, Web of Science, and Cochrane Library to locate all pertinent papers published till June 8, 2023. Data on cancer detection rate and complications after prostate biopsy were gathered and analyzed via Review Manager software. A subgroup analysis was conducted to evaluate the influence of the study type. A total of 19 publications, involving 80133 patients with suspicion of prostate cancer who underwent prostate biopsy, were enrolled in the analysis. The pooled estimate demonstrated no significant differences in the cancer detection rate between TR and TP (risk difference (RD)=0.03; 95% CI: -0.01 to 0.08; P = .12). In terms of complications, the TP approach significantly decreased the risk of rectal bleeding (odds ratio (OR)=0.24; 95% CI: 0.12-0.51; P < .001), fever and urinary tract infection (OR=0.28; 95% CI: 0.15-0.52; P < .001), and sepsis (OR=0.50; 95% CI: 0.28-0.90; P=.02) compared to the TR approach. In conclusion, there was no significant disparity in the cancer detection rate between TP and TR approaches. However, the TP strategy exhibited an advantage over TR due to a reduced risk of infection and rectal bleeding. Further research is needed to validate these results and develop a more efficient diagnostic approach for prostate cancer.

A nomogram with coagulation markers for prostate cancer prediction in patients with PSA levels of 4-20 ng/mL.

The global incidence of prostate cancer (PCa) is rising, necessitating improved diagnostic strategies. This study explores coagulation parameters' predictive value for clinically significant PCa (csPCa) and develops a nomogram. This study retrospectively analyzed data from 702 patients who underwent prostate biopsy at Shandong Provincial Hospital (SDPH) and 142 patients at Shandong Cancer Hospital and Institute (SDCHI). SDPH patients were randomly assigned at a 7:3 ratio for internal validation, while SDCHI data served as external validation. LASSO and logistic regression identified the best predictive factors for csPCa, which were used to construct a model. The model's efficacy was tested using AUC, calibration curves, and decision curve analysis. TPSA, age, D-dimer, prostate volume (PV), and digital rectal examination (DRE) were identified as independent risk factors for csPCa. A predictive model was constructed using a nomogram. The AUC for the training set was 0.841, for internal validation 0.809, and for external validation 0.814. Calibration and decision curves confirmed the model's clinical utility. The nomogram incorporating D-dimer, TPSA, age, PV, and DRE provides a highly accurate tool for assessing csPCa risk in individuals with PSA levels of 4-20 ng/mL, supporting personalized diagnostics and clinical decision-making.

Predictive value of neutrophil-to-lymphocyte ratio for clinically significant prostate cancer

Purpose: The neutrophil-to-lymphocyte ratio (NLR), one of the inflammatory markers, can be calculated simply from peripheral blood samples. In this study, the role of NLR in predicting significant prostate cancer (sPC) before prostate biopsy was investigated. Materials and methods: Patients who underwent prostate biopsy between 2017 and 2022 at the National Taiwan University Hospital were enrolled prospectively. NLR was calculated before the biopsy. sPC was defined as an unfavorable-intermediate or higher risk of the National Comprehensive Cancer Network risk classification. Results: Among 484 enrolled patients, 164 sPC (33.9%) and 320 benign/insignificant prostate cancer (isPC; 66.1%) patients were diagnosed. The sPC group was older and had a higher prostate-specific antigen (PSA), smaller prostate volume, higher PSA density, more advanced clinical T stage, and more family history of prostate cancer than the benign/isPC group. There was no difference in neutrophil count (P = 0.929), lymphocyte count (P = 0.927), or NLR (P = 0.931) between groups. NLR failed to predict sPC in univariable (odds ratio, 1.02; 95% confidence interval [CI], 0.93–1.12; P = 0.7) and multivariable (odds ratio, 0.94; 95% CI, 0.79–1.12; P = 0.506) logistic regression models, respectively. The area under the curve of the receiver operating characteristic curve in the model consisted of age, PSA, prostate volume, T stage, and family history of prostate cancer was 0.882 (95% CI, 0.849–0.914). There was no statistical difference when the model included NLR (P = 0.625). Conclusion: NLR did not differ between sPC and benign/isPC groups. NLR cannot predict sPC successfully before prostate biopsy.

Proportion of Gleason Score ≥8 Prostate Cancer on Biopsy and Tumor Aggressiveness in Matched Cohorts of East Asian and Non-East Asian Men.

Historically, Asia had a lower prostate cancer (PCa) incidence and mortality compared with Western countries, but the gap is narrowing. Paradoxically, Asians have been reported to present with more advanced disease though more favorable outcomes. Despite PCa becoming an emerging health priority in East Asia, our knowledge remains limited. We compared the prevalence of high-grade PCa on biopsy and disease progression after radical prostatectomy (RP) in East Asian men from Asia and non-East Asian men from Western countries. This retrospective cohort study included men who underwent prostate biopsy and RP at academic centers in Shanghai, China, and Toronto, Canada (2014-2019). The expanded RP cohort included East Asian men from Singapore (n=282) and non-East Asians from Paris (n=192). Primary endpoints included the proportion of men with Gleason score (GS) ≥8 on biopsy and metastasis-free survival (MFS) after RP for GS ≥8. Multivariable logistic regression and Cox proportional hazard models were performed. Propensity score matching was used to reduce imbalances between cohorts. PCa was found on biopsy in 2,343 of 4,905 (48%) East Asians and 2,317 of 3,482 (67%) non-East Asians (P<.001). Prostate-specific antigen (PSA) levels at presentation and the proportion of men with GS ≥8 were higher in East Asians than non-East Asians (12.4 vs 6.6 ng/mL and 15.0% vs 8.8%, respectively; both P<.001). On multivariable analysis, there was no difference in the proportion of men with GS ≥8 between matched cohorts with PSA <20 ng/mL (n=3,572; odds ratio, 1.05 [95% CI, 0.77-1.43]; P=.76). No difference in MFS was found after RP between matched cohorts (hazard ratio, 0.97 [95% CI, 0.55-1.70]; P=.92). This contemporary study demonstrates that East Asian men are equally as likely to harbor aggressive PCa on biopsy as non-East Asian men at PSA levels observed in screening programs, with no difference in disease aggressiveness after RP. The assumption that unfavorable PCa at diagnosis is more common but less aggressive in East Asians should be revisited and viewed in the context of the expected increase in the PCa burden worldwide.

Added value of amide proton transfer-weighted magnetic resonance imaging to Prostate Imaging Reporting and Data System version 2.1 in differentiating clinically significant prostate cancer.

Amide proton transfer-weighted magnetic resonance imaging (APTw-MRI) and apparent diffusion coefficient (ADC) values can effectively differentiate clinically significant prostate cancer (csPCa). However, their added value in Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v. 2.1) remains uncertain. This study aimed to investigate the added value of APTw-MRI and ADC to PI-RADS v. 2.1 in differentiating csPCa. This study included 161 participants who underwent prostate biopsy or radical prostatectomy within 1 month following MRI. Six magnetic resonance (MR) parameters [minimum APT value (APTmin), maximum APT value (APTmax), mean APT value (APTmean), minimum ADC value (ADCmin), maximum ADC value (ADCmax), and mean ADC value (ADCmean)] and the PI-RADS score were acquired by two radiologists. Receiver operating characteristic (ROC) curve analysis was performed to evaluate and select parameters with the greatest performance from the APT and ADC values. Multivariate logistic regression analysis was conducted to construct models based on selected MR parameters and the PI-RADS score. The Delong test was applied to compare the area under the ROC curve (AUC) between models. The lesions of csPCa exhibited higher APT values and lower ADC values than not clinically significant prostate cancer (ncsPCa). The greatest comprehensive performance in differentiating csPCa was achieved by APTmean (AUC: 0.723; cutoff value: >1.644) and ADCmean (AUC: 0.759; cutoff value: <0.656). The addition of APTmean significantly improved the performance of the model based on the PI-RADS score (AUC: APT PI-RADS 0.867 vs. PI-RADS 0.813; P=0.002). The introduction of ADCmean further improved the performance of the model based on APTmean and PI-RADS (AUC: combined model 0.875 vs. PI-RADS 0.867, P=0.69), and it retained high diagnostic efficacy (AUC: 0.835) in the validation cohort. Further verification through calibration curve and decision curve analysis demonstrated that the adjusted PI-RADS model exhibited remarkable precision and practicality. APTw-MRI and ADC are valuable parameters for differentiating csPCa and can provide additional value for improving the efficiency and clinical benefit of PI-RADS v. 2.1.

A Machine Learning Model for Prostate Cancer Prediction in Korean Men

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability.Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio.Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863.Conclusions: Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Tumor characteristics, treatments, and oncological outcomes of prostate cancer in men aged ≤60 years: real-world data from a single urological center over a 10-year period.

Prostate cancer (PCa) has emerged as one of the most common malignancies among men globally. However, its pathogenesis, clinical features, and treatment responses in younger patients (aged 60 years or below) remain underexplored. This study aims to evaluate the distinctive clinical features, treatment strategies, and oncological outcomes of PCa in men aged 60 years or younger over a 10-year period at a single urological center. We retrospectively analyzed data from The Second Hospital of Tianjin Medical University, spanning January 2010 to June 2020. The study included patients aged ≤60 years who underwent prostate biopsies. We examined clinical characteristics, pathological findings, treatment approaches, and survival outcomes using t-tests and chi-square tests. Adjusted linear regression models evaluated the relationships between treatment modalities and outcomes, while Kaplan-Meier survival analysis and Cox regression assessed progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS). Of the 5,686 patients who underwent prostate biopsy, 643 (11.3%) were ≤60 years old. Among these, 42.8% (275/643) were diagnosed with PCa, with 59.6% presenting at advanced stages. Compared to those with negative pathology, patients with PCa were older and more likely to have hypertension, alcohol consumption, and metabolic syndrome. Most patients (69.8%) received definitive local therapy, while 22.2% opted for palliative care and 8.0% were loss to follow-up. The median follow-up period for the entire cohort was 28.0 months and the median PFS was 77.0 months. For patients receiving definitive local therapy, the median CSS and OS were not reached, while those undergoing palliative therapy had median CSS and OS of 52.0 and 59.0 months, respectively. Multivariable analysis identified prostate-specific antigen >20 ng/mL, International Society of Urological Pathology >3, bone metastasis, and localized treatment as independent factors affecting PFS. Propensity score matching showed that definitive therapy led to superior PFS compared to palliative therapy for patients with localized PCa and a life expectancy of over 5 years. Our findings highlight the influence of incidence, diagnostic characteristics, and treatment methods in younger men with PCa, emphasizing the need to identify specific risk factors and treatment response patterns. Further large-scale, multi-center research is necessary to improve diagnosis and outcomes for PCa patients in this age group.

Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment

This bicenter retrospective analysis included 162 patients who had undergone prostate biopsy following prebiopsy MRI, excluding those with PCa identified only in the peripheral zone (PZ). DLR T2WI achieved a 69% reduction in scan time relative to TSE T2WI. The intermethod agreement between the two T2WI sets in terms of the Prostate Imaging Reporting and Data System (PI-RADS) classification and extraprostatic extension (EPE) grade was measured using the intraclass correlation coefficient (ICC) and diagnostic performance was assessed with the area under the receiver operating characteristic curve (AUC). Clinically significant PCa (csPCa) was found in 74 (45.7%) patients. Both T2WI methods showed high intermethod agreement for the overall PI-RADS classification (ICC: 0.907–0.949), EPE assessment (ICC: 0.925–0.957) and lesion size measurement (ICC: 0.980–0.996). DLR T2WI and TSE T2WI showed similar AUCs (0.666–0.814 versus 0.684–0.832) for predicting EPE. The AUCs for detecting csPCa with DLR T2WI (0.834–0.935) and TSE T2WI (0.891–0.935) were comparable in 139 patients with TZ lesions with no significant differences (P > 0.05). The findings suggest that DLR T2WI is an efficient alternative for TZ lesion assessment, offering reduced scan times without compromising diagnostic accuracy.

The Comparative Study on the Application Value of Transperineal Prostate Combined Biopsy and Transrectal Prostate Systematic Biopsy in Diagnosing Prostate Cancer in Patients with Different PSA Zones.

This study aimed to compare the effects of transperineal prostate combined biopsy (TP-CB) and transrectal prostate systematic biopsy (TR-SB) on the detection rate and safety of prostate cancer in patients with prostate-specific antigen (PSA) gray zone, PSA levels of 10~20 ng/mL, and 20~40 ng/mL, and explore the comparative value of their applications. We collected 243 samples from patients with PSA ≤ 40 ng/mL who underwent prostate biopsy. All patients were divided into two groups according to different patterns of prostate biopsy. The detection rates of prostate cancer and clinically significant prostate cancer (CsPCa) in patients with PSA levels of 4~10, 10~20, and 20~40 ng/mL were compared between two different biopsy methods, surgical conditions, and the incidence of complications. The rate of a positive prostate cancer biopsy was significantly higher in TP-CB than in TR-SB (P < 0.05). Further subgroup analyses revealed no statistical significance in the rate of positive prostate cancer biopsy in patients with PSA levels of 4~10 and 20~40 ng/mL between the two groups (P > 0.05). However, patients with PSA levels of 10~20 ng/mL in the TP-CB group exhibited a higher detection rate (P < 0.05), with CsPCa accounting for a higher proportion. The TP-CB and TR-SB groups did not exhibit a significant difference in surgical conditions or overall complication rates (P > 0.05). However, the TR-SB group exhibited a higher risk of postoperative febrile infection than the TP-CB group (P < 0.05). For patients with PSA in the 'sub-gray zone' (10~20 ng/mL), TP-CB has a better diagnostic and application value and is more suitable for clinical promotion.

Estimated diagnostic performance of prostate MRI performed with clinical suspicion of prostate cancer

PurposeTo assess the diagnostic performance of prostate MRI by estimating the proportion of clinically significant prostate cancer (csPCa) in patients without prostate pathology.Materials and methodsThis three-center retrospective study included prostate MRI examinations performed for clinical suspicion of csPCa (Grade group ≥ 2) between 2018 and 2022. Examinations were divided into two groups: pathological diagnosis within 1 year after the MRI (post-MRI pathology) is present and absent. Risk prediction models were developed using the extracted eleven common predictive variables from the patients with post-MRI pathology. Then, the csPCa proportion in the patients without post-MRI pathology was estimated by applying the model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV/NPV) of prostate MRI in diagnosing csPCa were subsequently calculated for patients with and without post-MRI prostate pathology (estimated statistics) with a positive threshold of PI-RADS ≥ 3.ResultsOf 12,191 examinations enrolled (mean age, 65.7 years ± 8.4 [standard deviation]), PI-RADS 1–2 was most frequently assigned (55.4%) with the lowest pathological confirmation rate of 14.0–18.2%. Post-MRI prostate pathology was found in 5670 (46.5%) examinations. The estimated csPCa proportions across facilities were 12.6–15.3%, 18.4–31.4%, 45.7–69.9%, and 75.4–88.3% in PI-RADS scores of 1–2, 3, 4, and 5, respectively. The estimated (observed) performance statistics were as follows: AUC, 0.78–0.81 (0.76–0.79); sensitivity, 76.6–77.3%; specificity, 67.5–78.6%; PPV, 49.8–66.6% (52.0–67.7%); and NPV, 84.4–87.2% (82.4–86.6%).ConclusionWe proposed a method to estimate the probabilities harboring csPCa for patients who underwent prostate MRI examinations, which allows us to understand the PI-RADS diagnostic performance with several metrics.Clinical relevance statementThe reported estimated performance metrics are expected to aid in understanding the true diagnostic value of PI-RADS in the entire prostate MRI population performed with clinical suspicion of prostate cancer.Key PointsCalculating performance metrics only from patients who underwent prostate biopsy may be biased due to biopsy selection criteria, especially in PI-RADS 1–2.The estimated area under the receiver operating characteristic curve of PI-RADS in the entire prostate MRI population ranged from 0.78 to 0.81 at three facilities.The estimated statistics are expected to help us understand the true PI-RADS performance and serve as a reference for future studies.Graphical

Single-dose vs prolonged antibiotic prophylaxis of fosfomycin for transrectal prostate biopsy: a single-center prospective, randomized, controlled trial

BackgroundTransrectal prostate biopsy is a commonly performed urological procedure in which antibiotic prophylaxis is recommended. Fluoroquinolone-type antibiotics are no longer acceptable in the EU. Fosfomycin-trometamol may be used, but there is no evidence regarding its ideal dose and administration time. MethodsPatients who underwent prostate biopsy between 2021 and 2023 were evaluated prospectively. 204 patients were randomized into two arms: 102 patients (Arm A) received a single-dose of fosfomycin-trometamol one hour before surgery, and 102 patients (Arm B) received one additional dose of fosfomycin-trometamol 48 hours after the first dose. Urine tests and questionnaires were administered during the postoperative period and the subsequent four weeks to identify any symptoms, infectious, or other complications. ResultsThere was no statistical difference in the rate of asymptomatic bacteriuria (4.90% (5) vs. 8.82% (9), P = 0.27) symptomatic urinary tract infection (0% (0) vs. 1.96% (2), P = 0.50), or febrile urinary tract infection (0% (0) vs. 0.98% (1), P = 1) between the groups. Only hematuria was significantly more common in Arm B (6.86% (7) vs. 16.67% (17), P = 0.03), whereas other complications did not differ significantly. There was no statistical difference in hospitalization (0.98% (1) vs. 2.94 (3), P = 0.62) or mortality rate (0 % (0) vs. 0.98% (1), P = 1). Sub-group analysis of previous antibiotic users showed no difference in terms of complications. ConclusionThere is no significant difference in infectious complications between single-dose and prolonged prophylaxis of fosfomycin-trometamol for transrectal prostate biopsy. A single-dose of fosfomycin one hour before biopsy is an ideal choice with a better ecological impact compared with prolonged antibiotic prophylaxis for transrectal prostate biopsy.

A Novel Machine Learning-Based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator

ObjectivesTo create a machine learning predictive model combining PI-RADS score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa). MethodsWe evaluated a cohort of patients who underwent prostate biopsy for suspected prostate cancer (PCa) in New Zealand, Australia, and Switzerland. We collected data on age, body mass index (BMI), PSA level, prostate volume, PSA density (PSAD), PI-RADS scores, previous biopsy, and corresponding histology results. The dataset was divided into derivation (training) and validation (test) sets using random splits. An independent dataset was obtained from the Harvard Dataverse for external validation. A cohort of 1272 patients was analyzed. We fitted a Lasso model, XGBoost, and LightGBM to the training set and assessed their accuracy. ResultsAll models demonstrated ROC AUC values ranging from 0.830 to 0.851. LightGBM was considered the superior model, with an ROC of 0.851 [95%CI: 0.804 – 0.897] in the test set and 0.818 [95% CI: 0.798 – 0.831] in the external dataset. The most important variable was PI-RADS, followed by PSA density, history of previous biopsy, age, and BMI. ConclusionsWe developed a predictive model for detecting csPCa that exhibited a high ROC-AUC value for internal and external validations. This suggests that the integration of the clinical parameters outperformed each individual predictor. Additionally, the model demonstrated good calibration metrics, indicative of a more balanced model than the existing models.

Assessing the predictive value of intraductal carcinoma of the prostate (IDC-P) in determining abiraterone efficacy for metastatic hormone-sensitive prostate cancer (mHSPC) patients.

This study explored the value of intraductal carcinoma of the prostate (IDC-P) in predicting the efficacy of abiraterone treatment in metastatic hormone-sensitive prostate cancer (mHSPC) patients. A retrospective study of 925 patients who underwent prostate biopsies to detect IDC-P was conducted, with participants divided into two cohorts. The first cohort of 165 mHSPC patients receiving abiraterone treatment was analyzed to compare therapeutic effectiveness between IDC-P positive and negative cases. Utilizing propensity score matching (PSM) to reduce bias, outcomes such as PSA response, progression-free survival (PSA-PFS), radiographic progression-free survival (rPFS), and overall survival were assessed. Additionally, the second cohort of 760 mHSPC patients compared the efficacy of abiraterone with conventional hormone therapy, focusing on differences between IDC-P positive and negative individuals. After PSM, our first cohort included 108 patients with similar baseline characteristics. Among them, 50% (54/108) were diagnosed with IDC-P, with 22.2% (12/54) having IDC-P pattern 1 and 77.8% (42/54) with IDC-P pattern 2. While no notable difference was seen in PSA responses between IDC-P positive and negative patients, IDC-P presence linked to worse clinical outcomes (PSA-PFS: 18.6 months vs. not reached [NR], p = 0.009; rPFS: 23.6 months vs. NR, p = 0.020). Further analysis showed comparable outcomes for IDC-P pattern 1 but significantly worse prognosis for IDC-P pattern 2 (PSA-PFS: 18.6 months vs. NR, p = 0.002; rPFS: 22.4 months vs. NR, p = 0.010). Subgroup analysis revealed IDC-P pattern 2 consistently predicted poorer outcomes across patient subgroups. Remarkably, both IDC-P positive and negative patients gained more from androgen deprivation therapy with abiraterone than conventional treatment, with IDC-P negative patients showing a more significant survival advantage, supported by better hazard ratios (0.47 and 0.66). This study found that IDC-P, especially pattern 2, predicts poor prognosis in mHSPC patients on abiraterone therapy. Also, abiraterone's advantage over hormone therapy is reduced in cases with IDC-P compared to those without.

Clinical value of a radiomics model based on machine learning for the prediction of prostate cancer.

Radiomics models have demonstrated good performance for the diagnosis and evaluation of prostate cancer (PCa). However, there are currently no validated imaging models that can predict PCa or clinically significant prostate cancer (csPCa). Therefore, we aimed to identify the best such models for the prediction of PCa and csPCa. We performed a retrospective study of 942 patients with suspected PCa before they underwent prostate biopsy. MRI data were collected to manually segment suspicious regions of the tumor layer-by-layer. We then constructed models using the extracted imaging features. Finally, the clinical value of the models was evaluated. A diffusion-weighted imaging (DWI) plus apparent diffusion coefficient (ADC) random-forest model and a T2-weighted imaging plus ADC and DWI multilayer perceptron model were the best models for the prediction of PCa and csPCa, respectively. Areas under the curve (AUCs) of 0.942 and 0.999, respectively, were obtained for a training set. Internal validation yielded AUCs of 0.894 and 0.605, and external validation yielded AUCs of 0.732 and 0.623. Models based on machine learning comprising radiomic features and clinical indicators showed good predictive efficiency for PCa and csPCa. These findings demonstrate the utility of radiomic models for clinical decision-making.

Unlocking Precision Enhancing Prostate Cancer Detection and Reducing Unnecessary Biopsies with Combined Prostate-Specific Antigen Density and PI-RADS Score.

To determine clinically significant prostate cancer (csPCa) detection rate by combining the prostate-specific antigen density (PSAD) and prostate imaging-reporting and data system (PI-RADS) scores. Descriptive study. Place and Duration of the Study: Department of Urology, University of Health Sciences, Ankara Oncology Training and Research Hospital, from January 2018 to April 2023. Patients who underwent prostate biopsies after multiparametric magnetic resonance imaging (mpMRI) were included in the study. PI-RADS 4 and 5 lesions were considered as MR positive. The cut-off values for PSAD were also determined to evaluate csPCa. csPCa detection rates were evaluated by grouping the patients based on the PSAD and mpMRI findings. PSAD cut-off value of 0.165 ng/mL/mL (sensitivity 80%, specificity 72%) was detected to predict csPCa (AUC = 0.81, 95% CI:0.756-0.866, p<0.001). csPCa detection rate was low (3%) in patients who have low PI-RADS scores (1-3) and a PSAD <0.165 ng/mL/mL. On the other hand, csPCa detection rate was high (50.5%) in patients who have a high PI-RADS score (4-5 lesions) and with a PSAD ≥0.165 ng/mL/mL. csPCa detection rates are low in patients with PI-RADS 1-3 lesions and low PSAD values. Unnecessary biopsy may be avoided in these patients. Gleason score, PI-RADS, Prostate cancer, Prostate-specific antigen, Prostate-specific antigen density.

Abstract A039: Joint roles of proteomics and neighborhood-level social determinants of metastatic prostate cancer

Abstract Introduction: Striking racial disparities in prostate cancer (PCa) outcomes among American Black men warrant significant efforts to identify early predictors of lethal PCa, to avoid over- treatment of clinically favorable disease with minimal risk of progression. Building on prior work, this study examined independent and joint roles of proteomic markers and social determinants of health (SDOHs) in predicting prostate disease aggressiveness in a racially diverse cohort of men undergoing prostate biopsy. Methodology: A retrospective cohort of 300 men biopsied for prostate cancer was assembled at University Hospitals (UH) Seidman Cancer Center in Cleveland, Ohio between January 1, 2005-May 2022. An extensive medical chart review was performed to identify Black and White men undergoing transrectal ultrasound- guided biopsy that resulted in: (i) a negative biopsy in patients with a history of 1+ prior negative biopsy and no PCa history (“NEG”); (ii) a biopsy-detected PCa of Gleason sum 6 (“GL6”); and (iii) biopsy-detected PCa with nodal and/or distant metastases at initial cancer detection (“METS”). Prostate biopsy tissues were obtained on each patient, balancing 100 men per biopsy group, and balanced on race (1:1) within each biopsy group. Informed by findings from an untargeted analysis of the entire proteome of 60 patients (“Discovery cohort”), we conducted a targeted proteomics analysis of 16 protein markers generated from prostate biopsy tissue, using mass spectrometry, on n=240 patients (“Verification cohort”). Chi-square testing and Analysis of Variance (ANOVA) were used to assess associations between protein expression, neighborhood-level SDOHs, and biopsy group. Race-stratified analyses were performed to examine statistical interactions between study associations. Principal components (PC) analysis was used to identify protein markers that predicted the greatest variance in biopsy group. Multivariable logistic regression (MLR) was used to predict METS (vs. NEG and GL6, combined), as a function of PC-selected protein markers and census block-group level area deprivation index (ADI). Bonferroni correction was used to establish the threshold for the decision rule of statistical significance (alpha error 0.05/16 markers: p&amp;lt; 0.0031). Results: Median patient age was 68 years. Protein expression distributions were transformed into quintiles due to non-normality. Of 16 protein markers, 14 were significantly correlated with biopsy group, including PARP 1 and TGFB1, for which METS patients had the highest expression. ADI was strongly correlated with race but not protein expression. ADI was significantly higher in METS patients, followed by GL6 and lowest in NEG group; however no associations were seen for ADI with protein expression. Eight proteins were found to predict METS in PC and MLR analysis. Conclusions: These data support use of proteomics markers to predict a metastatic versus non-metastatic biopsy result. Next steps include deep exploration of proteome findings generated in the Discovery cohort, such as biological pathways. Citation Format: Jennifer Cullen, Tao Liu, Holly Hartman, Fangzhou Liu, Anood Alfahmy, Rini Ghosh, Ayesha Shafi, Julia Payne, Randy Vince, Lee Ponsky, Gregory MacLennan. Joint roles of proteomics and neighborhood-level social determinants of metastatic prostate cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A039.