Consistent predictive ability of prostate-specific antigen density prediction model for clinically significant prostate cancer across age strata.

BACKGROUND: Prebiopsy prostate-specific antigen density (PSAD) is a well-known predictor of clinically significant prostate cancer (csPCa). Since prostate-specific antigen (PSA) and prostate volume (PV) increase normally with aging, PSAD thresholds may vary. The purpose of the study was to determine if PSAD was predictive of csPCa in different age strata. METHODS: We retrospectively reviewed our institutional database for patients who underwent multiparametric magnetic resonance imaging (MRI) between January 2016 and December 2021. We included patients who had post-MRI prostate biopsies. Based on age, we divided our cohort into four subgroups (groups 1-4): <55, 55-64, 65-74, and ≥75 years old. PSAD accuracy was estimated by the area under the curve (AUC) as a predictive model for differentiating csPCa between the groups. CsPCa was defined as a Gleason Grade Group 2 or higher. Three different PSAD thresholds (0.1, 0.15, and 0.2) were tested across the groups for sensitivity, specificity, and positive predictive value (PPV) and negative predictive value (NPV). Chi-square and analysis of variance tests were used for bivariate analysis. All analys were completed using R 4.3 (R Core Team, 2023). RESULTS: Among 1913 patients, 883 (46.1%) had prostate biopsies. In groups 1, 2, 3, and 4, there were 62 (7%), 321 (36.4%), 404 (45.8%), and 96 (10.9%) patients, respectively. Median PSA was 5.6 (interquartile range 3.4-8.1), 6.2 (4.8-9), 6.8 (5.1-9.7), and 9 (5.6-13), respectively (p < 0.01). Median PV was 42.3 (30-62), 51 (36-77), 55.5 (38-85.9), and 59.3 (42-110) mL, respectively (p < 0.01). No difference was observed in median PSAD between age groups 1-4 (0.1 [0.07-0.16], 0.11 [0.08-0.18], 0.1 [0.07-0.19], and 0.1 [0.07-0.2]), respectively (p = 0.393). CsPCa was diagnosed in 241 (27.3%) patients, of which 10 (16.1%), 65 (20.2%), 121 (30%), and 45 (46.7%) were in groups 1-4, respectively (p < 0.001). For groups 1-4, the PSAD AUC for predicting csPCa was 0.75, 0.68, 0.71, and 0.74. While testing PSAD threshold of 0.15 across the different age groups (1-4), the PPV vs. NPV was 39.1 vs. 93.2, 33.6 vs. 87, 50.9 vs. 80.8, and 66.1 vs. 64.7, respectively. CONCLUSIONS: PSAD prediction model was found to be similar among different age groups. In young patients, PSAD had a high NPV but low PPV. With increasing age, the opposite trend was observed, likely due to higher disease prevalence. While PSAD thresholds may be less useful in older patients to rule out higher-grade prostate cancer, the clinical consequences of these diagnoses require a case-by-case evaluation.

Risk factor analysis and optimal cutoff value selection of PSAD for diagnosing clinically significant prostate cancer in patients with negative mpMRI: results from a high-volume center in Southeast China.

BACKGROUND: Multi-parametric magnetic resonance imaging (mpMRI) is a diagnostic tool used for screening, localizing, and staging prostate cancer. Patients with Prostate Imaging Reporting and Data System (PI-RADS) score of 1 and 2 are considered negative mpMRI, with a lower likelihood of detecting clinically significant prostate cancer (csPCa). However, relying solely on mpMRI is insufficient to completely exclude csPCa, necessitating further stratification of csPCa patients using biomarkers. METHODS: A retrospective study was conducted on mpMRI-negative patients who underwent prostate biopsy at the First Affiliated Hospital of Zhejiang University from January 2022 to June 2023. A total of 607 patients were included based on inclusion and exclusion criteria. Univariate and multivariate logistic regression analysis were performed to identify risk factors for diagnosing csPCa in patients with negative mpMRI. Receiver Operating Characteristic (ROC) curves were plotted to compare the discriminatory ability of different Prostate-Specific Antigen Density (PSAD) cutoff values for csPCa. RESULTS: Among the 607 patients with negative mpMRI, 73 patients were diagnosed with csPCa. In univariate logistic regression analysis, age, PSA, f/tPSA, prostate volume, and PSAD were all associated with diagnosing csPCa in patients with negative mpMRI (P < 0.05), with PSAD being the most accurate predictor. In multivariate logistic regression analysis, f/tPSA, age, and PSAD were independent predictors of csPCa (P < 0.05). PSAD cutoff value of 0.20 ng/ml/ml has better discriminatory ability for predicting csPCa and is a significant risk factor for csPCa in multivariate analysis. CONCLUSION: Age, f/tPSA, and PSAD are independent predictors of diagnosing csPCa in patients with negative mpMRI. It is suggested that patients with negative mpMRI and PSAD less than 0.20 ng/ml/ml could avoid prostate biopsy, as a PSAD cutoff value of 0.20 ng/ml/ml has better diagnostic performance than the traditional cutoff value of 0.15 ng/ml/ml.

PI-RADS v2.1 and PSAD for the prediction of clinically significant prostate cancer among patients with PSA levels of 4-10 ng/ml.

This study intended to evaluate the diagnostic accuracy of the prostate imaging reporting and data system (PI-RADS) and prostate-specific antigen density (PSAD) for clinically significant prostate cancer (csPCa) with PSA levels of 4-10 ng/ml. Between July 2018 and June 2022, a total of 453 patients with PSA levels of 4-10 ng/ml were retrospectively included, which were randomly assigned to the training group (323 patients) and validation group (130 patients). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CI were calculated. The overall diagnostic performance was determined with area under the receiver operating characteristic curve (AUC), and an integrated nomogram combining PI-RADS score and PSAD was constructed and tested in a validation cohort. In the training group, the AUC for PI-RADS 2.1 and PSAD alone were 0.875 (95% CI 0.834-0.916) and 0.712 (95% CI 0.648-0.775). At the cutoff PI-RADS score ≥ 4, the sensitivity and specificity were 86.2% (95% CI 77.4-1.9%) and 84.7% (95% CI 79.6-88.8%), respectively. For PSAD, the sensitivity and specificity were 73.3% (95% CI 63.0-82.4%) and 62.1% (95% CI 55.8-68.5%) at the cutoff 0.162 ng/ml/ml. While combining PI-RADS with PSAD, the diagnostic performance was improved significantly, with AUC of 0.893 (95% CI 0.853-0.933). In the validation group, the nomogram yielded a AUC of 0.871 (95% CI 0.807-0.934), which is significantly higher than PI-RADS alone (0.829, 95% CI 0.759-0.899, P = 0.02). For patients with PSA levels of 4-10 ng/ml, PSAD demonstrated moderate diagnostic accuracy whereas PI-RADS showed high performance. By combination of PSAD and PI-RADS together, the diagnostic performance could be improved significantly.

Evaluation of Cutoff Point Prostate Specific Antigen (PSA) and Prostate Specific Antigen Density (PSAD) in Patients with Suspected Prostate Cancer.

Background: Prostate cancer is the second leading cause of cancer death in men worldwide. There is no national standard for PSA cut-off levels even through the transrectal prostate biopsy procedure causes many serious complications such as bleeding, infection, and sepsis. Therefore, determining cut-off levels for PSA and PSAD is essential to avoid unnecessary biopsies. Objective: This study aims to determine the Prostate Specific Antigen (PSA) and Prostate Specific Antigen Density (PSAD) cut-off points in patients with suspected prostate cancer. Methods: A retrospective study was conducted from January 2018 until March 2021 in Saiful Anwar General Hospital Malang Indonesia. Inclusion criterias were patients with suspected prostate cancer; > 50 years old; underwent PSA, PSAD, and prostate biopsy. Exclusion criterias were patients refuse to participate in the study and incomplete patient medical record data. Medical records from 53 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy were reviewed. Statistical analysis was performed using Mann-Whitney U, Chi-Square, Fisher's Exact, and Receiver Operator Characteristic (ROC) curves. Results and Discussion: Medical records conducted 53 patients who met inclusion criteria and underwent transrectal ultrasonography (TRUS)-guided prostate biopsy were reviewed. PSA cut off level for prostate biopsy was 19.71 ng/ml with a sensitivity of 69.23% and a specificity of 72.5%. The positive predictive value is 45% and the negative predictive value is 87.87%. PSAD cut off level for prostate biopsy was 0.4113 with a sensitivity of 61.54% and a specificity of 63.16%. The positive predictive value is 36.36% and the negative predictive value is 82.76%. Conclusion: Results from this study, the cut off levels of PSA and PSAD in prostate disease patients is higher than the recommended cut off; prostate cancer is the largest malignancy in men worldwide and has a higher incidence in the older age and high serum PSA levels group.

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions.

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Evaluating the Utility of Prostate-Specific Antigen Density in Risk Stratification of PI-RADS 3 Peripheral Zone Lesions on Non-Contrast-Enhanced Prostate MRI: An Exploratory Single-Institution Study.

Objective This study aimed to explore the potential of prostate-specific antigen density (PSAD) as a supplementary tool for defining high-risk Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions in the peripheral zone on non-contrast-enhanced MRI. This additional stratification tool could supplement the decision-making process for biopsy, potentially helping in identifying higher-risk patients more accurately, minimizing unnecessary procedures in lower-risk patients, and limiting the need for dynamic contrast-enhanced (DCE) scans. Materials and methods Between January 2019 and April 2023, 30 patients with PI-RADS 3 lesions underwent MRI-ultrasound fusion biopsies at our institution. Age and PSAD values were investigated using logistic regression and chi-square automatic interaction detection (CHAID) analysis to discern their predictive value for malignancy. Results The mean patient age was 64.7 years, and the mean PSAD was 0.13 ng/mL2. Logistic regression demonstrated PSAD to be a significant predictor of cancer (p=0.012), but not age (p=0.855). CHAID analysis further identified a PSAD cut-off value of 0.12, below which the cancer detection rate was 23.1% and above which the rate increased to 76.5%. Conclusions This exploratory study suggests that PSAD might be utilized to enhance the stratification of high-risk PI-RADS 3 lesions in the peripheral zone on non-contrast-enhanced MRI, aiding in decision-making for biopsy. While biopsy remains the gold standard for definitive diagnosis, a high PSAD value may suggest a greater need for biopsy in this specific group. Although further validation in larger cohorts is required, our findings contribute to the ongoing discourse on optimizing PI-RADS 3 lesion management. Limitations include a small sample size, the retrospective nature of the study, and the single-center setting, which may impact the generalizability of our results.

Risks of prostate cancer and mortality in the city of Sharjah, United Arab Emirates.

Background: Prostatic hyperplasia (BPH) and prostate cancer (PCa) are common age-related diseases in men. According to World Health Organization (WHO), PCa is the second most common cancer among Emirati men. This study aimed to identify the risk factors associated with PCa and mortality in a cohort diagnosed with PCa between 2012 and 2021 in Sharjah, United Arab Emirates (UAE). Methods: The data collected in this retrospective case-control study included patient demographics and comorbidities, as well as PCa markers such as prostate-specific antigen (PSA), prostate volume, prostate-specific antigen density (PSAD), and Gleason scores. Risk factors for PCa were assessed using multivariate logistic regression analysis, and factors associated with all-cause mortality in PCa patients were evaluated using Cox-proportional hazard analysis. Results: Of the 192 cases analyzed in this study, 88 were diagnosed with PCa and 104 were diagnosed with BPH. Regarding risk factors for PCa, a higher risk of PCa was associated with age 65 or older (OR=2.76, 95% confidence interval (CI): 1.04-7.30; P=0.038) and serum PSAD greater than 0.1 ng/mL2 (OR=3.48, 95% CI:1.66-7.32; P=0.001), whereas being of UAE nationals (OR=0.40, 95% CI:0.18-0.88; P=0.029) were associated with lower risk of PCa, after adjusting for patient demographics and comorbidities. Moreover, regarding cancer markers, higher serum PSA level (P=0.003) and smaller prostate volume (P=0.028) were associated with a higher risk of PCa, after adjusting with patients' age and BMI. Additionally, a high-grade Gleason score was associated with an increased risk of all-cause mortality after adjusting for patient's age and BMI (hazard ratio, aHR= 2.3, 95% CI:1.3-4.1; P= 0.016). Conclusion: This study found that age 65 or older and serum PSAD greater than 0.1 ng/mL2 are risk factors for PCa, while UAE nationality is associated with a lower risk. PSAD may be a better screening marker for PCa compared to traditional markers such as PSA and prostate volume.

PI-RADS 3 lesions: a critical review and discussion of how to improve management.

Since the publication of PI-RADS v1 in 2012, the debate regarding the question of how to manage PI-RADS 3 lesions has been mostly unsolved. However, based on our review of the current literature we discuss possible solutions and improvements to the original classification, factors such as PSAD (Prostate Specific Antigen Density), age, and tumor volume, in the decision of whether to proceed with a biopsy or not.

Combining prostate-specific antigen density with prostate imaging reporting and data system score version 2.1 to improve detection of clinically significant prostate cancer: A retrospective study.

Globally, Prostate cancer (PCa) is the second most common cancer in the male population worldwide, but clinically significant prostate cancer (CSPCa) is more aggressive and causes to more deaths. The authors aimed to construct the risk category based on Prostate Imaging Reporting and Data System score version 2.1 (PI-RADS v2.1) in combination with Prostate-Specific Antigen Density (PSAD) to improve CSPCa detection and avoid unnecessary biopsy. Univariate and multivariate logistic regression and receiver-operating characteristic (ROC) curves were performed to compare the efficacy of the different predictors. The results revealed that PI-RADS v2.1 score and PSAD were independent predictors for CSPCa. Moreover, the combined factor shows a significantly higher predictive value than each single variable for the diagnosis of CSPCa. According to the risk stratification model constructed based on PI-RADS v2.1 score and PSAD, patients with PI-RADS v2.1 score of ≤2, or PI-RADS V2.1 score of 3 and PSA density of <0.15 ng/mL2, can avoid unnecessary of prostate biopsy and does not miss clinically significant prostate cancer.

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer.

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.

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

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

Flow cytometry-based study of model marine microalgal consortia revealed an ecological advantage of siderophore utilization by the dinoflagellate Amphidinium carterae.

Investigations of phytoplankton responses to iron stress in seawater are complicated by the fact that iron concentrations do not necessarily reflect bioavailability. Most studies to date have been based on single species or field samples and are problematic to interpret. Here, we report results from an experimental cocultivation model system that enabled us to evaluate interspecific competition as a function of iron content and form, and to study the effect of nutritional conditions on the proteomic profiles of individual species. Our study revealed that the dinoflagellate Amphidinium carterae was able to utilize iron from a hydroxamate siderophore, a strategy that could provide an ecological advantage in environments where siderophores present an important source of iron. Additionally, proteomic analysis allowed us to identify a potential candidate protein involved in iron acquisition from hydroxamate siderophores, a strategy that is largely unknown in eukaryotic phytoplankton.

Can we use neutrophil to lymphocyte ratio in the diagnosis and prediction of extracapsular extension in localized prostate cancer?

PURPOSE: We investigated role of neutrophil-to-lymphocyte ratio (NLR) in the diagnosis and prediction of extracapsular extension (ECE) in clinically localized prostate cancer (PCa). MATERIALS AND METHODS: A total of 396 patients with clinically localized PCa who underwent open radical retropubic prostatectomy (RRP), and 260 patients with benign prostatic hyperplasia (BPH) who underwent suprapubic prostatectomy were included in the study. Preoperative NLR, prostate specific antigen (PSA), prostate specific antigen density (PSAD), free PSA, prostate volume (PV), free/total PSA (f/t PSA) in both groups, and relation of NLR with PSA, Gleason Score (GS), and pathologic stage in PCa group were investigated. Records of patients were analyzed retrospectively. RESULTS: NLR, free PSA, f/t PSA, and PV were statistically higher in BHP, and PSAD was higher in PCa group. In PCa group, NLR was found to be higher in patients with PSA >10 ng/ml compared to those with less than ⩽10 ng/ml. NLR increases as the preoperative GS increases, and it was higher in pT3 patients than pT2 patients. NLR was statistically higher in those patients with positive lymph nodes than those without after RRP (p = 0.029). CONCLUSION: NLR is not a sufficient biomarker in differentiating clinically localized PCa from BPH. NLR increases as preoperative GS and pathologic stage increases. Lymph node involved patients after RRP have statistically higher NLR. NLR can be an indicator of ECE and lymph-node involvement in clinically localized PCa.

Efficient nitrite accumulation and elemental sulfur recovery in partial sulfide autotrophic denitrification system: Insights of seeding sludge, S/N ratio and flocculation strategy.

Partial sulfide autotrophic denitrification (PSAD) has been proposed as a promising process to achieve elemental sulfur recovery and nitrite accumulation, which is required for anaerobic ammonium oxidation reaction. This study investigated the effect of seeding sludge on the start-up performance of PSAD process, with different sludge taken from the oxidation zone (S-o) of wastewater treatment plants, partial denitrification reactor (S-PD), and anoxic/oxic reactor (S-A/O). The results showed that the PSAD process could be achieved rapidly in three systems on day 22, 29 and 26, respectively. In particular, the S-O system completed the start-up in the shortest time of 22 d, with NO3--N and S2- removal efficiency of 85.3% and 99.3%, respectively. Selected the S-O system to operate long term, the nitrite (NO2--N) and biological elemental sulfur (S0) accumulation efficiencies were systematically investigated under different S/N ratios (in a range of 0.71-1.2). The maximum NO2--N and S0 accumulation efficiencies were 85.2% and 73.5%, respectively, at the S/N ratio of 1.1. In addition, the separation and recovery of S0 in effluent was achieved by employing polyaluminum chloride (PAC) as a flocculant. Using 2D Gaussian function as quadratic model for the maximizing of S0 flocculant efficiency (SFR), an optimal condition of PAC dosage 7.92 mL/L and pH 5.14 was obtained, and the SFR reached 94.1%, under such conditions. The findings offered useful information to facilitate the application of the PSAD process.

Considering Predictive Factors in the Diagnosis of Clinically Significant Prostate Cancer in Patients with PI-RADS 3 Lesions.

The use of multi-parametric magnetic resonance imaging (mpMRI) in conjunction with the Prostate Imaging Reporting and Data System (PI-RADS) is standard practice in the diagnosis, surveillance, and staging of prostate cancer. The risk associated with lesions graded at a PI-RADS score of 3 is ambiguous. Further characterization of the risk associated with PI-RADS 3 lesions would be useful in guiding further work-up and intervention. This study aims to better characterize the utility of PI-RADS 3 and associated risk factors in detecting clinically significant prostate cancer. From a prospectively maintained IRB-approved dataset of all veterans undergoing mpMRI fusion biopsy at the Southeastern Louisiana Veterans Healthcare System, we identified a cohort of 230 PI-RADS 3 lesions from a dataset of 283 consecutive UroNav-guided biopsies in 263 patients from October 2017 to July 2020. Clinically significant prostate cancer (Gleason Grade ≥ 2) was detected in 18 of the biopsied PI-RADS 3 lesions, representing 7.8% of the overall sample. Based on binomial analysis, PSA densities of 0.15 or greater were predictive of clinically significant disease, as was PSA. The location of the lesion within the prostate was not shown to be a statistically significant predictor of prostate cancer overall (p = 0.87), or of clinically significant disease (p = 0.16). The majority of PI-RADS 3 lesions do not represent clinically significant disease; therefore, it is possible to reduce morbidity through biopsy. PSA density is a potential adjunctive factor in deciding which patients with PI-RADS 3 lesions require biopsy. Furthermore, while the risk of prostate cancer for African-American men has been debated in the literature, our findings indicate that race is not predictive of identifying prostate cancer, with comparable Gleason grade distributions on histology between races.

Endogenous testosterone density as ratio of endogenous testosterone levels on prostate volume predicts tumor upgrading in low-risk prostate cancer.

OBJECTIVES: To evaluate preoperative endogenous testosterone (ET) density (ETD), defined as the ratio of ET on prostate volume, and tumor upgrading risk in low-risk prostate cancer (PCa). MATERIALS AND METHODS: From November 2014 to December 2019, 172 low-risk patients had ET (nmol/L) measured. ETD, prostate-specific antigen density (PSAD) and the ratio of percentage of biopsy positive cores (BPC) to prostate volume (PV), defined as BPC density (BPCD), were evaluated. Associations with tumor upgrading in the surgical specimen were assessed by statistical methods. RESULTS: Overall, 121 patients (70.3%) had tumor upgrading, which was predicted by BPCD (odds ratio, OR = 4.640; 95% CI 1.903-11.316; p = 0.001; overall accuracy: 70.3%). On multivariate analysis, tumor upgrading and clinical density factors related to each other for BPCD being predicted by ETD (regression coefficient, b = 0.032; 95% CI 0.021-0.043; p < 0.0001), PSAD (b = 1.962; 95% CI 1.067-2.586; p < 0.0001) and tumor upgrading (b = 0.259; 95% CI 0.112-0.406; p = 0.001). According to the model, as BPCD increased, ETD and PSAD increased, but the increase was higher for upgraded cases who showed either higher tumor load but significantly lower mean levels of either ET or PSA. CONCLUSIONS: As ETD increased, higher tumor loads were assessed; however, in upgraded patients, lower ET was also detected. ETD might stratify low-risk disease for tumor upgrading features.

Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making.

PURPOSE: The purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making. METHODS: We retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group. RESULTS: In the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators. CONCLUSION: The prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

Bioelectric Impedance Analysis Test Improves the Detection of Prostate Cancer in Biopsy Candidates: A Multifeature Decision Support System.

Prostate cancer (PCa) gold-standard diagnosis relies on prostate biopsy, which is currently overly recommended since other available noninvasive tools such as prostate-specific antigen (PSA) multiparametric MRI (mMRI) showed low diagnostic accuracy or high costs, respectively. The aim of the study was to determine the accuracy of a novel Bioelectric Impedance Analysis (BIA) test endorectal probe for the selection of patients candidate to prostate biopsy and in particular the clinical value of three different parameters such as resistance (R), reactance (Xc), and phase angle (PA) degree. One-hundred twenty-three consecutive candidates to prostate biopsy and 40 healthy volunteers were enrolled. PSA and PSA density (PSAD) determinations, Digital Rectal Examination (DRE), and the novel BIA test were analyzed in patients and controls. A 16-core prostate biopsy was performed after a mMRI test. The study endpoints were to determine accuracy of BIA test in comparison with PSA, PSAD levels, and mMRI and obtain prostate cancer (PCa) prediction by BIA test. The Mann-Whitney U-test, the Wilkoxon rank test, and the Holm-Bonferroni's method were adopted for statistical analyses, and a computational approach was also applied to differentiate patients with PCa from those with benign disease. Combined PSA, PSAD, DRE, and trans-rectal ultrasound test failed to discern patients with PCa from those with benign disease (62.86% accuracy). mMRI PIRADS ≥3 showed a sensitivity of 83% and a specificity of 59%. The accuracy in discerning PCa increased up to 75% by BIA test (sensitivity 63.33% and specificity 83.75%). The novel finger probe BIA test is a cheap and reliable test that may help to improve clinical multifeature noninvasive diagnosis for PCa and reduce unnecessary biopsies.

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

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

Update and optimization of active surveillance in prostate cancer in 2021. / Actualización y optimización de la vigilancia activa en cáncer de próstata en 2021.

INTRODUCTION AND OBJECTIVES: Within the paradigm shift of the last decade in the management of prostate cancer (PCa), perhaps the most relevant event has been the emergence of active surveillance (AS) as a mandatory strategy in low-risk disease. We carry out a critical review of the clinical, pathological and radiological improvements that allow optimizing AS in 2021. MATERIAL AND METHODS: Critical narrative review of the literature on improvement issues and controversial aspects of AS. RESULTS: Adequate use of traditional criteria, optimized by enhanced biopsy and calculation of the prostate volume technique thanks to multiparametric magnetic resonance imaging (mpMRI) allow a better selection of patients for AS. This management should not be limited to patients under 60years of age, and patients with intermediate-risk PCa should be carefully selected to be included. Biopsies are still required in the follow-up, which can be personalized according to risk patterns. The pathologist must identify the cribriform or intraductal histology on biopsies in order to exclude these patients from AS, in the same way as with patients with alterations in DNA repair genes. CONCLUSIONS: Controversial indications such as the inclusion of patients from intermediate-risk groups, or the transition to active treatment due to exclusive progression in tumor volume, should be further optimized. It is possible that the future competition of tissue biomarkers, the refinement of objective parameters of mpMRI and the validation of PSA kinetics calculators may sub-stratify risk groups.