The Value of Prostate-Specific Antigen-Related Indexes and Imaging Screening in the Diagnosis of Prostate Cancer

Prostate cancer with elevated free prostate-specific antigen density: A case report

BACKGROUND

Prostate cancer is the second most common cancer among men worldwide, and prostate-specific antigen (PSA) is often used in clinical practice to screen for prostate cancer. Normal total PSA (tPSA) level initially excludes prostate cancer. Here, we report a case of prostate cancer with elevated free PSA density (fPSAD).

CASE SUMMARY

A patient diagnosed with benign prostatic hyperplasia underwent prostatectomy, and the postoperative pathological results showed acinar adenocarcinoma of the prostate. The patient is currently undergoing endocrine chemotherapy.

CONCLUSION

We provide a clinical reference for diagnosis and treatment of patients with normal tPSA but elevated fPSAD.

Investigation of awareness rate of prostate-specific antigen (PSA) among the general public in China and analysis of influencing factors

Objective

This study aimed to evaluate the awareness rate of prostate-specific antigen (PSA) among the general public in China and provide data about prostate cancer (PCa) for related scientific research.

Methods

A cross-sectional survey of PSA awareness was conducted in multiple regional populations using an online questionnaire. The questionnaire included basic information, knowledge about PCa, the awareness rate and application of PSA, and future expectations toward applying PSA screening in clinical practice. The study applied the methods of Pearson chi-square analysis and Logistic regression analysis.

Results

A total of 493 valid questionnaires were included. Two hundred and nineteen respondents (44.4%) were males, and 274 (55.6%) were females. Of all respondents, 212 (43.0%) were under 20 years old, 147 (29.8%) were 20-30 years old, 74 (15.0%) were 30-40 years old, and 60 (12.2%) were over 40 years old. There are 310 people (62.9%) with medical educational background and 183 (37.1%) without. One hundred eighty-seven (37.9%) of the respondents were aware of PSA, and 306 (62.1%) were unaware of PSA. Statistically significant differences were obtained between the two groups regarding different ages, educational backgrounds, occupations, departments, and habits of knowing medical knowledge (all p < 0.05). In addition, the differences between the group of aware of PSA (AP) and the group unaware of PSA (UAP) in terms of whether they had been exposed to PSA screening and whether they had exposure to PCa patients or related knowledge were also observed (all p < 0.05). Age ≥30 years, medical educational background, understanding of medical knowledge, exposure to PCa patients or related knowledge, exposure to PSA screening, and status as a graduate student and above were independent factors for the occurrence of PSA awareness events (all p < 0.05). In addition, age ≥ 30 years, medical educational background, and awareness of PSA were independent factors for future expectations toward PSA (all p < 0.05).

Conclusions

We first analyzed the public awareness of PSA. Cognition degrees of PSA and PCa awareness vary among different populations in China. Therefore, we should designate corresponding widespread scientific educational programs for different populations to increase the awareness rate of PSA.

Three-dimensional ultrasound integrating nomogram and the blood flow image for prostate cancer diagnosis and biopsy: A retrospective study

Backgrounds

Prostate cancer (PCa) is the second most common male cancer in the world and based on its high prevalence and overwhelming effect on patients, more precise diagnostic and therapeutic methods are essential research topics. As such, this study aims to evaluate the value of three-dimensional transrectal ultrasound (3D-TRUS) in the detection, diagnosis and biopsy of PCa, and to provide a basis for clinical practice of PCa.

Methods

Retrospective analysis and comparison of a total of 401 male patients who underwent prostate TRUS in our hospital from 2019 to 2020 were conducted, with all patients having prostate biopsy. Nomogram was used to estimate the probability of different ultrasound signs in diagnosing prostate cancer. The ROC curve was used to estimate the screening and diagnosis rates of 3D-TRUS, MRI and TRUS for prostate cancer.

Results

A total of 401 patients were randomly divided into two groups according to different methods of prostate ultrasonography, namely the TRUS group (251 patients) and the 3D-TRUS group (150 patients). Of these cases, 111 patients in 3D-TRUS group underwent MRI scan. The nomogram further determined the value of 3D-TRUS for prostate cancer. The ROC AUC of prostate cancer detected by TRUS, MRI and 3D-TRUS was 0.5580, 0.6216 and 0.6267 respectively. Biopsy complications were lower in 3D-TRUS group than TRUS group, which was statistically significant (P&lt;0.005).

Conclusions

The accuracy of 3D-TRUS was higher in diagnosis and biopsy of prostate cancer. Meanwhile, the positive rate of biopsy could be improved under direct visualization of 3D-TRUS, and the complications could be decreased markedly. Therefore, 3D-TRUS was of high clinical value in diagnosis and biopsy of prostate cancer.

The potential of a nomogram combined PI-RADS v2.1 and contrast-enhanced ultrasound (CEUS) to reduce unnecessary biopsies in prostate cancer diagnostics

OBJECTIVES

To develop a nomogram prediction model based on Prostate Imaging Reporting and Data System v.2.1 (PI-RADS v2.1) and contrast-enhanced ultrasound (CEUS) for predicting prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in males with prostate-specific antigen (PSA) 4-10 ng ml-1 to avoid unnecessary biopsy.

METHODS

A total of 490 patients who underwent prostate biopsy for PSA 4-10 ng ml-1 were enrolled and randomly divided into a pilot cohort (70%) and a validation cohort (30%). Univariate and multivariate logistic regression models were constructed to select potential predictors of PCa and csPCa, and a nomogram was created. The area under receiver operating characteristic (ROC) curve (AUC) was calculated, and compared using DeLong's test. The diagnostic performance and unnecessary biopsy rate of the nomogram prediction model were also assessed. Hosmer-Lemeshow goodness-of-fit test was employed to test for model fitness.

RESULTS

The multivariate analysis revealed that features independently associated with PCa and csPCa were age, PI-RADS score and CEUS manifestations. Incorporating these factors, the nomogram achieved good discrimination performance of AUC 0.843 for PCa, 0.876 for csPCa in the pilot cohort, and 0.818 for PCa, 0.857 for csPCa in the validation cohort, respectively, and had well-fitted calibration curves. And the diagnostic performance of the nomogram was comparable to the model including all the parameters (p > 0.05). Besides, the nomogram prediction model yielded meaningful reduction in unnecessary biopsy rate (from 74.8 to 21.1% in PCa, and from 83.7 to 5.4% in csPCa).

CONCLUSIONS

The nomogram prediction model based on age, PI-RADS v2.1 and CEUS achieved an optimal prediction of PCa and csPCa. Using this model, the PCa risk for an individual patient can be estimated, which can lead to a rational biopsy choice.

ADVANCES IN KNOWLEDGE

This study gives an account of improving pre-biopsy risk stratification in males with "gray zone" PSA level through PI-RADS v2.1 and CEUS.

Semi-Automatic Multiparametric MR Imaging Classification Using Novel Image Input Sequences and 3D Convolutional Neural Networks

The role of multi-parametric magnetic resonance imaging (mp-MRI) is becoming increasingly important in the diagnosis of the clinical severity of prostate cancer (PCa). However, mp-MRI images usually contain several unaligned 3D sequences, such as DWI image sequences and T2-weighted image sequences, and there are many images among the entirety of 3D sequence images that do not contain cancerous tissue, which affects the accuracy of large-scale prostate cancer detection. Therefore, there is a great need for a method that uses accurate computer-aided detection of mp-MRI images and minimizes the influence of useless features. Our proposed PCa detection method is divided into three stages: (i) multimodal image alignment, (ii) automatic cropping of the sequence images to the entire prostate region, and, finally, (iii) combining multiple modal images of each patient into novel 3D sequences and using 3D convolutional neural networks to learn the newly composed 3D sequences with different modal alignments. We arrange the different modal methods to make the model fully learn the cancerous tissue features; then, we predict the clinical severity of PCa and generate a 3D cancer response map for the 3D sequence images from the last convolution layer of the network. The prediction results and 3D response map help to understand the features that the model focuses on during the process of 3D-CNN feature learning. We applied our method to Toho hospital prostate cancer patient data; the AUC (=0.85) results were significantly higher than those of other methods.

Artificial Intelligence Algorithm-Based MRI for Differentiation Diagnosis of Prostate Cancer

The rapid increase in prostate cancer (PCa) patients is similar to that of benign prostatic hyperplasia (BPH) patients, but the treatments are quite different. In this research, magnetic resonance imaging (MRI) images under the weighted low-rank matrix restoration algorithm (RLRE) were utilized to differentiate PCa from BPH. The diagnostic effects of different sequences of MRI images were evaluated to provide a more effective examination method for the clinical differential diagnosis of PCa and BPH. 150 patients with suspected PCa were taken as the research objects. Pathological examination revealed that 137 patients had PCa and 13 patients had BPH. The pathological results were the gold standard and were compared with the MRI results of different sequences. Therefore, the accuracy of the MRI results was evaluated. The results showed that with the rise of Gaussian noise, the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of all three algorithms gradually decreased, but the PSNR and SSIM of the RLRE algorithm were always higher than those of the RL and BM3D algorithms (P < 0.05). The sensitivity (97.08%), specificity (92.31%), accuracy (96.67%), and consistency (0.678) of the dynamic contrast enhancement (DCE) sequence were higher than those of the plain scan (86.13%, 69.23%, 84.67%, and 0.469, respectively). In conclusion, the RLRE algorithm could promote the resolution of MRI images and improve the display effect. DCE could better differentiate PCa from BPH, had great clinical application value, and was worthy of clinical promotion.

Exploration of the Value of Combined UA, IL-6, and fPSA/tPSA in the Diagnosis of Prostate Cancer

Objective

To investigate the differences in uric acid (UA), interleukin-6 (IL-6), and free prostatic-specific antigen (fPSA)/total prostatic-specific antigen (tPSA) (F/T) between patients with and without prostate cancer (PCa) in order to discover the value of the three indicators in improving PCa diagnostic accuracy.

Methods

Patients with pathologically diagnosed PCa (PCa group, n = 25), patients with other benign prostate diseases (benign group, n = 25), and men who underwent normal physical examination (control group, n = 25) at the First Affiliated Hospital of Guangzhou University of Chinese Medicine between October 2020 and January 2021 were included. The serum UA, IL-6, and F/T levels of participants in the three groups were measured, and the measured data were statistically analyzed.

Results

There were statistically significant differences in IL-6 and F/T among the three groups (all P < 0.05), but there were no statistically significant differences in UA (P > 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) for the three indicators was, respectively, as follows: PCa group-benign group 0.5416, 0.6776, and 0.6832; PCa group-control group 0.5432, 0.9536, and 0.9887; and benign group-control group 0.5000, 0.8784, and 0.9456. Logistic regression analysis indicated that IL-6 and F/T were independent predictors of PCa, with AUCs of 0.6776 and 0.6832, respectively, and a combined accuracy of 72.0%.

Conclusion

These results suggest that IL-6 and F/T have a good detection effect for PCa screening. Compared with the detection of F/T alone, the combined detection of IL-6 and F/T can improve the diagnosis rate of PCa to a certain extent, providing effective guidance for the clinical diagnosis and treatment of patients. The value of UA needs to be further studied, and its feasibility in the diagnosis of PCa needs to be further explored.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.