The roles of prostate-specific antigen (PSA) density, prostate volume, and their zone-adjusted derivatives in predicting prostate cancer in patients with PSA less than 20.0 ng/mL

Apparent differences in prostate zones: susceptibility to prostate cancer, benign prostatic hyperplasia and prostatitis

Men are inevitably plagued by prostate disease throughout their lives. However, the understanding of the pathogenesis of prostate diseases is still limited. In the 1960s, McNeal proposed the theory of prostate zones: the prostate was divided into three main zones: transition zone, central zone, and peripheral zone. Over the past 50 years, significant differences between different prostate zones have been gradually revealed. We summarized the most significant differences in different zones of the prostate. For the first time, we proposed the "apparent difference in prostate zones" concept. This new concept has been proposed to understand the different zones of the prostate better. It also provided new ideas for exploring the susceptibility of lesions in different prostate zones. Despite the reported differences between zones, the treatment of prostate-related diseases remains partition agnostic. Therefore, we also discussed the clinical significance of the "apparent difference in the prostate zone" and emphasized the necessity of prostate zones.

Digital droplet immunoassay based on a microfluidic chip with magnetic beads for the detection of prostate-specific antigen

Sensitive biomarker detection techniques are beneficial for both disease diagnosis and postoperative examinations. In this study, we report an integrated microfluidic chip designed for the immunodetection of prostate-specific antigens (PSAs). The microfluidic chip is based on the three-dimensional structure of quartz capillaries. The outlet channel extends to 1.8 cm, effectively facilitating the generation of uniform droplets ranging in size from 3 to 50 μm. Furthermore, we successfully immobilized the captured antibodies onto the surface of magnetic beads using an activator, and we constructed an immunosandwich complex by employing biotinylated antibodies. A key feature of this microfluidic chip is its integration of microfluidic droplet technology advantages, such as high-throughput parallelism, enzymatic signal amplification, and small droplet size. This integration results in an exceptionally sensitive PSA detection capability, with the detection limit reduced to 7.00 ± 0.62 pg/mL.

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.

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer

Objective

To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa).

Methods

MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA).

Results

The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913).

Conclusions

We found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Significance of the prostate central gland and total gland volume ratio in the diagnosis of prostate cancer patients in the prostate specific antigen grey zone

Objective

To explore the significance of the prostate central gland to total gland volume ratio (PVc/PV) in the diagnosis of prostate cancer (PCa) in patients with prostate specific antigen (PSA) levels in the grey zone (4–10 ng/ml).

Methods

This retrospective study enrolled patients that had undergone prostate biopsy. The volume of the prostate and the central prostate gland were measured. The differences in PSA, the ratio of free to total PSA (f/tPSA), PSA density (PSAD) and PVc/PV between the PCa and non-PCa groups were compared. Receiver operating characteristic curve analysis for PCa and clinically significant PCa (csPCa) diagnosis were calculated according to PSA (reference), f/tPSA, PSAD and PVc/PV.

Results

This study enrolled 136 patients. There was no significant difference in PSA and f/tPSA between the PCa and non-PCa groups, while there were significant differences in PSAD and PVc/PV. The area under the curve values of PVc/PV for PCa or csPCa diagnosis were 0.876 and 0.933, respectively; and for PSAD, they were 0.705 and 0.790, respectively. These were significantly different compared with the PSA curve, whereas f/tPSA showed no significant difference from the PSA curve.

Conclusion

PVc/PV could be a predictor of PCa when PSA is between 4–10 ng/ml.

Biomarkers in the setting of benign prostatic hyperplasia-induced lower urinary tract symptoms: what an interventional radiologist needs to know

With increasing evidence to support prostate artery embolization (PAE) in the treatment of benign prostatic hyperplasia (BPH)-induced lower urinary tract symptoms (LUTS), Interventional Radiologists have begun to play an important role in the management of these patients. One area of knowledge needed when developing a PAE practice is knowledge of prostate-specific antigen (PSA) and other biomarkers utilized to detect prostate cancer in this population and what role they should play in the work up and follow-up of patients presenting with presumed BPH-induced LUTS. Furthermore, understanding how to evaluate presumed BPH-induced LUTS and stratify the risk of prostate cancer is an important skill to develop. The goal of this review is to provide Interventional Radiologists who have begun or aim to begin a PAE practice with the information they need to know regarding PSA levels and prostate cancer risk stratification for this patient population.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.

A novel fluorescent probe for the early detection of prostate cancer based on endogenous zinc sensing

BACKGROUND

The early detection of prostate cancer can significantly optimize the prognosis, prolong patient lifespan, and improve quality of life. It has been well documented that prostate cancer tissues have lower zinc content than normal prostate tissues due to an impairment of the zinc accumulation mechanism.

METHODS

A novel diketopyrrolopyrrole (DPP)-based fluorescent zinc ion probe named DPP-C2 was prepared. The fluorescent intensity of this novel molecule is in direct proportion to environmental zinc concentration. Malignant (DU145 and PC3 cells) and normal prostate epithelial RWPE-1 cells were tested. Prostate cancer tissues were also cultured and observed as tissue sections. The probe was also intravenously administered to tumor-bearing (DU145 and PC3 cells) nude mice and observed under a whole-body fluorescence live-imaging system.

RESULTS

The probe showed minimal cytotoxicity to malignant and normal prostate cells. The RWPE-1 cells showed not only stronger baseline fluorescence but also a significant increase in signal intensity after culturing in a zinc-supplemented medium. In human prostate sections, the pathologically confirmed cancer tissues exhibited weaker fluorescence signals than normal and benign hyperplastic tissues. With proper excitation, prostate tissues revealed more intense fluorescence signals than tumor tissues, whereas other surrounding tissues showed almost no fluorescence.

CONCLUSIONS

The novel zinc ion fluorescent probe DPP-C2 is low toxic and showed potential application for the early detection of prostate cancer based on endogenous zinc sensing.

Downregulation of serum CXCL4L1 predicts progression and poor prognosis in prostate cancer patients treated by radical prostatectomy

Our previous study found that plate factor-4 variant (CXCL4L1) was downregulated in the serum of patients with prostate cancer (PCa). The aim of the present study was to investigate the prognostic value of CXCL4L1 in PCa. In total, 213 PCa patients treated with radical prostatectomy were enrolled and peripheral blood samples of all patients were collected. Expression of serum CXCL4L1 in patients with different tumor stages and grades were measured by enzyme-linked immunosorbent assay (ELISA). The Kaplan–Meier method was applied to estimate the progression to castration-resistant prostate cancer (CRPC), metastasis, biochemical recurrence (BCR)-free survival, and overall survival (OS). Prognostic factors for BCR-free survival and OS were determined by univariate and multivariate analyses using the Cox proportional hazards regression model. The expression of CXCL4L1 was significantly lower in PCa patients with advanced pathological tumor stage, high-grade Gleason score, and metastasis. Moreover, downregulation of CXCL4L1 not only strongly correlated with aggressive clinicopathological features, but also predicted tumor progression and unfavorable outcomes. Finally, multivariate Cox regression analyses identified CXCL4L1 as an independent prognostic factor for both BCR-free survival (hazard ratio [HR]: 2.03, 95% confidence interval [CI]: 1.26–3.27; P = 0.004) and OS (HR: 2.26, 95% CI: 1.07–4.79; P = 0.033). In conclusion, our results indicate that CXCL4L1 might serve as a novel and promising prognostic biomarker for patients with PCa and potential therapeutic target in the future.

Prediction models for prostate cancer outcomes: what is the state of the art in 2017?

PURPOSE OF REVIEW

Prostate cancer (PCa) remains a significant public health burden, with multiple points for decision-making at all stages of the disease. Given the amount and variety of data that may influence disease management, prediction models have been published to assist clinicians and patients in making decisions about the best next course of action at many disease states. We sought to review the most important studies related to PCa prediction models since 2016 and evaluate their impact upon the evolving field of risk modeling in PCa.

RECENT FINDINGS

There has been a significant amount of work published in the past year concerning risk modeling in PCa at all stages of disease, ranging from screening to predicting survival with metastatic disease. The majority of recent publications focus upon the addition of a new biomarker to prediction models or upon validating previously published prediction models. In particular, MRI has been the topic of a number of more recent studies.

SUMMARY

Prediction modeling in PCa currently compares the area under the receiver operating curve between models with and without the biomarker of interest to predict the outcome of interest in multiple disease states, ranging from diagnosis to prediction of survival with metastatic disease. Future work should provide additional information regarding clinical impact and measures of prediction confidence.