Gray-zone Prostate-specific Antigen Research Articles

Single‑center, retrospective, evaluator‑blinded, pilot and pivotal clinical trials: Assessing the mirCaP Kit (hsv2‑miR‑H9/hsa‑miR‑3659) as a diagnostic marker for prostate cancer in patients with PSA levels in the gray zone.

Prostate-specific antigen (PSA) remains a key biomarker for the diagnosis and monitoring of prostate cancer (PCa). For patients within the 'PSA gray zone', the positive predictive value (PPV) of PSA for PCa detection by biopsy is estimated to be between 30 and 42%. In the present study, a single-center, retrospective, evaluator-blinded, pilot and pivotal clinical trial was performed to assess the clinical performance of the mirCaP kit (Urotech, Inc.), which measures the herpes simplex virus 2-microRNA (miR)-H9/hsa-miR-3659 ratio, with respect to helping physicians make appropriate decisions regarding further assessment of patients with PSA levels within this gray zone. For the patients in the initial clinical trial group who were in the PSA gray zone, the sensitivity, specificity, accuracy, PPV and negative predictive value (NPV) of the mirCaP kit were 94.29, 77.50, 85.33, 78.57 and 93.94%, respectively. For those in the pivotal clinical trial, these values were 94.50, 82.73, 87.90, 81.10 and 95.04%, respectively. These results suggest that the mirCaP kit may be an effective non-invasive diagnostic marker for PCa in patients with PSA levels in the gray zone. Thus, the mirCaP kit is a promising tool that can help physicians make a decision regarding the need for prostate biopsy in these patients. Of note, the NPV of >90% indicates that the mirCaP kit could prevent unnecessary prostate biopsies in >90% of these cases.

Diagnostic performance of MRI in detecting prostate cancer in patients with prostate-specific antigen levels of 4–10 ng/mL: a systematic review and meta-analysis

ObjectiveTo investigate the diagnostic performance of MRI in detecting clinically significant prostate cancer (csPCa) and prostate cancer (PCa) in patients with prostate-specific antigen (PSA) levels of 4–10 ng/mL.MethodsA computerized search of PubMed, Embase, Cochrane Library, Medline, and Web of Science was conducted from inception until October 31, 2023. We included articles on the use of MRI to detect csPCa or PCa at 4–10 ng/mL PSA. The primary and secondary outcomes were MRI performance in csPCa and PCa detection, respectively; the estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were pooled in a bivariate random-effects model.ResultsAmong the 19 studies (3879 patients), there were 10 (2205 patients) and 13 studies (2965 patients) that reported MRI for detecting csPCa or PCa, respectively. The pooled sensitivity and specificity for csPCa detection were 0.84 (95% confidence interval [CI], 0.79–0.88) and 0.76 (95%CI, 0.65–0.84), respectively, for PCa detection were 0.82 (95%CI, 0.75–0.87) and 0.74 (95%CI, 0.65–0.82), respectively. The pooled NPV for csPCa detection was 0.91 (0.87–0.93). Biparametric magnetic resonance imaging also showed a significantly higher sensitivity and specificity relative to multiparametric magnetic resonance imaging (both p < 0.01).ConclusionProstate MRI enables the detection of csPCa and PCa with satisfactory performance in the PSA gray zone. The excellent NPV for csPCa detection indicates the possibility of biopsy decision-making in patients in the PSA gray zone, but substantial heterogeneity among the included studies should be taken into account.Clinical relevance statementProstate MRI can be considered a reliable and satisfactory tool for detecting csPCa and PCa in patients with PSA in the “gray zone”, allowing for reducing unnecessary biopsy and optimizing the overall examination process.Key PointsProstate-specific antigen (PSA) is a common screening tool for prostate cancer but risks overdiagnosis.MRI demonstrated excellent negative predictive value for prostate cancer in the PSA gray zone.MRI can influence decision-making for these patients, and biparametric MRI should be further evaluated.Graphical

Transrectal versus transperineal prostate biopsy for cancer detection in patients with gray-zone prostate-specific antigen: a multicenter, real-world study.

Knowledge about the effect of different prostate biopsy approaches on the prostate cancer detection rate (CDR) in patients with gray-zone prostate-specific antigen (PSA) is limited. We performed this study to compare the CDR among patients who underwent different biopsy approaches and had rising PSA levels in the gray zone. Two hundred and twenty-two patients who underwent transrectal prostate biopsy (TRB) and 216 patients who underwent transperineal prostate biopsy (TPB) between June 2016 and September 2022 were reviewed in this study. In addition, 110 patients who received additional targeted biopsies following the systematic TPB were identified. Clinical parameters, including age, PSA derivative, prostate volume (PV), and needle core count, were recorded. The data were fitted via propensity score matching (PSM), adjusting for potential confounders. TPB outperformed TRB in terms of the CDR (49.6% vs 28.3%, P = 0.001). The clinically significant prostate cancer (csPCa) detection rate was not significantly different between TPB and TRB (78.6% vs 68.8%, P = 0.306). In stratified analysis, TPB outperformed TRB in CDR when the age of patients was 65-75 years (59.0% vs 22.0%, P < 0.001), when PV was 25.00-50.00 ml (63.2% vs 28.3%, P < 0.001), and when needle core count was no more than 12 (58.5% vs 31.5%, P = 0.005). The CDR ( P = 0.712) and detection rate of csPCa ( P = 0.993) did not significantly differ among the systematic, targeted, and combined biopsies. TPB outperformed TRB in CDR for patients with gray-zone PSA. Moreover, performing target biopsy after systematic TPB provided no additional benefits in CDR.

Diagnostic value of 18F-PSMA-1007 PET/CT combined with prostate specific antigen derived indicators in gray area prostate cancer.

The incidence of prostate cancer is increasing every year, and precision diagnosis and treatment can help reduce unnecessary prostate punctures for prostate cancer patients in the gray area. This study aims to investigate the diagnostic value of 18F-prostate specific membrane antigen (PSMA) imaging combined with prostate specific antigen (PSA)-derived indicators for gray zone prostate cancer. A total of 107 patients who underwent 18F-PSMA PET/CT imaging for suspicious prostate cancer with tPSA of 4 to 10 μg/L (PSA gray zone) in a hospital were retrospectively included, and were divided into a prostate cancer group and a non-prostate cancer group based on pathological findings. Patients underwent PSA testing, 18F-PSMA, and abdominal ultrasound, and age, tPSA, fPSA, f/tPSA, prostate volume, PSA density (PSAD), maximum standardized uptake value (SUVmax), and molecular imaging prostate specific membrane antigen (miPSMA) score were compared between the 2 groups. Multivariate logistic regression was used to analyze the influencing factors the diagnosis of gray zone prostate cancer. Receiver operating characteristic (ROC) curves were constructed to evaluate the efficacy of PSAD and SUVmax alone and in combination in diagnosing gray zone prostate cancer. The volume of the prostate cancer group [42.00(34.00, 58.00) cm3 vs 49.00(41.27, 60.41) cm3] was smaller than that of the non-prostate cancer group (Z=-2.376, P=0.017), and the PSAD [(0.18±0.06) μg/(L·cm3) vs 0.15±0.05 μg/(L·cm3)] and SUVmax [18.63(8.03, 28.57) vs 9.33(5.90, 13.52)] were higher than those in the non-prostate cancer group (both P<0.05). The percentage of miPSMA score ≥2 in the prostate cancer group was higher than that in the non-prostate cancer group (χ2=40.987, P<0.001). PSAD (OR=22.154, 95% CI 1.430 to 873.751, P=0.042) and SUVmax (OR=1.301, 95% CI 1.034 to 1.678, P=0.009) were independent influential factors for the diagnosis of prostate cancer in the gray zone. The optimal cut-off values of PSAD and SUVmax were 0.22 μg/(L·cm3) and 8.02, respectively, and the AUCs for the diagnosis of prostate cancer in the gray zone alone and in combination were 0.628 (95% CI 0.530 to 0.720, P<0.05) and 0.806 (95% CI 0.718 to 0.876, P<0.05), 0.847 (95% CI 0.765 to 0.910, P<0.05), with sensitivities of 41.03%, 76.92%, and 74.36% and specificities of 79.41%, 89.71%, and 92.65%, respectively. PSAD and SUVmax are increased in patients with gray zone prostate cancer, and the combination of PSAD and SUVmax is of high value in diagnosing gray zone prostate cancer.

Interfacial Polymerization Produced Magnetic Particles with Nano-Filopodia for Highly Accurate Liquid Biopsy in the PSA Gray Zone.

Magnetic particles are leading separation materials for biological purification and detection. Existing magnetic particles, which almost rely on molecule-level interactions, however, often encounter bottlenecks in highly efficient cell-level separation due to the underestimate of surface structure effects. Here, immune cell-inspired magnetic particles with nano-filopodia (NFMPs) produced by interfacial polymerization for highly efficient capture of circulating tumor cells (CTCs) and further accurate clinical diagnosis of prostate cancer are reported . The unprecedented construction of nano-filopodia on polymer-based magnetic particles is achieved by introducing electrostatic interactions in emulsion interfacial polymerization. Due to the unique nano-filopodia, the NFMPs allow remarkably enhanced CTCs capture efficiency (86.5%±2.8%) compared with smooth magnetic particles (SMPs, 35.7%±5.7%). Under the assistance of machine learning by combining with prostate-specific antigen (PSA) and free to total PSA (F/T-PSA), the NFMPs strategy demonstrates high sensitivity (100%), high specificity (93.3%), and a high area under the curve (AUC) value (98.1%) for clinical diagnosis of prostate cancer in the PSA gray zone. The NFMPs are anticipated as an efficient platform for CTCs-based liquid biopsy toward early cancer diagnosis and prognosis evaluation.

Clinical value of prostate health index as an indicator for recommending magnetic resonance imaging in patients with gray-zone prostate-specific antigen level.

To evaluate the usefulness of prostate health index (PHI) as an indicator for recommending magnetic resonance imaging (MRI) in patients with prostate-specific antigen (PSA) gray zone level < 10ng/mL. 443 patients who underwent prostate biopsy (PB) after serum PHI test and MRI between April 2019 and December 2022 were enrolled. For patients with visible lesion on MRI with Prostate Imaging Reporting and Data System Score (PI-RADS) ≥ 3, MRI-targeted PB was performed in addition to systematic 12-core PB. The optimal cutoff value of PHI for predicting PI-RADS ≥ 3 lesions was 39.6, which was significantly associated with overall prostate cancer (OR 3.07, p = 0.018) and clinically significant prostate cancer (csPCa) (OR 4.15, p = 0.006) at MRI-targeted PB cores. When MRI was restricted to patients with PHI ≥ 39.6 alone, 28.7% of unnecessary MRI could be saved at the cost of missing 13.6% of csPCa. When omitting MRI for patients with PHI < 39.6 and PSAD < 0.12ng/mL2, unnecessary MRI could be reduced by 20.1% with the risk of missing 6.2% of csPCa. With addition of systematic PB, 21.0% of patients with negative MRI-targeted PB were diagnosed as csPCa. For patients in PSA gray zone, PHI of 39.6 might be an indicator for MRI and further MRI-targeted PB in additional to PSAD of 0.12ng/mL2, reducing 20.1% of unnecessary MRI with the minimal risk of missing 6.2% of csPCa. To maximize csPCa detection, combining both MRI-targeted and systematic PB should be also considered.

Economic evaluation of prostate cancer risk assessment methods: A cost-effectiveness analysis using population data.

The current prostate cancer (PCa) screening standard of care (SOC) leads to unnecessary biopsies and overtreatment because decisions are guided by prostate-specific antigen (PSA) levels, which have low specificity in the gray zone (3-10 ng/mL). New risk assessment tools (RATs) aim to improve biopsy decision-making. We constructed a modeling framework to assess new RATs in men with gray zone PSA from the British Columbia healthcare system's perspective. We evaluated the cost-effectiveness of a new RAT used in biopsy-naïve men aged 50+ with a PSA of 3-10 ng/mL using a time-dependent state-transition model. The model was informed by engaging patient partners and using linked administrative health data, supplemented with published literature. The incremental cost-effectiveness ratio and the probability of the RAT being cost-effective were calculated. Probabilistic analysis was used to assess parameter uncertainty. In the base case, a RAT based on an existing biomarker's characteristics was a dominant strategy associated with a cost savings of $44 and a quality-adjusted life years (QALY) gain of 0.00253 over 18 years of follow-up. At a cost-effectiveness threshold of $50,000/QALY, the probability that using a RAT is cost-effective relative to the SOC was 73%. Outcomes were sensitive to RAT costs and accuracy, especially the detection rate of high-grade PCa. Results were also impacted by PCa prevalence and assumptions about undetected PCa survival. Our findings showed that a more accurate RAT to guide biopsy can be cost-effective. Our proposed general model can be used to analyze the cost-effectiveness of any novel RAT.

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

BackgroundThe prostate-specific antigen (PSA) has been widely used in screening and early diagnosis of prostate cancer (PCa). However, in the PSA grey zone of 4–10 ng/ml, the sensitivity and specificity for diagnosing PCa are limited, resulting in considerable number of unnecessary and invasive prostate biopsies, which may lead to potential overdiagnosis and overtreatment. We aimed to predict clinically significant PCa (CSPCa) by combining the maximal standardized uptake value (SUVmax) based on 68Ga‑PSMA PET/CT and clinical indicators in men with gray zone PSA levels.Methods81 patients with suspected PCa based on increased serum total PSA (TPSA) levels of 4 − 10 ng/mL who underwent transrectal ultrasound/magnetic resonance imaging (MRI)/PET fusion-guided biopsy were enrolled. Among them, patients confirmed by histopathology were divided into the CSPCa group and the non-CSPCa group, and data on PSA concentration, prostate volume (PV), PSA density (PSAD), free PSA (FPSA)/TPSA, Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) score, 68Ga-PSMA PET/CT imaging evaluation results and SUVmax were compared. Multivariate logistic regression analysis was performed to identify the independent predictors for CSPCa, thereby establishing a predictive model based on SUVmax that was evaluated by analyzing the receiver operating characteristic (ROC) curve and decision curve analysis.ResultsCompared to non-CSPCa, CSPCa patients had smaller PVs (median, 31.40 mL), lower FPSA/TPSA (median, 0.12), larger PSADs (median, 0.21 ng/mL2) and higher PI-RADS scores (P < 0.05). The prediction model comprising 68Ga-PSMA PET/CT maximal standardized uptake value, PV and FPSA/TPSA had the highest AUC of 0.927 compared with that of other predictors alone (AUCs of 0.585 for PSA, 0.652 for mpMRI and 0.850 for 68Ga-PSMA PET/CT). The diagnostic sensitivity and specificity of the prediction model were 86.21% and 86.54%, respectively.ConclusionGiven the low diagnostic accuracy of regular PSA tests, a new prediction model based on the 68Ga-PSMA PET/CT SUVmax, PV and FPSA/TPSA was developed and validated, and this model could provide a more satisfactory predictive accuracy for CSPCa. This study provides a noninvasive prediction model with high accuracy for the diagnosis of CSPCa in the PSA gray zone, thus may be better avoiding unnecessary biopsy procedures.

Development, comparison, and validation of four intelligent, practical machine learning models for patients with prostate-specific antigen in the gray zone.

Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier for patients in the prostate-specific antigen gray zone are to be developed and compared, identifying valuable predictors. Predictive models are to be integrated into actual clinical decisions. Patient information was collected from December 01, 2014 to December 01, 2022 from the Department of Urology, The First Affiliated Hospital of Nanchang University. Patients with a pathological diagnosis of prostate hyperplasia or prostate cancer (any PCa) and having a prostate-specific antigen (PSA) level of 4-10 ng/mL before prostate puncture were included in the initial information collection. Eventually, 756 patients were selected. Age, total prostate-specific antigen (tPSA), free prostate-specific antigen (fPSA), fPSA/tPSA, prostate volume (PV), prostate-specific antigen density (PSAD), (fPSA/tPSA)/PSAD, and the prostate MRI results of these patients were recorded. After univariate and multivariate logistic analyses, statistically significant predictors were screened to build and compare machine learning models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier to determine more valuable predictors. Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier exhibit higher predictive power than individual metrics. The area under the curve (AUC) (95% CI), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of the LogisticRegression machine learning prediction model were 0.932 (0.881-0.983), 0.792, 0.824, 0.919, 0.652, 0.920, and 0.728, respectively; of the XGBoost machine learning prediction model were 0.813 (0.723-0.904), 0.771, 0.800, 0.768, 0.737, 0.793 and 0.767, respectively; of the GaussianNB machine learning prediction model were 0.902 (0.843-0.962), 0.813, 0.875, 0.819, 0.600, 0.909, and 0.712, respectively; and of the LGBMClassifier machine learning prediction model were 0.886 (0.809-0.963), 0.833, 0.882, 0.806, 0.725, 0.911, and 0.796, respectively. The LogisticRegression machine learning prediction model has the highest AUC among all prediction models, and the difference between the AUC of the LogisticRegression prediction model and those of XGBoost, GaussianNB, and LGBMClassifier is statistically significant (p < 0.001). Machine learning prediction models based on LogisticRegression, XGBoost, GaussianNB, and LGBMClassifier algorithms exhibit superior predictability for patients in the PSA gray area, with the LogisticRegression model yielding the best prediction. The aforementioned predictive models can be used for actual clinical decision-making.​.

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens.

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

The Combined Effect of Polygenic Risk Score and Prostate Health Index in Chinese Men Undergoing Prostate Biopsy

To date, the combined effect of polygenic risk score (PRS) and prostate health index (phi) on PCa diagnosis in men undergoing prostate biopsy has never been investigated. A total of 3166 patients who underwent initial prostate biopsy in three tertiary medical centers from August 2013 to March 2019 were included. PRS was calculated on the basis of the genotype of 102 reported East-Asian-specific risk variants. It was then evaluated in the univariable or multivariable logistic regression models that were internally validated using repeated 10-fold cross-validation. Discriminative performance was assessed by area under the receiver operating curve (AUC) and net reclassification improvement (NRI) index. Compared with men in the first quintile of age and family history adjusted PRS, those in the second, third, fourth, and fifth quintiles were 1.86 (odds ratio, 95% confidence interval (CI): 1.34-2.56), 2.07 (95%CI: 1.50-2.84), 3.26 (95%CI: 2.36-4.48), and 5.06 (95%CI: 3.68-6.97) times as likely to develop PCa (all p < 0.001). Adjustment for other clinical parameters yielded similar results. Among patients with prostate-specific antigen (PSA) at 2-10 ng/mL or 2-20 ng/mL, PRS still had an observable ability to differentiate PCa in the group of prostate health index (phi) at 27-36 (Ptrend < 0.05) or >36 (Ptrend ≤ 0.001). Notably, men with moderate phi (27-36) but highest PRS (top 20% percentile) would have a comparable risk of PCa (positive rate: 26.7% or 31.3%) than men with high phi (>36) but lowest PRS (bottom 20% percentile positive rate: 27.4% or 34.2%). The combined model of PRS, phi, and other clinical risk factors provided significantly better performance (AUC: 0.904, 95%CI: 0.887-0.921) than models without PRS. Adding PRS to clinical risk models could provide significant net benefit (NRI, from 8.6% to 27.6%), especially in those early onset patients (NRI, from 29.2% to 44.9%). PRS may provide additional predictive value over phi for PCa. The combination of PRS and phi that effectively captured both clinical and genetic PCa risk is clinically practical, even in patients with gray-zone PSA.

Evaluating the Role of Morphological Parameters in the Prostate Transition Zone in PHI-Based Predictive Models for Detecting Gray Zone Prostate Cancer.

The early detection of clinically significant prostate cancer (csPCa) through the integration of multidimensional parameters presents a promising avenue for improving survival outcomes for this fatal disease. This study aimed to assess the contribution of prostate transition zone (TZ) to predictive models based on the prostate health index (PHI), with the goal of enhancing early detection of csPCa in the prostate-specific antigen (PSA) gray zone. In this observational cross-sectional study, a total of 177 PSA gray zone patients (total prostate-specific antigen [tPSA] level ranging from 4.0 to 10.0 ng/mL) were recruited and received PHI detections from August 2020 to March 2022. Prostatic morphologies especially the TZ morphological parameters were measured by transrectal ultrasound (TRUS). Univariable logistic regression indicated prostatic morphological parameters including total prostate volume (PV) indexes and transitional zone volume indexes were all associated with csPCa (P < .05), while the multivariable analysis demonstrated that C-reactive protein (CRP), PHI, PHI density (PHID), and PHI transition zone density (PHI-TZD) were the 4 independent risk factors. The receiver-operating characteristic (ROC) curve analysis suggested that integrated predictive models (PHID, PHI-TZD) yield area under the curves (AUCs) of 0.9135 and 0.9105 in csPCa prediction, which shows a relatively satisfactory predictive capability compared with other predictors. Moreover, the PHI-TZD outperformed PHID by avoiding 30 patients' unnecessary biopsies while maintaining 74.36% specificity at a sensitivity of 90%. Decision-curve analysis (DCA) confirmed the comparable performance of the multivariable full-risk prediction models, without the inclusion of the net benefit, thereby highlighting the superior diagnostic efficacy of PHID and PHI-TZD in comparison with other diagnostic models, in both univariable and multivariable models. Our data confirmed the value of prostate TZ morphological parameters and suggested a significant advantage for the TZ-adjusted PHI predictive model (PHI-TZD) compared with PHI and PHID in the early detection of gray zone csPCa under specific conditions.

MRI-Based Radiomics Nomogram for Predicting Prostate Cancer with Gray-Zone Prostate-Specific Antigen Levels to Reduce Unnecessary Biopsies.

The aim of this study was to establish a predictive nomogram for predicting prostate cancer (PCa) in patients with gray-zone prostate-specific antigen (PSA) levels (4-10.0 ng/mL) based on radiomics and other traditional clinical parameters. In all, 274 patients with gray-zone PSA levels were included in this retrospective study. They were randomly divided into training and validation sets (n = 191 and 83, respectively). Data on the clinical risk factors related to PCa with gray-zone PSA levels (such as Prostate Imaging Reporting and Data System, version 2.1 [PI-RADS V2.1] category, age, prostate volume, and serum PSA level) were collected for all patients. Lesion volumes of interest (VOI) from T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) imaging were annotated by two radiologists. The radiomics model, clinical model, and combined prediction model, which was presented on a nomogram by incorporating the radiomics signature and clinical and radiological risk factors for PCa, were developed using logistic regression. The area under the receiver operator characteristic (AUC-ROC) and decision, calibration curve were used to compare the three models for the diagnosis of PCa with gray-zone PSA levels. The predictive nomogram (AUC: 0.953) incorporating the radiomics score and PI-RADS V2.1 category, age, and the radiomics model (AUC: 0.941) afforded much higher diagnostic efficacy than the clinical model (AUC: 0.866). The addition of the rad score could improve the discriminatory performance of the clinical model. The decision curve analysis indicated that the radiomics or combined model could be more beneficial compared to the clinical model for the prediction of PCa. The nomogram showed good agreement for detecting PCa with gray-zone PSA levels between prediction and histopathologic confirmation. The nomogram, which combined the radiomics score and PI-RADS V2.1 category and age, is an effective and non-invasive method for predicting PCa. Furthermore, as well as good calibration and is clinically useful, which could reduce unnecessary prostate biopsies in patients having PCa with gray-zone PSA levels.

A smart, practical, deep learning-based clinical decision support tool for patients in the prostate-specific antigen gray zone: model development and validation.

Despite efforts to improve screening and early detection of prostate cancer (PC), no available biomarker has shown acceptable performance in patients with prostate-specific antigen (PSA) gray zones. We aimed to develop a deep learning-based prediction model with minimized parameters and missing value handling algorithms for PC and clinically significant PC (CSPC). We retrospectively analyzed data from 18 824 prostate biopsies collected between March 2003 and December 2020 from 2 databases, resulting in 12 739 cases in the PSA gray zone of 2.0-10.0 ng/mL. Dense neural network (DNN) and extreme gradient boosting (XGBoost) models for PC and CSPC were developed with 5-fold cross-validation. The area under the curve of the receiver operating characteristic (AUROC) was compared with that of serum PSA, PSA density, free PSA (fPSA) portion, and prostate health index (PHI). The AUROC values in the DNN model with the imputation of missing values were 0.739 and 0.708 (PC) and 0.769 and 0.742 (CSPC) in internal and external validation, whereas those of the non-imputed dataset were 0.740 and 0.771 (PC) and 0.807 and 0.771 (CSPC), respectively. The performance of the DNN model was like that of the XGBoost model, but better than all tested clinical biomarkers for both PC and CSPC. The developed DNN model outperformed PHI, serum PSA, and percent-fPSA with or without missing value imputation. DNN models for missing value imputation can be used to predict PC and CSPC. Further validation in real-life scenarios are need to recommend for actual implementation, but the results from our study support the increasing role of deep learning analytics in the clinical setting. A deep learning model for PC and CSPC in PSA gray zones using minimal, routinely used clinical parameter variables and data imputation of missing values was successfully developed and validated.

Identification of novel serological autoantibodies in Chinese prostate cancer patients using high-throughput protein arrays.

Autoantibody (AAb) has a prominent role in prostate cancer (PCa), with few studies profiling the AAb landscape in Chinese patients. Therefore, the AAb landscape in Chinese patients was characterized using protein arrays. First, in the discovery phase, Huprot arrays outlined autoimmune profiles against ~ 21,888 proteins from 57 samples. In the verification phase, the PCa-focused arrays detected 25 AAbs selected from the discovery phase within 178 samples. Then, PCa was detected using a backpropagation artificial neural network (BPANN) model. In the validation phase, an enzyme-linked immunosorbent assay (ELISA) was used to validate four AAb biomarkers from 196 samples. Huprot arrays profiled distinct PCa, benign prostate diseases (BPD), and health AAb landscapes. PCa-focused array depicted that IFIT5 and CPOX AAbs could distinguish PCa from health with an area under curve (AUC) of 0.71 and 0.70, respectively. PAH and FCER2 AAbs had AUCs of 0.86 and 0.88 in discriminating PCa from BPD. Particularly, PAH AAb detected patients in the prostate-specific antigen (PSA) gray zone with an AUC of 0.86. Meanwhile, the BPANN model of 4-AAb (IFIT5, PAH, FCER2, CPOX) panel attained AUC of 0.83 among the two cohorts for detecting patients with gray-zone PSA. In the validation cohort, the IFIT5 AAb was upregulated in PCa compared to health (p < 0.001). Compared with BPD, PAH and FCER2 AAbs were significantly elevated in PCa (p = 0.012 and 0.039). We have demonstrated the first extensive profiling of autoantibodies in Chinese PCa patients, identifying novel diagnostic AAb biomarkers, especially for identification of gray-zone-PSA patients.

Modified Prostate Health Index Density Significantly Improves Clinically Significant Prostate Cancer (csPCa) Detection.

BackgroundEarly screening of clinically significant prostate cancer (csPCa) may offer opportunities in revolutionizing the survival benefits of this lethal disease. We sought to introduce a modified prostate health index density (mPHI) model using imaging indicators and to compare its diagnostic performance for early detection of occult onset csPCa within the prostate-specific antigen (PSA) gray zone with that of PHI and PHID.Methods and ParticipationBetween August 2020 and January 2022, a training cohort of 278 patients (total PSA 4.0–10.0 ng/ml) who were scheduled for a prostate biopsy were prospectively recruited. PHI and PHID were compared with mPHI for the diagnosis performance in identifying csPCa. Pathology outcomes from systematic prostate biopsies were considered the gold standard.ResultsThis model was tested in a training cohort consisting of 73 csPCa, 14 non-clinically significant prostate cancer(non-csPCa), and 191 benign prostatic hyperplasia (BPH) samples. In the univariate analysis for the PSA gray zone cohort, for overall PCa, the AUC of mPHI (0.856) was higher than PHI (0.774) and PHID (0.835). For csPCa, the AUC of mPHI (0.859) also surpassed PHI (0.787) and PHID (0.825). For detection of csPCa, compared with lower specificities from PHI and PHID, mPHI performed the highest specificity (76.5%), by sparing 60.0% of unnecessary biopsies at the cost of missing 11 cases of csPCa. The mPHI outperformed PHI and PHID for overall PCa detection. In terms of csPCa, mPHI exceeds diagnostic performance with a better net benefit in decision curve analysis (DCA) compared with PHI or PHID.ConclusionsWe have developed a modified PHI density (mPHI) model that can sensitively distinguish early-stage csPCa patients within the PSA gray zone.Clinical Trial Registration ClinicalTrials.gov, NCT04251546.

Do prostate-specific antigen parameters have a similar role in predicting prostate cancer regardless of serum testosterone levels in men with gray-zone prostate-specific antigen levels?

BackgroundTo evaluate whether various prostate-specific antigen (PSA) parameters have a similar diagnostic value in predicting prostate cancer (PCa) in men with gray-zone PSA levels (4.0–10.0 ng/mL) depending on different serum testosterone levels.MethodsWe retrospectively reviewed the data of 635 men with gray-zone PSA levels who underwent prostate biopsy between January 2015 and December 2019. The study cohort was divided into two groups according to serum testosterone levels: normal (≥300 ng/dL) and low (<300 ng/dL) testosterone. Using the area under the receiver-operating characteristic curve (AUC), we analyzed the diagnostic accuracy of PSA parameters (total PSA, free PSA, free-to-total PSA ratio, testosterone-to-PSA ratio, and PSA density) in predicting PCa and compared the results between the two groups.ResultsThe median age was 68 (range, 40–88) years, and 76.1% (483 of 635) of the men had low testosterone levels. The PCa incidence was higher in the low testosterone group than in the normal testosterone group (45.5% vs. 35.5%, P=0.030). The AUC of free-to-total PSA ratio for predicting PCa showed no difference between the normal and low testosterone groups (AUC 0.616 vs. 0.684, P=0.257). Moreover, total PSA, testosterone-to-PSA ratio, and PSA density showed similar performance in predicting PCa between the two groups.ConclusionsThe analyzed PSA parameters showed a similar diagnostic value in predicting PCa regardless of testosterone levels in men with gray-zone PSA levels.

Urinary hsv2-miR-H9 to hsa-miR-3659 ratio is an effective marker for discriminating prostate cancer from benign prostate hyperplasia in patients within the prostate-specific antigen grey zone.

PurposeTumor microRNAs (miRNAs) are released to biofluids directly or indirectly. Although urinary miRNAs are promising non-invasive biomarkers for the diagnosis of prostate cancer (PCa), their clinical application is challenging for technical reasons. We examined the efficacy of urinary hsv2-miR-H9 to hsa-miR-3659 ratio as a non-invasive diagnostic biomarker of PCa.Materials and MethodsThe expression of urinary miRNAs was quantified by real-time PCR in 116 samples from 53 patients with benign prostatic hyperplasia (BPH) and 63 patients with PCa. The miRNA expression ratio was calculated using an upregulated miRNA (hsv2-miR-H9) as the numerator and a downregulated miRNA (hsa-miR-3659) as the denominator.ResultsThe urinary miR-H9 to miR-3659 ratio was significantly higher in PCa than in BPH controls (p<0.001). The diagnostic accuracy of the urinary miRNA expression ratio was comparable with that of prostate-specific antigen (PSA) (receiver operating characteristic [ROC] curve comparison, p=0.287). The area under the curve for urinary miRNA expression ratio was 0.862 and that for PSA was 0.642 in the “PSA gray zone” (3–10 ng/mL) (ROC curve comparison, p=0.034). The use of the urinary miRNA expression ratio would have prevented 70.6% of unnecessary prostate biopsies; however, 28.6% of PCa cases could be missed in patients within the PSA gray zone.ConclusionsThe expression ratio of urinary miR-H9 to miR-3659 could be a relevant non-invasive biomarker for PCa diagnosis, particularly for patients within the PSA gray zone.

Gri Zondaki Prostat Spesifik Antijen Düzeyleri Olan Erkeklerde Prostat Kanseri Tahmininde Bazı Kan Bazlı Parametrelerin Serum Prostat Spesifik Antijen ve Türevlerine Yardımcı Rolü: Prospektif Klinik Çalışma

Objective: To investigate the adjunctive role of some peripheral blood parameters to serum prostate specific antigen (PSA) in predicting the patients with prostate cancer (PCa) prior to prostate biopsy, and clinically significant PCa among those. Material and Methods: This prospective study included men aged ≥45 years, who were scheduled to undergo a prostate biopsy due to gray-zone PSA levels. The levels of free and total PSA (fPSA and tPSA), total testosterone (TT), PSA density (PSAD), C-reactive protein (CRP), De Ritis ratio (aspartate aminotransferase/alanine transaminase) and hemograms were recorded. Patients were divided into 2 groups as benign prostatic hyperplasia (Group 1) and PCa (Group 2) groups. The PCa group was further divided into 2 subgroups as clinically significant and clinically insignificant PCa. The pre-biopsy values of the variables were compared between the groups. Results: A total of 210 patients were included in the study (Group 1, n=105; Group 2, n=105). The mean age was 65.1±7.4 years, the mean tPSA level was 6.14±2.05 ng/mL, and the mean TT level was 15.46±5.47 nmol/L. Age, prostate volume, the values of fPSA, PSAD, CRP, and the ratios of fPSA/tPSA, CRP/albumin and De Ritis were statistically different between the groups. In the PCa group, only the tPSA level was significantly different between the subgroups (p=0.005). The area under curve and the cut-off value for tPSA to predict clinically significant PCa were 0.669 and 5.8 ng/mL, respectively. Conclusion: CRP, the CRP/albumin ratio and De Ritis ratio may further help predict a diagnosis of PCa.

Alpha-L-Fucosidase Has Diagnostic Value in Prostate Cancer With "Gray-Zone PSA" and Inhibits Cancer Progression via Regulating Glycosylation.

BackgroundThis study aimed to explore the diagnostic value of alpha-l-fucosidase (AFU) in prostate cancer (PCa) patients with “gray-zone PSA” and to investigate the correlation between AFU expression and clinicopathological characteristics of PCa patients.MethodsThe level of AFU and other necessary clinicopathological variables of patients were retrieved from electronic medical records. The transcriptome profiling and clinical information of PCa patients were obtained from The Cancer Genome Atlas (TCGA) database. The protein level of AFU in tissue was assessed by immunohistochemistry (IHC). All the data were processed by appropriate analysis methods. The p-value of <0.05 was considered statistically significant.ResultsAFU showed ideal diagnostic value for PCa with prostate-specific antigen (PSA) levels ranging from 4 to 10 ng/ml, and its optimal cutoffs were 19.5 U/L. Beyond this, low AFU expression was associated with high pathological grade, T stage and N stage, more postoperative residual tumors, and poor primary therapy outcome, as well as shorter progression-free interval. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis illustrated that FUCA1/FUCA2 exerted tumor-suppressive function by regulating the glycosylation.ConclusionsAFU (<19.5 U/L) could effectively distinguish the PCa from the patients with “gray-zone PSA”, and low expression of AFU was an independent unfavorable predictor for the clinicopathological characteristics of PCa patients.