Free Prostate-specific Antigen Research Articles

Unveiling the intricate connection between per- and polyfluoroalkyl substances and prostate hyperplasia

The presence of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in various everyday products has raised concerns about their potential impact on prostate health. This study aimed to investigate the effects of different types of PFAS on prostate health, including PFDeA, PFOA, PFOS, PFHxS, and PFNA. To assess the relationship between PFAS exposure and prostate injury, machine learning algorithms were employed to analyze prostate-specific antigen (PSA) metrics. The analysis revealed a linear and positive dose-dependent association between PFOS and the ratio of free PSA to total PSA (f/tPSA). Non-linear dose-response relationships were observed between the other four types of PFAS and the f/tPSA ratio. Additionally, the analysis showed a positive association between the mixture of PFAS and prostate hyperplasia, with PFNA having the highest impact followed by PFOS. These findings suggest that elevated serum levels of PFDeA, PFOA, PFOS, and PFNA are linked to prostate hyperplasia. Therefore, this study utilized advanced machine learning techniques to uncover potential hazardous effects of PFAS exposure on prostate health, specifically the positive association between PFAS and prostate hyperplasia.

Presentation of the results of free PSA determinate by immulite 2000 HPI analuzer in 2023 year in pho clinical Hospital Bitola, North Macedonia

Presentation of the results of free PSA determinate by immulite 2000 HPI analuzer in 2023 year in pho clinical Hospital Bitola, North Macedonia

Evaluation of the relationship between prostate volume and prostate specific antigen in patients diagnosed with benign prostatic hyperplasia

Aims: The aim of this study was to investigate the relationship between prostate volume and prostate specific antigen (PSA) levels in patients diagnosed with benign prostatic hyperplasia (BPH). In addition, it is aimed to show that BPH may be one of the causes of PSA elevation in patients without prostate cancer. Methods: Prostate volume was calculated in 60 BPH patients who underwent prostate magnetic resonance imaging (MRI) imaging in radiology department between December 1, 2023 and March 1, 2024 due to elevated PSA. The relationship between prostate volume and the tpsa and fpsa values of the patients in the last month was evaluated. Results: There was a positive correlation between age and total prostate specific antigen (tpsa) and free prostate specific antigen (fpsa) in patients with benign prostatic hyperplasia. However, the correlation between age and fpsa was stronger. Similarly, a positive correlation was found between prostate volume and tpsa and fpsa. The correlation between increased prostate volume and increased fpsa was also stronger. Conclusion: In our study, a positive correlation was found between prostate volume and tpsa and fpsa in patients diagnosed with BPH. It was determined that one of the reasons for the increase in PSA values may be the increase in prostate volume.

Lectin-nanoparticle concept for free PSA glycovariant providing superior cancer specificity

BackgroundUsing lectins to target cancer-associated modifications of PSA glycostructure for identification of clinically significant prostate cancers, e.g., Gleason score (GS) ≥ 7, from benign and indolent cancers (GS 6), is highly promising yet technically challenging. From previous findings to quantify increased PSA fucosylation in urine, we set out to construct a robust, specific test concept suitable for plasma samples. MethodsMacrophage galactose‐binding lectin (MGL) coupled to 100 nm Eu3 + -nanoparticles was used to probe PSA captured from cancer cell lines, seminal plasma, and plasma samples from 249 patients with a clinical suspicion of prostate cancer onto 3 mm dense spots of free PSA antibody fab fragments. Results were compared to four kallikrein tests: tPSA, fPSA, iPSA and hK2. ResultsThe fPSAMGLglycovariant provided superior discrimination of the GS ≥ 7 and benign + GS 6 groups (p 0.0003) compared to fPSA (NS). The corresponding AUC in ROC analysis was 0.70 compared to 0.66 for tPSA. In contrast to all four kallikrein tests, the fPSAMGLGV was independent of prostate gland volume. Using a logistic regression analysis the fPSAMGLGV significantly improved on the four-kallikrein model. ConclusionsDue to Eu-nanoparticles and a dense fPSA capture spot, the fPSAMGL glycovariant identifies an fPSA subform with the highest cancer specificity compared to the four conventional kallikreins.

Exploration and validation of optimal cut-off values for tPSA and fPSA/tPSA screening of prostate cancer at different ages

Objective: To determine the total and age-specific cut-off values of total prostate specific antigen (tPSA) and the ratio of free PSA divided total PSA (fPSA/tPSA) for screening prostate cancer in China. Methods: Based on the Chinese Colorectal, Breast, Lung, Liver, and Stomach cancer Screening Trial (C-BLAST) and the Tianjin Common Cancer Case Cohort (TJ4C), males who were not diagnosed with any cancers at baseline since 2017 and received both tPSA and fPSA testes were selected. Based on Cox regression, the overall and age-specific (＜60, 60-＜70, and ≥70 years) accuracy and optimal cut-off values of tPSA and fPSA/tPSA ratio for screening prostate cancer were evaluated with time-dependent receiver operating characteristic curve (tdROC) and area under curve (AUC). Bootstrap resampling was used to internally validate the stability of the optimal cut-off value, and the PLCO study was used to externally validate the accuracy under different cut-off values. Results: A total of 5 180 participants were included in the study, and after a median follow-up of 1.48 years, a total of 332 prostate cancer patients were included. In the total population, the tdAUC of tPSA and fPSA/tPSA screening for prostate cancer were 0.852 and 0.748, respectively, with the optimal cut-off values of 5.08 ng/ml and 0.173, respectively. After age stratification, the age specific cut-off values of tPSA in the <60, 60-<70, and ≥70 age groups were 3.13, 4.82, and 11.54 ng/ml, respectively, while the age-specific cut-off values of fPSA/tPSA were 0.153, 0.135, and 0.130, respectively. Under the age-specific cut-off values, the sensitivities of tPSA screening for prostate cancer in males <60, 60-70, and ≥70 years old were 92.3%, 82.0%, and 77.6%, respectively, while the specificities were 84.7%, 81.3%, and 75.4%, respectively. The age-specific sensitivities of fPSA/tPSA for screening prostate cancer were 74.4%, 53.3%, and 55.9%, respectively, while the specificities were 83.8%, 83.7%, and 83.7%, respectively. Both bootstrap's internal validation and PLCO external validation provided similar results. The combination of tPSA and fPSA/tPSA could further improve the accuracy of screening. Conclusion: To improve the screening effects, it is recommended that age-specific cut-off values of tPSA and fPSA/tPSA should be used to screen for prostate cancer in the general risk population.

Effects of occupational exposure on the hematologic parameters among welders

Introduction: The effect of prolonged exposure to welding fumes on blood levels remains an unresolved question. In our study, we aimed to determine the effects of metal fume exposure on various blood parameters in welders. Materials and Methods: The study was cross-sectional. It included all male welding workers admitted to a health institution, a reference hospital for occupational diseases, for 2021. It was conducted with 254 individuals. The variables examined included age, duration of employment, smoking habit, body mass index, hemoglobin levels, erythrocyte, leukocyte and platelet counts, aspartate aminotransferase, alanine aminotransferase, free prostate-specific antigen, creatinine and manganese levels, and erythrocyte sedimentation rates. Results: In our study, 40.6% of the welders were between 21-30 years old. 65.7% of the participants have been welding for more than five years. According to body-mass index values, 44.1% of the participants were pre-obese, and 16.9% were obese. 63.8% of the participants were smokers. In 35.8% of participants, hyperglycemia was present, and polycythemia was present in 27.2%. When blood parameters were analyzed, fasting blood glucose, aspartate transaminase, alanine transaminase, and sedimentation rate were lower in those who worked less than five years than those who worked five years or more. The difference was statistically significant. In blood test results, leukocyte, glucose, aspartate transaminase, alanine transaminase, and sedimentation rate values in welders were correlated with total working time. Conclusion: Welders in our study had a high prevalence of smoking, overweight, and obesity. Working time is correlated with liver enzyme levels and fasting blood glucose values. For healthier workers, employers should fulfill their responsibilities for occupational health and safety.

Blood-Based DNA Methylation Analysis by Multiplexed OBBPA-ddPCR to Verify Indications for Prostate Biopsies in Suspected Prostate Cancer Patients.

Current prostate carcinoma (PCa) biomarkers, including total prostate-specific antigen (tPSA), have unsatisfactory diagnostic sensitivity and specificity resulting in overdiagnosis and overtreatment. Previously, we described an optimised bias-based preamplification-digital droplet PCR (OBBPA-ddPCR) technique, which detects tumour DNA in blood-derived cell-free DNA (cfDNA) of cancer patients. The current study investigated the performance of newly developed OBBPA-ddPCR-based biomarkers. Blood plasma samples from healthy individuals (n = 90, controls) and PCa (n = 39) and benign prostatic hyperplasia patients (BPH, n = 40) were analysed. PCa and BPH patients had tPSA values within a diagnostic grey area of 2-15 ng/mL, for whom further diagnostic validation is most crucial. Methylation levels of biomarkers RASSF1A, MIR129-2, NRIP3, and SOX8 were found significantly increased in PCa patients compared to controls. By combining classical PCa risk factors (percentage of free PSA compared to tPSA (QfPSA) and patient's age) with cfDNA-based biomarkers, we developed PCa risk scores with improved sensitivity and specificity compared to established tPSA and QfPSA single-marker analyses. The diagnostic specificity was increased to 70% with 100% sensitivity for clinically significant PCa patients. Thus, prostate biopsies could be avoided for 28 out of 40 BPH patients. In conclusion, the newly developed risk scores may help to confirm the clinical decision and prevent unnecessary prostate biopsy.

An approach to diagnosis of prostate cancer using fuzzy logic

&lt;p&gt;&lt;span&gt;Early diagnosis of cancers is a major requirement for patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. The recognition of cancer diseases mostly operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system (FES) and its implementation for the detection of prostate cancer. Specifically, prostate-specific antigen (PSA), prostate volume (PV), age, and percentage free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while PCR serves as an output parameter. Mamdani fuzzy inference method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to determine whether their patients need a biopsy. This system is fast as it requires minimum calculation and hence comparatively less time which reduces mortality and morbidity and is more reliable than other economic systems and can be frequently used by doctors.&lt;/span&gt;&lt;/p&gt;

Preanalytical stability of molecular forms of prostate-specific antigen in serum samples (PSA, free PSA, [-2]proPSA) and their impact on fPSA/tPSA ratio and PHI.

Prostate cancer is a common cancer in men. Detection methods include the measurement of biomarkers: prostate-specific antigen (PSA), free PSA, [-2]proPSA, and the calculated indices: fPSA/tPSA ratio and Prostate Health Index (PHI). Proper preanalytical conditions are crucial for precise measurement and failure to adhere to protocols or regulations can influence the diagnostic algorithm. We assessed the stability of the above-mentioned biomarkers, fPSA/tPSA ratio and PHI, under various pre-analytical conditions. Serum samples from 45 males were collected and stored under specific conditions before tPSA, fPSA, and [-2]proPSA were measured. Subsequently, the fPSA/tPSA and PHI were calculated. tPSA, fPSA, and [-2]proPSA remained stable during the two freeze-thaw cycles. Storage at 4°C and 22°C resulted in stable tPSA concentrations. However, fPSA levels decreased and [-2]proPSA levels increased over time. The fPSA/tPSA ratio remained stable for 72 h, at which point a decrease was observed in the samples kept at 4°C and 22°C. A gradual increase in PHI was observed in the samples kept at 4°C and 22°C. All biomarkers remained stable during two freeze-thaw cycles. tPSA was the most stable analyte when stored at 4°C, as well as at RT. A gradual increase of [-2]proPSA and a slight decrease in fPSA were observed during the storage test. This led to a decrease in the fPSA/tPSA ratio and an elevation in the PHI. We therefore recommend measuring prostate biomarkers promptly following blood collection. Understanding the pre-analytical stability of prostate biomarkers helps prevent false positive results and improve the accuracy of diagnostics for prostate cancer.

Stability of ten serum tumor markers after one year of storage at-18°C.

The storage of serum tumor markers (STM) at-18 °C for one year has been a legal requirement in France since 1999, but has been abolished in 2022. This raises the question of the relevance of maintaining these biobanks in terms of conditions of storage. These should only be implemented after validation; in order to maintain the integrity of the biological sample and must be controlled over time according to the laboratory's procedures. The aim of the study was to assess the suitability of storing 10 STMs by evaluating their stability after one year of storage at-18 °C. A new immuno-enzymatic assay (A+1) was conducted on samples that had been stored at-18 °C for one year after an initial assay (A)of one of the following STMs: carcino-embryonic antigen (CEA), alpha-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carbohydrate antigen 15-3 (CA15-3), carbohydrate antigen 19-9 (CA19-9), total (TPSA), and free (FPSA) prostate-specific antigen, calcitonin (CT), thyroglobulin (TG), and neuro-specific enolase (NSE). The results were confronted to four different permissible error sources. In total, 1148 A+1 assays were performed. A strong correlation between A+1 and A values was found for all analytes, but with a statistically significant reduction in the mean A+1 concentration compared to the mean A concentration in 7/10 STMs. The bias induced by conservation seems to be technically unsustainable if we rely on the repositories closest to the current analytical performances. These results support the discontinuation of mandatory STM biobank storage at-18 °C, which requires considerable technical time and organizational effort.

Non-Invasive Diagnosis of Prostate Cancer and High-Grade Prostate Cancer Using Multiparametric Ultrasonography and Serological Examination

ObjectivesThis study aimed to assess the efficacy of multiparametric ultrasonography (mpUS) combined with serological examination, as a non-invasive method, in detecting prostate cancer (PCa) or high-grade prostate cancer (HGPCa) respectively. MethodsA cohort of 245 individuals with clinically suspected PCa were enrolled. All subjects underwent a comprehensive evaluation, including basic data collection, serological testing, mpUS and prostate biopsy. Random Forest (RF) models were developed, and the mean area under the curve (AUC) in 100 cross-validations was used to assess the performance in distinguishing PCa from HGPCa. ResultsmpUS features showed significant differences (p < 0.001) between the PCa and non-PCa groups, as well as between the HGPCa and low-grade prostate cancer (LGPCa) groups including prostate-specific antigen density (PSAD), transrectal real-time elastography (TRTE) and intensity difference (ID). The RF model, based on these features, demonstrated an excellent discriminative ability for PCa with a mean area under the curve (AUC) of 0.896. Additionally, another model incorporating free prostate-specific antigen (FPSA) and color Doppler flow imaging (CDFI) achieved a high accuracy in predicting HGPCa with a mean AUC of 0.830. The nomogram derived from these models exhibited excellent individualized prediction of PCa and HGPCa. ConclusionThe RF models incorporating mpUS and serological variables achieved satisfactory accuracies in predicting PCa and HGPCa.

Combined measurement of serum zinc with PSA ameliorates prostate cancer screening efficiency via support vector machine algorithms

BackgroundEarly screening of prostate cancer (PCa) is pivotal but challenging in the clinical scenario due to the phenomena of false positivity or false negativity of some serological evaluations, e.g. PSA testing. Decline of serum Zn2+ levels in PCa patients reportedly plays a crucial role in early screening of PCa. Accordingly, we combined 4 indices comprising the serum levels of total PSA (tPSA), free PSA (fPSA), Zn2+ and demographic information (especially age) in order to ameliorate the efficacies of PCa screening with support vector machine (SVM) algorithms. MethodsA total of 858 male patients with prostate disorders and 345 healthy male controls were enrolled. Patients’ data included 4 variables and serum Zn2+ was quantified via a self-invented Zn2+ responsive AIE-based fluorescent probe as previously published. tPSA and fPSA were routinely determined by a chemiluminescent method. Mathematical simulations were conducted to establish a SVM model for the combined diagnostics with the four variables. Moreover, ROC and its characteristic AUC were also employed to evaluate the classification efficacy of the model. Sigmoid function was utilized to estimate corresponding probabilities of classifying the clinical subjects as per 5 grades, which were incorporated into our established prostate index (PI) stratification system. ResultsIn SVM model, the mean AUC of the ROC with the quartet of variables was approximately 84% for PCa diagnosis, whereas the mean AUC of the ROCs with tPSA, fPSA, [Zn2+] or age alone was 64%, 62%, 55% and 59%, respectively. We further established an integrated prostate index (PI) stratification system with 5 grades and a software package to support clinicians in predicting PCa, with the accuracy of our risk stratification system being 83.3%, 91.6% and 83.3% in predicting normal, benign and PCa cases in corresponding groups. Follow-up findings especially MRI results and PI-RADS scores supported the reliability of this stratification platform as well. ConclusionFindings from our present study demonstrated that index combination via SVM algorithms may well facilitate clinicians in early differential screening of PCa. Meanwhile, our established PI stratification system based on SVM model and Sigmoid function provided substantial accuracy in preclinical risk prediction of developing prostate cancer.

The relationship between methylation of tumor suppressor genes &lt;i&gt;APC&lt;/i&gt;, &lt;i&gt;GSTP1&lt;/i&gt;, &lt;i&gt;RASSF1A&lt;/i&gt; and content of prostate-specific antigen- associated markers in prostate cancer diagnosis

Despite the improvement of prostate cancer (PCa) diagnostic methods and the introduction of prostate-specific antigen (PSA) monitoring, the incidence of advanced PCa in Russia remains high. New more informative variables are needed for the effective diagnosis of early stage and high-grade PCa. In the field of epigenetics of special interest is the detection of DNA hypermethylation, which can serve as a PCa biomarker, since it is common to find and may induce a stable appropriate gene silencing, leading to significant cell changes. Aim of the study was to analyze the relationship between methylation of the APC, GSTP1 and RASSF1A genes and total PSA and prostate health index (PHI) in PCa. Material and methods. The present study included 54 patients with suspicion of PCa, up to 75-years old, who had an initial total PSA level from 2.5 to 10 ng/ml. To calculate PHI value the concentrations of total PSA, free PSA and pre-mature form of PSA ([–2]proPSA) in blood serum were measured by chemiluminescence immunoassay. Results and discussion. The study has revealed the statistically significant correlation between the degree of methylation in blood plasma samples and biopsy material of only the GSTP1 gene and PSA-associated markers. We found an increase of parameters contingency as Gleason score increased. Conclusions. As a result of this work, statistically significant direct correlations were identified between changes in methylation patterns of the promoter region of the GSTP1 gene and PHI, which allows us to consider them as potential candidates for inclusion in a diagnostic panel for more effective early detection of prostate cancer.

Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection

PurposeTo construct machine learning models based on radiomics features combing conventional transrectal ultrasound (B-mode) and contrast-enhanced ultrasound (CEUS) to improve prostate cancer (PCa) detection in peripheral zone (PZ).MethodsA prospective study of 166 men (72 benign, 94 malignant lesions) with targeted biopsy-confirmed pathology who underwent B-mode and CEUS examinations was performed. Risk factors, including age, serum total prostate-specific antigen (tPSA), free PSA (fPSA), f/t PSA, prostate volume and prostate-specific antigen density (PSAD), were collected. Time-intensity curves were obtained using SonoLiver software for all lesions in regions of interest. Four parameters were collected as risk factors: the maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (MTT). Radiomics features were extracted from the target lesions from B-mode and CEUS imaging. Multivariable logistic regression analysis was used to construct the model.ResultsA total of 3306 features were extracted from seven categories. Finally, 32 features were screened out from radiomics models. Five models were developed to predict PCa: the B-mode radiomics model (B model), CEUS radiomics model (CEUS model), B-CEUS combined radiomics model (B-CEUS model), risk factors model, and risk factors-radiomics combined model (combined model). Age, PSAD, tPSA, and RT were significant independent predictors in discriminating benign and malignant PZ lesions (P < 0.05). The risk factors model combing these four predictors showed better discrimination in the validation cohort (area under the curve [AUC], 0.84) than the radiomics images (AUC, 0.79 on B model; AUC, 0.78 on CEUS model; AUC, 0.83 on B-CEUS model), and the combined model (AUC: 0.89) achieved the greatest predictive efficacy.ConclusionThe prediction model including B-mode and CEUS radiomics signatures and risk factors represents a promising diagnostic tool for PCa detection in PZ, which may contribute to clinical decision-making.

Potential diagnostic value of multiple indicators combined with total prostate-specific antigen in prostate cancer.

We aimed to investigate the diagnostic value of different laboratory indicators in combination with total prostate-specific antigen (TPSA) for prostate cancer (PCa). In this retrospective study, we selected 291 patients who underwent prostate biopsy. Patients were divided into the benign prostatic hyperplasia group and the PCa group. In both groups, patients were again divided into a group with TPSA 4.0-10.0 ng/mL and a group with TPSA >10.0 ng/mL. Clinical data including age, pre-puncture TPSA, free prostate-specific antigen (FPSA), and prostate volume (PV) were collected from all patients. We calculated the metrics PSA/PV (prostate-specific antigen density, PSAD), age/PV (AVR), age × PV/TPSA (PSA-AV), and (FPSA/TPSA)/PSAD [(F/T)/PSAD]). We plotted receiver operating characteristic (ROC) curves and calculated the area under the ROC curve (AUC). We found statistically significant differences in PV, PSAD, AVR, PSA-AV, and (F/T) PSAD for patients with TPSA 4.0-10.0 ng/mL and TPSA >10 ng/mL. We further plotted the ROC of individual or combined indices in different subgroups and calculated the AUC. We found that the diagnostic efficacy of the combined indices was higher with TPSA >10 ng/mL. The combination of TPSA with multiple indicators may improve diagnostic accuracy for PCa.

Patient-related characteristics predict prostate cancers in men with PI-RADS 4-5 to further optimize the diagnostic performance of MRI.

To develop a prediction model based on patient-related characteristics for detecting prostate cancer (PCa) in patients with Prostate Imaging Reporting and Data System (PI-RADS) 4-5 in multiparametric magnetic resonance imaging (mp-MRI), aiming to optimize pre-biopsy risk stratification in MRI. The patient-related characteristics including the lesion location, age, prostate-specific antigen (PSA), free prostate-specific antigen (fPSA), fPSA/PSA, prostate-specific antigen density (PSAD) and body mass index (BMI) were collected for patients who underwent mp-MRI and prostate biopsy between February 2014 and October 2022. Univariate and multivariate logistic regression analyses were conducted to select independent predictors of PCa and further create a prediction model. The diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Moreover, sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) were also calculated. A total of 833 patients were included in this study. In the subgroup PI-RADS 4, the independent characteristics of lesion location, age, fPSA/PSA and PSAD were selected to create the prediction model with an AUC of 0.748 (95% CI 0.694-0.803), sensitivity of 61.88%, specificity of 85.32%, PPV of 92.52%, and NPV of 43.26%. Besides, the prediction model in PI-RADS 5 was created using PSA and PSAD with an AUC of 0.893 (95% CI 0.844-0.941), sensitivity of 81.40%, specificity of 84.85%, PPV of 98.37% and NPV of 28.87%. The patient-related clinical characteristics were significant predictors of PCa and the prediction model based on selected characteristics could achieve a medium risk prediction of PCa in PI-RADS 4-5.

B-376 Performance of Pathological Anatomy Immunohistochemical Markers and Serum Tumor Markers in Prostate Cancer

Abstract Background The gold standard method for diagnosing prostate cancer is systematic prostate biopsy under ultrasound guidance and local anesthesia.Serum tumor marker tests (PSA, Free PSA and testosterone) and confirmatory or classificatory immunohistochemical (IHC) complement the conclusion of the patient’s report. The maximum amount of information regarding the pathological anatomy and markers provided to physicians favors assertive decision-making in the conduct of treatment. However, the results of prostate biopsies after treatment with prostatectomy can differ significantly in the course of the disease. In this context, the authors’ objectives were to analyze the performance of the dosages of these widely used serum markers against the IHC markers of biopsies, as well as their indicators of positive and negative predictive values (PPV and NPV) for prostatic neoplasms. The authors analyzed the level of agreement between pathological prostate biopsy results and immunohistochemical (IHC) testing for 34ßE12, p63, and alpha-methylacyl-CoA racemase (AMACR) to provide final pathological results for prostate cancer, as well as their relationship with data provided by biochemical tests for the markers PSA, Free PSA and Testosterone. Methods Descriptive, retrospective study with a qualitative and quantitative approach, based on data obtained through IHC exams and available in a database of a large laboratory in São Paulo—Brazil. Data collection was based on anatomopathological results of male patients diagnosed with prostate cancer, aged between 40 and 69 years between August 2021 and December 2022 with a total of 1403 analyzes for biopsies using IHC combined markers such as 34ßE12 , p63 and AMACR, and a total of 144 analyzes for combined prostatic serum markers (total PSA, free PSA and testosterone). All analyzes were conducted using the R software version 4.1.0 (R CORE TEAM, 2021) and considered a significance level (α) of 5%. Results The Mann-Whitney test indicated that the distribution of the PSA variable (ng/dL) does not differ statistically between the 34ßE12 groups (W = 822.0; P = 0.224; r = 0.139 , p63 (W = 826.0; P = 0.361 ; r = 0.103), and AMACR (W = 610.0; P = 0.351; r = -0.106). Likewise, the Free PSA marker (ng/dL) did not differ statistically between 34ßE12 (W = 309.5; P = 0.473 ; r = -0.097), p63 (W = 319.5; P = 0.488; r = - 0.093 and AMACR (W = 372.0; P = 0.725; r = 0.048). The results of the ROC curve with PSA (ng/dL) as predictor of the biopsy result with the marker 34ßE12, p63 and AMACR the mean positive predictive values (MPVP) were 0.623 , 0.635 and 0.615 respectively. For Free PSA (ng/dL) with the markers respectively, the VPPM were 0.743, 0.750 and 0.455. As for testosterone, our sample was very small. Conclusion Our findings showed that all markers are important indicators for the diagnosis and monitoring of prostate cancer and that they complement each other. Free PSA showed good PPV for 34ßE12 and p63 markers and low PPV for AMACR.

B-042 Evaluation of the Analytical Performance of Three Prostate-specific Antigen Immunoassays on the Atellica CI 1900 Analyzer

Abstract Background The Atellica® CI 1900 Analyzer is an automated, mid-throughput, integrated chemistry and immunoassay analyzer employing both Atellica CH and Atellica IM assays. This study was designed to evaluate the analytical performance of the Atellica IM Prostate-Specific Antigen (PSA)*, Complexed Prostate-Specific Antigen (cPSA)*, and Free Prostate-Specific Antigen (fPSA)* assays on the Atellica CI 1900 Analyzer. Methods The Atellica CI 1900 PSA, cPSA, and fPSA assays use the same reagents and calibrators as the comparable Atellica IM assays. Limit of quantitation (LoQ), precision, and method comparison (MC) were used as performance indicators for the Atellica CI 1900 Analyzer. LoQ studies were performed in accordance with CLSI EP17-A2 using native and contrived human serum samples. Precision studies were performed according to CLSI EP05-A3 using contrived human serum samples. One aliquot of each sample pool was tested in duplicate in two runs per day &amp;gt;2 hours apart on each analyzer for &amp;gt;20 days. MC studies were performed according to CLSI EP09-A3. Individual native human serum samples were analyzed using the Atellica IM assays on both the Atellica IM and Atellica CI 1900 Analyzers. Results LoQ is defined as the lowest amount of analyte in a sample at which the within-laboratory %CV is ≤20%. Representative precision and MC results for each assay across indicated sample ranges are listed in the table. Over the three assays tested, repeatability and within-lab %CVs were ≤8.0% and ≤15.5%, respectively. Slopes determined by the Deming linear regression model were approximately equal to 1. Conclusion Evaluation of the Atellica IM PSA, cPSA, and fPSA assays using the Atellica CI 1900 Analyzer demonstrated good precision and equivalent performance compared to the same assays on the Atellica IM Analyzer. *The products/features mentioned here are not commercially available in all countries. Their future availability cannot be guaranteed. fPSA assay is not available for sale in US.

Development and evaluation of the MiCheck® Prostate test for clinically significant prostate cancer

BackgroundThere is a clinical need to identify patients with an elevated PSA who would benefit from prostate biopsy due to the presence of clinically significant prostate cancer (CSCaP). We have previously reported the development of the MiCheck® Test for clinically significant prostate cancer. Here, we report MiCheck's further development and incorporation of the Roche Cobas standard clinical chemistry analyzer. ObjectivesTo further develop and adapt the MiCheck® Prostate test so it can be performed using a standard clinical chemistry analyzer and characterize its performance using the MiCheck-01 clinical trial sample set. Design, settings, and participantsAbout 358 patient samples from the MiCheck-01 US clinical trial were used for the development of the MiCheck® Prostate test. These consisted of 46 controls, 137 non-CaP, 62 non-CSCaP, and 113 CSCaP. MethodsSerum analyte concentrations for cellular growth factors were determined using custom-made Luminex-based R&D Systems multi-analyte kits. Analytes that can also be measured using standard chemistry analyzers were examined for their ability to contribute to an algorithm with high sensitivity for the detection of clinically significant prostate cancer. Samples were then re-measured using a Roche Cobas analyzer for development of the final algorithm. Outcome measurements and statistical analysisLogistic regression modeling with Monte Carlo cross-validation was used to identify Human Epidydimal Protein 4 (HE4) as an analyte able to significantly improve the algorithm specificity at 95% sensitivity. A final model was developed using analyte measurements from the Cobas analzyer. ResultsThe MiCheck® logistic regression model was developed and consisted of PSA, %free PSA, DRE, and HE4. The model differentiated clinically significant cancer from no cancer or not-clinically significant cancer with AUC of 0.85, sensitivity of 95%, and specificity of 50%. Applying the MiCheck® test to all evaluable 358 patients from the MiCheck-01 study demonstrated that up to 50% of unnecessary biopsies could be avoided while delaying diagnosis of only 5.3% of Gleason Score (GS) ≥3+4 cancers, 1.8% of GS≥4+3 cancers and no cancers of GS 8 to 10. ConclusionsThe MiCheck® Prostate test identifies clinically significant prostate cancer with high sensitivity and negative predictive value (NPV). It can be performed in a clinical laboratory using a Roche Cobas clinical chemistry analyzer. The MiCheck® Prostate test could assist in reducing unnecessary prostate biopsies with a marginal number of patients experiencing a delayed diagnosis.

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.