Significant Diagnostic Value of Free-Serum PSA (FPSA)/Prostate-Specific Antigen Density (PSAD) and (F/T)/PSAD for Prostate Cancer of the Chinese Population in a Single Institution

Prostate cancer: Novel genetic and immunologic biomarkers

Prostate cancer (PCa) is considered one of the most prevalent male malignancies worldwide with a global burden estimated to increase over the next two decades. Due to significant mortality and debilitation of survival, early diagnosis has been described as key. Unfortunately, current diagnostic serum-based strategies have low specificity and sensitivity. Histologic examination is invasive and not useful for treatment and monitoring purposes. Hence, a plethora of studies have been conducted to identify and validate an efficient noninvasive approach in the diagnosis, staging, and prognosis of PCa. These investigations may be categorized as genetic (non-coding biomarkers and gene markers), immunologic (immune cells, interleukins, cytokines, antibodies, and auto-antibodies), and heterogenous (PSA-related markers, PHI-related indices, and urinary biomarkers) subgroups. This review examines current approaches and potential strategies using biomarker panels in PCa.

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

OBJECTIVE

We aimed to investigate the diagnostic value of different laboratory indicators in combination with total prostate-specific antigen (TPSA) for prostate cancer (PCa).

METHODS

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).

RESULTS

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.

CONCLUSION

The combination of TPSA with multiple indicators may improve diagnostic accuracy for PCa.

Association of Blood Inflammatory Biomarkers with Clinical Outcomes in Patients with AECOPD: An 8-Year Retrospective Study in Beijing

Purpose

To discover potential inflammatory biomarkers, which can compare favorably with traditional biomarkers, and their best cut-offs at first admission to predict clinical outcomes (short-term and long-term) and the risk of readmission among acute exacerbations of chronic obstructive pulmonary disease (AECOPD) patients.

Patients and Methods

Novel inflammatory biomarkers (such as the neutrophil-lymphocyte ratio [NLR], platelet-lymphocyte ratio [PLR], etc.) were compared with traditional biomarkers by Pearson's correlation test. Logistic regression analysis and receiver operating characteristic (ROC) curves were applied to judge the accuracy of these novel biomarkers to predict in-hospital mortality.

Results

Surviving AECOPD patients had lower NLR, PLR, and lymphocyte-to-monocyte ratios than non-survival patients (all P < 0.001). According to Pearson's correlation test, there was a linear correlation between novel and traditional biomarkers (all P < 0.05). In terms of a single biomarker, the AUC value of NLR was the largest, which was not inferior to C-reactive protein (Z-P = 0.064), and superior to erythrocyte sedimentation rate (Z-P = 0.002) and other novel single inflammatory biomarkers (all Z-P < 0.05). The mortality of patients with NLR ≥ 4.43 was 2.308-fold higher than that of patients with NLR < 4.43. After dividing patients into a higher or lower NLR group, pooled results showed that patients with NLR ≥ 4.43 had a higher rate of treatment failure, intensive care unit admission, longer hospital length of stay, one-year mortality after the index hospitalization, and overall mortality than patients with NLR < 4.43 (all P < 0.001). Patients with NLR ≥ 4.43 were associated with higher and earlier first readmission due to AECOPD than patients with lower NLR.

Conclusion

NLR was the best to forecast the clinical prognosis and readmission risk among AECOPD patients, which was not inferior to CRP, and the best cut-off value of NLR was 4.43.

Efficacy and safety evaluation of complete intrafascial prostatectomy in suspected prostate cancer patients with dysuria: a retrospective cohort study

Background

Suspected localized prostate cancer (PCa) patients with dysuria Complete intrafascial prostatectomy (CIP) can remove the whole prostate gland with the maximal retain of adjacent normal tissues around the prostate, and can be applied in some suspected localized prostate cancer (PCa) patients with dysuria. However, precious few studies have assessed the efficacy and safety of CIP in these patients without preoperative needle biopsies.

Methods

In this retrospective single-arm cohort study, all 22 suspected PCa patients with dysuria who underwent CIP at our hospital were enrolled. The clinical data including age, prostate-specific antigen (PSA), free-serum PSA, prostate volume, perioperative and postoperative complications were collected. The PSA level at 6 weeks after CIP and recoveries of urinary continence and erectile function were acquired in the follow-up procedures, and were used as the main measurements of efficacy and safety for CIP respectively.

Results

The patients had an average age of 71.91±8.29 years and an average preoperative PSA level of 10.75±4.25 ng/mL. The operations for all 22 patients were successfully completed. The average operation time was 135.20±41.44 min (range, 40.0-215.0 min), and the average blood loss volume was 128.64±145.09 mL. In total, 17 patients (77.27%) had PCa confirmed by postoperative pathology, and 5 patients (22.73%) had benign prostatic hyperplasia. The PSA level dropped to 0.010±0.004 ng/mL at 6 weeks after surgery. According to the loose criteria to assess urinary incontinence, the patients achieved continence rates of 63.6% immediately after the operation, 95.5% at 1 month, and 100% at 3 months. According to the strict criteria, the continence rates immediately, and at 1, 3, 6, and 9 months after surgery were 27.3%, 63.6%, 90.9%, 95.5%, and 100%, respectively. None of the patients complained of urinary obstruction symptoms after surgery. Before CIP, all the patients had erectile dysfunction and an International Index of Erectile Function 5 (IIEF-5) score of 9.64±5.91. After surgery, the patients had IIEF-5 scores at 3, 6, and 12 months of 5.45±4.43, 6.95±5.30, and 7.57±5.69, respectively.

Conclusions

Although the study had some limitations, CIP may be a prudent option for patients with suspected localized PCa who also present with dysuria.

Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy

Objective

This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers.

Methods

First, we retrospectively collected the clinicopathologic data from 475 patients who underwent prostate biopsy at our hospital between January 2019 to August 2021. Univariate and multivariate logistic regression analyses were used to select risk factors. Then, we established the nomogram prediction model based on the risk factors. The model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots and the Hosmer–Lemeshow test. Decision curve analysis (DCA) was used to evaluate the net benefit of the model at different threshold probabilities. The model was validated in an independent cohort of 197 patients between September 2021 and June 2022.

Results

The univariate and multivariate logistic regression analyses based on the development cohort indicated that the model should include the following factors: age (OR = 1.056, p = 0.001), NEUT (OR = 0.787, p = 0.008), HPR (OR = 0.139, p &lt; 0.001), free/total (f/T) PSA (OR = 0.013, p = 0.015), and PI-RADS (OR = 3.356, p &lt; 0.001). The calibration curve revealed great agreement. The internal nomogram validation showed that the C-index was 0.851 (95% CI 0.809-0.894). Additionally, the AUC was 0.851 (95% CI 0.809-0.894), and the Hosmer–Lemeshow test result presented p = 0.143 &gt; 0.05. Finally, according to decision curve analysis, the model was clinically beneficial.

Conclusion

Herein, we provided a nomogram combining patients’ clinical data with biomarkers to help diagnose prostate cancers.

The influence of prostate volume on clinical parameters in prostate cancer screening

PURPOSE

The purpose of the study was to evaluate the diagnostic significance of two new and a few clinical markers for prostate cancer (PCa) at various prostate volumes (PV).

METHODS

The study subjects were divided into two groups. Among them, there were 70 cases in the PV ≤30 ml group (benign prostatic hyperplasia [BPH]: 32 cases, PCa: 38 cases) and 372 cases in the PV > 30 ml group (BPH: 277 cases, PCa: 95 cases). SPSS 26.0 and GraphPad Prism 8.0 were used to construct their receiver operating characteristic (ROC) curves for diagnosing PCa and calculating their area under the ROC curve (AUC).

RESULTS

In the PV ≤30 ml group, the diagnostic parameters based on prostate-specific antigen (PSA) had a decreased diagnostic significance for PCa. In the PV > 30 ml group, PSAD (AUC = 0.709), AVR (AVR = Age/PV, AUC = 0.742), and A-PSAD (A-PSAD = Age×PSA/PV, AUC = 0.736) exhibited moderate diagnostic significance for PCa, which was better than PSA-AV (AUC = 0.672), free PSA (FPSA, AUC = 0.509), total PSA (TPSA, AUC = 0.563), (F/T) PSA (AUC = 0.540), and (F/T)/PSAD (AUC = 0.663). Compared with AVR, A-PSAD exhibited similar diagnostic significance for PCa, but higher than PSA density (PSAD).

CONCLUSIONS

Choosing appropriate indicators for different PVs could contribute to the early screening and diagnosis of PCa. The difference in the diagnostic value of two new indicators (A-PSAD and AVR), and PSAD for PCa may require further validation by increasing the sample size.

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Objective

The aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.

Methods

A total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.

Results

Prostate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.

Conclusion

The RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

Clinical research analysis based on prostate cancer screening diagnosis

This study aimed to analyse the clinical characteristics and risk factors of patients with positive prostate biopsy at 4-20 ng/mL of prostate-specific antigen (PSA), construct a new parameter based on this characteristics and assess its diagnostic value for prostate cancer (PCa). Logistic regression analysis was used to clarify the risk factors of PCa, and a new parameter based on the results was constructed. Compare the diagnostic value of various diagnostic parameters for PCa. Logistic multivariate regression analysis revealed that age (OR, 5.269; 95%CI, 2.762-10.050), comorbid diabetes (OR, 2.437; 95%CI, 1.162-5.111), PSA (OR, 2.462; 95%CI, 1.198-5.059) and prostate volume (PV) (OR, 0.227; 95%CI, 0.100-0.516) are risk factors for PCa. The age, PSA and PV of patients were combined to construct a new parameter, that is A-PSAD = (age × total PSA [TPSA])/PV]. The area under the receiver-operating characteristic curve(AUC) of A-PSAD (0.728) for PCa diagnosis was higher than the AUCs of TPSA (0.581), free prostate-specific antigen (0.514), (F/T)PSA (0.535) and PSAD (0.696), with significant differences. Age, history of diabetes, TPSA and PV are risk factors for PCa(PSA:4-20ng/mL); in addition, A-PSAD has a moderate diagnostic value for PCa and may become a new indicator for PCa screening.

Development and validation of a nomogram for predicting prostate cancer in patients with PSA ≤ 20 ng/mL at initial biopsy

The aim of this study was to construct a nomogram for predicting prostate cancer (PCa) in patients with PSA ≤ 20 ng/mL at initial biopsy.The patients with PSA ≤ 20 ng/mL who underwent prostate biopsy were retrospectively included in this study. The nomogram was developed based on predictors for PCa, which were assessed by multivariable logistic regression analysis. The receiver operating characteristic curve, calibration plots and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.This retrospective study included 691 patients, who were divided into training set (505 patients) and validation set (186 patients). The nomogram was developed based on the multivariable logistic regression model, including age, total PSA, free PSA, and prostate volume. It had a high area under the curve of 0.857, and was well verified in validation set. Calibration plots and DCA further validated its discrimination and potential clinical benefits. Applying the cut-off value of 15%, our nomogram would avoid 42.5% of unnecessary biopsies while miss only 4.4% of PCa patients.The nomogram provided high predictive accuracy for PCa in patients with PSA ≤ 20 ng/mL at initial biopsy, which could be used to avoid the unnecessary biopsies in clinical practice.

Prostate-specific antigen and (free prostate-specific antigen/ prostate-specific antigen) ratio in patients with Benign Prostatic Hyperplasia and Prostate Cancer

Background: Prostate cancer is one of the most common cancers in menworldwide. Many markers are suggested as markers of prostate cancer with differentspecificity and sensitivity.Objective : The present study’s main aim is to examine the possible utility ofprostate-specific antigen indices as markers of prostate cancer.Methods: A case-control study was conducted in the Department of Chemistry andBiochemistry, College of Medicine, Al- Nahrain University, Baghdad, Iraq from July2018 till March 2019, includes 84 subjects divided into three groups:Twenty Four patients with prostate cancer (PCA), thirty patients with benignprostatic hyperplasia (BPH) and thirty healthy subjects as a control group wereexamined in this study.Thirty healthy volunteer subjects were asked to be involved in this study as a controlgroup. Blood samples from these patients were collected before obtaining a prostaticbiopsy. Serum PSA, fPSA levels were quantified by the ELISA technique.Results: PSA cut-off value was found to be more than 9.57 ng/ml for Prostate Cancerpatients, values range between 3.17 - 9.57 ng/ml for BPH patients and cut-off valuefor control was found to be less than 3.17 ng/ml, while serum (fPSA/PSA) % cut-offvalue was less than 11.1% for Prostate Cancer patients, values range between 11.1% -31 % for BPH patients, and cut-off value was greater than 31% for the control group.Conclusion: There is a highly significant difference in serum PSA levels and(fPSA/PSA)% between the prostate cancer and control groups. Body mass indexshowed an inverse association with the risk of prostate cancer.

Development and validation of a nomogram for predicting prostate cancer in men with prostate-specific antigen grey zone based on retrospective analysis of clinical and multi-parameter magnetic resonance imaging/transrectal ultrasound fusion-derived data

Background

Urologists face a dilemma when deciding whether prostate biopsy is required for patients with prostate-specific antigen (PSA) levels in the grey zone (4 to 10 ng/mL).

Methods

We retrospectively analyzed data from consecutive patients with PSA levels in grey zone, who underwent targeted multiparametric magnetic resonance imaging (MP-MRI)/transrectal ultrasound (TRUS) fusion biopsy with elastography between November 2017 and December 2019 in our hospital. The patientse data including age, PSA, fPSA (free PSA), fPSA/PSA, PSA density (PSAD), prostate volume, elastography Q-analysis score (EQS), and prostate imaging-reporting and data system (PI-RADS) score were collected. The nomogram was built using logistic regression and the final cohort of patients was randomly divided into a training cohort (70%) and a validation cohort (30%) by R software. The models were evaluated by receiver operating characteristic curve (ROC) analysis and calibration curve analysis. The nomogram was constructed from the best model.

Results

The final study cohort consisted of 155 patients (training cohort, 109 patients; validation cohort, 46 patients) with PSA in the grey zone, of which 36 patients were pathologically diagnosed with PCa. The EQS model, -EQS model, +EQS model were built. The +EQS model that consisted of fPSA/PSA, EQS, and PI-RADS score had the best PCa diagnostic accuracy (development and validation, 0.783 and 0.781) and probability score (development and validation, 0.939 vs. 0.622). The new nomogram based on this model was constructed, in which fPSA/PSA ratio had the largest impact, followed by PI-RADS and EQS.

Conclusions

Elastography and pre-biopsy MP-MRI has clinical significance for patients with PSA in the grey zone. The new nomogram, which is based on pre biopsy data including serological analysis, PI-RADS score, and EQS, can be helpful for clinical decision-making to avoid unnecessary biopsy.