SelectMDx and Multiparametric Magnetic Resonance Imaging of the Prostate for Men Undergoing Primary Prostate Biopsy: A Prospective Assessment in a Multi-Institutional Study

Evaluation of blood and urine based biomarkers for detection of clinically-significant prostate cancer

BACKGROUND

Recognizing the limitations of prostate-specific antigen (PSA) screening and the morbidity of prostate biopsies, several blood- and urine-based biomarkers have been proposed for pre-biopsy risk stratification. These assays aim to reduce the frequency of unnecessary biopsies (i.e., negative or Grade Group 1 [GG1]) while maintaining highly sensitive detection of clinically significant cancer (GG ≥ 2) prostate cancer.

METHODS

We reviewed the literature describing the use of currently available blood- and urine-based biomarkers for detection of GG ≥ 2 cancer, including the Prostate Health Index (PHI), 4Kscore, MyProstateScore (MPS), SelectMDx, ExoDx Prostate Intelliscore (EPI), and IsoPSA. To facilitate clinical application, we focused on the use of biomarkers as a post-PSA secondary test prior to biopsy, as proposed in clinical guidelines. Our outcomes included test performance measures-sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV)-as well as clinical outcomes resulting from biomarker use (i.e., unnecessary biopsies avoided, GG ≥ 2 cancers missed).

RESULTS

Contemporary validation data (2015-2023) reveal that currently available biomarkers provide ~15-50% specificity at a sensitivity of 90-95% for GG ≥ 2 PCa. Clinically, this indicates that secondary use of biomarker testing in men with elevated PSA could allow for avoidance of up to 15-50% of unnecessary prostate biopsies, while preserving detection of 90-95% of GG ≥ 2 cancers that would be detected under the traditional "biopsy all" approach.

CONCLUSIONS

The contemporary literature further supports the proposed role of post-PSA biomarker testing to reduce the use of invasive biopsy while maintaining highly sensitive detection of GG ≥ 2 cancer. Questions remain regarding the optimal application of biomarkers in combination or in sequence with mpMRI.

Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

VOC-based detection of prostate cancer using an electronic nose and ion mobility spectrometry: A novel urine-based approach

BACKGROUND

Many diseases leave behind specific metabolites which can be detected from breath and urine as volatile organic compounds (VOC). Our group previously described VOC-based methods for the detection of bladder cancer and urinary tract infections. This study investigated whether prostate cancer can be diagnosed from VOCs in urine headspace.

METHODS

For this pilot study, mid-stream urine samples were collected from 56 patients with histologically confirmed prostate cancer. A control group was formed with 53 healthy male volunteers matched for age who had recently undergone a negative screening by prostate-specific antigen (PSA) and digital rectal exam. Headspace measurements were performed with the electronic nose Cyranose 320TM . Statistical comparison was performed using principal component analysis, calculating Mahalanobis distance, and linear discriminant analysis. Further measurements were carried out with ion mobility spectrometry (IMS) to compare detection accuracy and to identify potential individual analytes. Bonferroni correction was applied for multiple testing.

RESULTS

The electronic nose yielded a sensitivity of 77% and specificity of 62%. Mahalanobis distance was 0.964, which is indicative of limited group separation. IMS identified a total of 38 individual analytical peaks, two of which showed significant differences between groups (p < 0.05). To discriminate between tumor and controls, a decision tree with nine steps was generated. This model led to a sensitivity of 98% and specificity of 100%.

CONCLUSIONS

VOC-based detection of prostate cancer seems feasible in principle. While the first results with an electronic nose show some limitations, the approach can compete with other urine-based marker systems. However, it seems less reliable than PSA testing. IMS is more accurate than the electronic nose with promising sensitivity and specificity, which warrants further research. The individual relevant metabolites identified by IMS should further be characterized using gas chromatography/mass spectrometry to facilitate potential targeted rapid testing.

PI-RADS v2.1 evaluation of prostate "nodule in nodule" variants: clinical, imaging, and pathological features

OBJECTIVES

To analyze the correlation among the imaging features of prostate "nodule in nodule," clinical prostate indices, and pathology results.

METHODS

We retrospectively analyzed the prostate images from 47 male patients who underwent MRI scans and pathological biopsy from January 2022 to July 2023. Two radiologists (R1/R2) evaluated the morphology and signal intensity of the "nodule in nodule" in a double-blind manner and calculated the PI-RADS v2.1 score, which was compared with clinical prostate indices and pathological results.

RESULTS

34.04% (16/47) of patients were pathologically diagnosed with clinically significant prostate cancer (csPCa). Total prostate-specific antigen (tPSA), free/t PSA, PSA density (PSAD), and prostate gland volume (PGV) were significantly different between csPCa patients and benign prostatic hyperplasia (BPH) patients with prostate "nodule in nodule". R1/R2 detected 17/17 prostate "nodule in nodule" pathologically confirmed as csPCa on MRI; 10.60% (16/151) (R1) and 11.11% (17/153) (R2) had diffusion-weighted imaging (DWI) PI-RADS v2.1 score of 4, and 0.66% (1/151) (R1) had a score of 3. The percentages of encapsulated, circumscribed, and atypical nodules and obscured margins were 0.00% (0/151), 0.00% (0/151), 5.96% (9/151), and 5.30% (8/151), respectively, for R1, and 0.00% (0/153), 0.00% (0/153), 5.88% (9/153), and 4.58% (7/153) for R2.

CONCLUSION

When the inner nodules of "nodule in nodule" lesions in PI-RADS v2.1 category 1 in the TZ show incomplete capsulation or obscured margins, they are considered atypical nodules and might be upgraded to PI-RADS v2.1 category 3 if they exhibit marked diffusion restriction. However, further validation is needed.

CRITICAL RELEVANCE STATEMENT

This study first analyzed the relationship between clinical and pathological findings and the size, margin, and multimodal MRI manifestations of the prostate "nodule in nodule." These findings could improve the diagnostic accuracy of PI-RADS v2.1 for prostate lesions.

KEY POINTS

• The margin of the prostate inner nodules affects the PI-RADS v2.1 score. • The morphology of prostate "nodule in nodule" is related to their pathology. • The PI-RADS v2.1 principle requires consideration of prostate "nodule in nodule" variants.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

Importance

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion.

Objective

To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data.

Data Sources

PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022.

Study Selection

English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data.

Data Extraction and Synthesis

Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

Main Outcomes and Measures

Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis.

Results

The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15).

Conclusions and Relevance

These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

A meta-analysis for the diagnostic accuracy of SelectMDx in prostate cancer

To overview the diagnostic accuracy of SelectMDx for the detection of clinically significant prostate cancer and to review sources of methodologic variability. Four electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library were searched for eligible studies investigating the diagnostic value of SelectMDx compared with the gold standard. The pooled sensitivity, specificity, and positive and negative predictive values were calculated. Included studies were assessed according to the Standards for Quality Assessment of Diagnostic Accuracy Studies 2 tool. The review identified 14 relevant publications with 2579 patients. All reports constituted phase 1 biomarker studies. Pooled analysis of findings found an area under the receiver operating characteristic analysis curve of 70% [95% CI, 66%-74%], a sensitivity of 81% [95% CI, 69%-89%], and a specificity of 52% [95% CI, 41%-63%]. The positive likelihood ratio was 1.68, and the negative predictive value is 0.37. Factors that may influence variability in test results included the breath collection method, the patient's physiologic condition, the test environment, and the method of analysis. Considerable heterogeneity was observed among the studies owing to the difference in the sample size. SelectMDx appears to have moderate to good diagnostic accuracy in differentiating patients with clinically significant prostate cancer from people at high risk of developing prostate cancer. Higher-quality clinical studies assessing the diagnostic accuracy of SelectMDx for clinically significant cancer are still needed.

Evaluation of HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1 polymorphisms associated with prostate cancer in samples of patients from HUPE-UERJ

PURPOSE

Prostate cancer (PCa) is the leading cause of death among men in 48 countries. Genetic alterations play a significant role in PCa carcinogenesis. For the hypothesis of this research, five unique polymorphisms (SNP) were investigated in different genes that showed to be associated in different ways with PCa: rs4430796, rs2735839, rs4792311, rs12329760, and rs28931588, respectively for the genes HNF1B, KLK3, ELAC2, TMPRSS2-ERG, and CTNNB1.

PATIENTS AND METHODS

Blood samples from 426 subjects were evaluated: 290 controls (161 females and 129 males) and 136 PCa patients. SNP were determined by real-time polymerase chain reaction. TaqMan SNP genotyping assay. In the control samples, the SNPs were defined in association with the self-reported ethnicity, and in 218 control samples with markers with ancestry indicators. The genes were in Hardy-Weinberg equilibrium. One hundred and seventy control samples were matched by ethnicity for comparison with the PCa samples.

RESULTS

The G allele at rs28931588 was monomorphic in both patients and controls studied. Significant differences were observed in allelic and genotypic frequencies between the control and Pca samples in rs2735839 (KLK3; p = 0.002 and χ2  = 8.73 and p = 0.01, respectively), by the global frequency and in the dominant model rs2735839_GG (odds ratio [OR] = 0.51, p = 0.02). AA and GA genotypes at rs4792311 (ELAC2) were more frequent in patients with Gleason 7(4 + 3), 8, and 9 (n = 37%-59.7%) compared to patients with Gleason 6 and 7(3 + 4) (n = 26%-40.0%) conferring a protective effect on the GG genotype (OR = 0.45, p = 0.02). The same genotype showed an OR = 2.71 (p = 0.01) for patients with low severity. The HNF1B-KLK3-ELAC2-TMPRSS2-ERG haplotypes: GAAT, AAAT, GAGT, and AAGT were more frequent in patients with Pca with OR ranging from 4.65 to 2.48.

CONCLUSIONS

Higher frequencies of risk alleles were confirmed in the SNPs, KLK3 rs2735839_A, ELAC2 rs4792311_A, and TMPRSS2 rs12329760_T in patients with Pca. Rs2735839_A was associated with risk of Pca and rs4792311_A with severity and Gleason score of 7(4 + 3) or greater. There is a need for careful observation of rs2735839 and rs4792311 in association with the prostatic biopsy due to the increased risk of Pca.

Investigating Efficient Risk-Stratified Pathways for the Early Detection of Clinically Significant Prostate Cancer

Risk-stratified pathways (RSPs) are recommended by the European Association of Uro-logy (EAU) to improve the early detection of clinically significant prostate cancer (csPCa). RSPs can reduce magnetic resonance imaging (MRI) demand, prostate biopsies, and the over-detection of insignificant PCa (iPCa). Our goal is to analyze the efficacy and cost-effectiveness of several RSPs by using sequential stratifications from the serum prostate-specific antigen level and digital rectal examination, the Barcelona risk calculators (BCN-RCs), MRI, and Proclarix™. In a cohort of 567 men with a serum PSA level above 3.0 ng/mL who underwent multiparametric MRI (mpMRI) and targeted and/or systematic biopsies, the risk of csPCa was retrospectively assessed using Proclarix™ and BCN-RCs 1 and 2. Six RSPs were compared with those recommended by the EAU that, stratifying men from MRI, avoided 16.7% of prostate biopsies with a prostate imaging-reporting and data system score of <3, with 2.6% of csPCa cases remaining undetected. The most effective RSP avoided mpMRI exams in men with a serum PSA level of >10 ng/mL and suspicious DRE, following stratifications from BCN-RC 1, mpMRI, and Proclarix™. The demand for mpMRI decreased by 19.9%, prostate biopsies by 19.8%, and over-detection of iPCa by 22.7%, while 2.6% of csPCa remained undetected as in the recommended RSP. Cost-effectiveness remained when the Proclarix™ price was assumed to be below EUR 200.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

Diagnostic Accuracy of Liquid Biomarkers for Clinically Significant Prostate Cancer Detection: A Systematic Review and Diagnostic Meta-analysis of Multiple Thresholds

CONTEXT

Many liquid biomarkers have entered clinical practice with the praise to improve the detection of clinically significant prostate cancer (csPCa), helping avoid unnecessary prostate biopsies.

OBJECTIVE

We aimed to assess the diagnostic accuracy of multianalyte biomarkers for csPCa detection using multiple thresholds.

EVIDENCE ACQUISITION

A comprehensive literature search was done through PubMed, Web of Science, and Scopus in March 2023 for prospective and retrospective studies reporting the diagnostic performance of liquid biomarkers for detecting csPCa. The outcomes of interest were the diagnostic performance of liquid biomarkers for csPCa detection and identification of optimal thresholds for each biomarker.

EVIDENCE SYNTHESIS

Overall, 49 studies were eligible for this meta-analysis. Using each representative threshold based on the Youden Index, the pooled sensitivity and specificity for detecting csPCa were 0.85 and 0.37 for prostate cancer gene 3 (PCA3), 0.85 and 0.52 for prostate health index (PHI), 0.87 and 0.58 for four kallikrein (4K), 0.82 and 0.56 for SelectMDx, 0.85 and 0.54 for ExoDx, and 0.82 and 0.59 for mi prostate score (MPS), respectively. The diagnostic odds ratio was highest for 4K (8.84), followed by MPS (7.0) and PHI (6.28). According to the meta-analysis incorporating multiple thresholds, the corresponding sensitivity was 0.77 for 4K, 0.69 for PHI, and 0.63 for PCA3; specificity was 0.72 for PHI, 0.70 for 4K, and 0.69 for PCA3.

CONCLUSIONS

Regarding the detection of csPCa, 4K had the highest diagnostic performance among the commercial liquid biomarkers. Based on the optimal thresholds calculated by the present meta-analysis, 4K had the highest sensitivity and PHI had the highest specificity for detecting csPCa. Nevertheless, clinical decision-making requires combination strategies between liquid and imaging biomarkers.

PATIENT SUMMARY

Novel biomarkers for prostate cancer detection were useful for more accurate diagnosis of clinically significant prostate cancer to avoid unnecessary biopsies.

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression

BACKGROUND AND OBJECTIVE

Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations.

METHODS

A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from 'The Cancer Genome Atlas'. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision.

RESULTS

An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK.

CONCLUSIONS

This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are consistent with each other and with the literature, opening a horizon for its application in clinical practice. In the near future, these genes, in combination with our model, could be applied to liquid biopsy to improve PCa screening.

Beyond blood biomarkers: the role of SelectMDX in clinically significant prostate cancer identification

INTRODUCTION

New potential biomarkers to pre-intervention identification of a clinically significant prostate cancer (csPCa) will prevent overdiagnosis and overtreatment and limit quality of life impairment of PCa patients.

AREAS COVERED

We have developed a comprehensive review focusing our research on the increasing knowledge of the role of SelectMDX® in csPCa detection. Areas identified as clinically relevant are the ability of SelectMDX® to predict csPCa in active surveillance setting, its predictive ability when combined with multiparametric MRI and the role of SelectMDX® in the landscape of urinary biomarkers.

EXPERT OPINION

Several PCa biomarkers have been developed either alone or in combination with clinical variables to improve csPCa detection. SelectMDX® score includes genomic markers, age, PSA, prostate volume, and digital rectal examination. Several studies have shown consistency in the ability to improve detection of csPCa, avoidance of unnecessary prostate biopsies, helpful in decision-making for clinical benefit of PCa patients with future well designed, and impactful studies.

Optimal PSA density threshold and predictive factors for the detection of clinically significant prostate cancer in patient with a PI-RADS 3 lesion on MRI

INTRODUCTION

While Prostate Imaging Reporting and Data System (PI-RADS) 4 and 5 lesions usually justify prostate biopsy (PBx), the management of a PI-RADS 3 lesion can be discussed. The aim of our study was to determine the optimal prostate-specific antigen density (PSAD) threshold and predictive factors of clinically significant prostate cancer (csPCa) in patients with a PI-RADS 3 lesion on MRI.

PATIENTS AND METHODS

Using our prospectively maintained database, we conducted a monocentric retrospective study, including all patients with a clinical suspicious of prostate cancer (PCa), all of them had a PI-RADS 3 lesion on the mpMRI prior to PBx. Patients under active surveillance or displaying suspicious digital rectal examination were excluded. Clinically significant (csPCa) was defined as PCa with any ISUP grade group ≥ 2 (Gleason ≥ 3 + 4).

RESULTS

We included 158 patients. The detection rate of csPCa was 22.2%. In case of PSAD ≤ 0.15 ng/ml/cm³, PBx would be omitted in 71.5% (113/158) of men at the cost of missing 15.0% (17/113) of csPCa. With a threshold of 0.15 ng/ml/cm³, the sensitivity and the specificity were 0.51 and 0.78 respectively. The positive predictive value was 0.40 and the negative predictive value was 0.85. According to multivariate analysis, age (OR = 1.10, CI95% 1.03-1.19, P = 0.007), and PSAD ≥ 0.15 ng/ml/cm³ (OR = 3.59, CI95% 1.41-9.47, P = 0.008) were independent predictive factors of csPCa. Previous negative PBx was negatively associated with csPCa (OR = 0.24, CI 95% 0.07-0.66, P = 0.01).

CONCLUSION

Our result suggests that the optimal PSAD threshold was 0.15 ng/ml/cm³. However, in this case omitting PBx in 71.5% of cases would be at the cost of missing 15.0% of csPCa. PSAD should not be used alone, other predictive factors as age and PBx history should also be considered in the discussion with the patient, to avoid PBx while missing few csPCa.

Contrast-enhanced ultrasound combined targeted microbubbles for diagnosis of highly aggressive papillary thyroid carcinoma

Background

Accurate diagnosis of highly aggressive papillary thyroid cancer (PTC) may greatly help avoid overdiagnosis and overtreatment of PTC. However, there is still a lack of a convenient and accurate method. Targeted microbubbles, an emerging ultrasound contrast agent, have the potential to accurately diagnose highly aggressive PTC.

Purpose

To design and prepare a targeted microbubble for specific contrast-enhanced ultrasound (CEUS) imaging of highly invasive PTC.

Methods

Using β-galactoside-binding protein galectin-3 (Gal-3) overexpressed on the surface of highly invasive PTC cells as a target, C12 polypeptide (ANTPCGPYTHDCPVKR) with high affinity and specificity for Gal-3 was coupled to the surface of lipid microbubbles to prepare targeted microbubbles (Gal-3-C12@lipo MBs). The targeted microbubbles were prepared by thin-film hydration method and mechanical shaking method. The morphology, diameter, concentration and stability of microbubbles were investigated by fluorescence microscopy and an AccuSizer. The biosafety of microbubbles was studied using BCPAP cells through CCK8 assay. Confocal laser scanning microscope and flow cytometry were applied to research the cellular uptake of microbubbles to investigate the targeting ability to highly aggressive PTC. Finally, the specific contrast-enhanced ultrasound imaging of microbubbles in highly invasive PTC was validated on the mice bearing subcutaneous BCPAP tumor model via a clinically ultrasound imaging system.

Results

Gal-3-C12@lipo MBs were successfully prepared which showed a well-defined spherical morphology with an average diameter of 1.598 ± 0.848 μm. Gal-3-C12@lipo MBs showed good stability without rupture within 4 hours after preparation. At the cellular level, Gal-3-C12@lipo MBs exhibited favorable biosafety and superior targeting ability to BCPAP cells, with 2.8-fold higher cellular uptake than non-targeted lipid microbubbles (Lipo MBs). At the animal level, Gal-3-C12@lipo MBs significantly improved the quality of contrast-enhanced ultrasound imaging in highly invasive PTC, with an echo intensity of tumor significantly higher than that of Lipo MBs.

Conclusion

We designed and fabricated a novel targeted microbubble for the specific ultrasound imaging diagnosis of highly aggressive PTC. The targeted microbubbles have good stability, superior biosafety and high targeting specificity, which can significantly improve the tumor signal-to-noise ratio of highly invasive PTC, and have the potential to facilitate and accurately diagnose highly invasive PTC.

Should men undergo MRI before prostate biopsy - CON

Prostate magnetic resonance imaging (MRI) is increasingly used prior to biopsy in response to the overdiagnosis and overtreatment of prostate cancer (CaP) associated with prostate-specific antigen (PSA) based screening. However, technical limitations in the conventional diffusion-weighted imaging (DWI) sequences as well as the high degree of radiologist-to-radiologist variability in interpreting prostate MRI result in inadequate accuracy. Specifically, the insufficient negative predictive value (NPV) of prostate MRI (76%-87%) does not allow biopsy to be omitted in the negative MRI setting. Additionally, the variable, and relatively low positive predictive value (PPV) of MRI (27%-44%) provides only an incremental improvement in risk prediction compared to readily available clinical tools such as the Prostate Cancer Prevention Trial risk calculator. This small benefit is likely confined to the minority of patients with positive MRI findings in a typically under-sampled region of the prostate (e.g., anterior lesions), which may be obviated by newer biopsy approaches and tools such as transperineal prostate biopsy and micro-ultrasound technology. With these considerations in mind, pre-biopsy prostate MRI in its current form is unlikely to provide a clinically significant benefit, and should not be considered as routine practice until its accuracy is sufficiently improved.

Biomarkers of Aggressive Prostate Cancer at Diagnosis

In the United States, prostate cancer (CaP) remains the second leading cause of cancer deaths in men. CaP is predominantly indolent at diagnosis, with a small fraction (25-30%) representing an aggressive subtype (Gleason score 7-10) that is prone to metastatic progression. This fact, coupled with the criticism surrounding the role of prostate specific antigen in prostate cancer screening, demonstrates the current need for a biomarker(s) that can identify clinically significant CaP and avoid unnecessary biopsy procedures and psychological implications of being diagnosed with low-risk prostate cancer. Although several diagnostic biomarkers are available to clinicians, very few comparative trials have been performed to assess the clinical effectiveness of these biomarkers. It is of note, however, that a majority of these clinical trials have been over-represented by men of Caucasian origin, despite the fact that African American men have a 1.7 times higher incidence and 2.1 times higher rate of mortality from prostate cancer. Biomarkers for CaP diagnosis based on the tissue of origin include urine-based gene expression assays (PCA3, Select MDx, ExoDx Prostate IntelliScore, Mi-Prostate Score, PCA3-PCGEM1 gene panel), blood-based protein biomarkers (4K, PHI), and tissue-based DNA biomarker (Confirm MDx). Another potential direction that has emerged to aid in the CaP diagnosis include multi-parametric magnetic resonance imaging (mpMRI) and bi-parametric magnetic resonance imaging (bpMRI), which in conjunction with clinically validated biomarkers may provide a better approach to predict clinically significant CaP at diagnosis. In this review, we discuss some of the adjunctive biomarker tests along with newer imaging modalities that are currently available to help clinicians decide which patients are at risk of having high-grade CaP on prostate biopsy with the emphasis on clinical utility of the tests across African American (AA) and Caucasian (CA) men.

Performance of Node-RADS Scoring System for a Standardized Assessment of Regional Lymph Nodes in Bladder Cancer Patients

BACKGROUND

Current cross-sectional imaging modalities exhibit heterogenous diagnostic performances for the detection of a lymph node invasion (LNI) in bladder cancer (BCa) patients. Recently, the Node-RADS score was introduced to provide a standardized comprehensive evaluation of LNI, based on a five-item Likert scale accounting for both size and configuration criteria. In the current study, we hypothesized that the Node-RADS score accurately predicts the LNI and tested its diagnostic performance.

METHODS

We retrospectively reviewed BCa patients treated with radical cystectomy (RC) and bilateral extended pelvic lymph node dissection, from January 2019 to June 2022. Patients receiving preoperative systemic chemotherapy were excluded. A logistic regression analysis tested the correlation between the Node-RADS score and LNI both at patient and lymph-node level. The ROC curves and the AUC depicted the overall diagnostic performance. In addition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for different cut-off values (>1, >2, >3, >4).

RESULTS

Overall, data from 49 patients were collected. Node-RADS assigned on CT scans images, was found to independently predict the LNI after an adjusted multivariable regression analysis, both at the patient (OR 3.36, 95%CI 1.68-9.40, p = 0.004) and lymph node (OR 5.18, 95%CI 3.39-8.64, p < 0.001) levels. Node-RADS exhibited an AUC of 0.87 and 0.91 at the patient and lymph node levels, respectively. With increasing Node-RADS cut-off values, the specificity and PPV increased from 57.1 to 97.1% and from 48.3 to 83.3%, respectively. Conversely, the sensitivity and NPV decreased from 100 to 35.7% and from 100 to 79.1%, respectively. Similar trends were recorded at the lymph node level. Potentially, Node-RADS > 2 could be considered as the best cut-off value due to balanced values at both the patient (77.1 and 78.6%, respectively) and lymph node levels (82.4 and 93.4%, respectively).

CONCLUSIONS

The current study lays the foundation for the introduction of Node-RADS for the regional lymph-node evaluation in BCa patients. Interestingly, the Node-RADS score exhibited a moderate-to-high overall accuracy for the identification of LNI, with the possibility of setting different cut-off values according to specific clinical scenarios. However, these results need to be validated on larger cohorts before drawing definitive conclusions.

Comparative Effectiveness of Magnetic Resonance Imaging-Ultrasound Fusion Versus In-bore Magnetic Resonance Imaging-targeted Prostate Biopsy

OBJECTIVE

To examine the comparative effectiveness of magnetic resonance imaging-ultrasound (MRI-U/S) fusion biopsy and in-bore MRI-targeted biopsy.

METHODS

We identified men aged 18-89 with a diagnosis of elevated prostate specific antigen (PSA) or Gleason 6 prostate cancer on active surveillance who underwent MRI-U/S fusion prostate biopsy (12-core + targeted) in the office or in-bore MRI-targeted biopsy (MRI-IB; targeted only). The cancer detection rate (CDR; Gleason 6-10) and clinically significant CDR (csCDR; Gleason 7-10) were compared across biopsy techniques, adjusted for patient and radiographic features.

RESULTS

A total of 280 patients (346 lesions) were included, of whom 23.9% were on active surveillance for Gleason 6 prostate cancer. In the per-patient analyses, there was no statistically significant difference in adjusted overall CDR (64.1% vs 54.2%; P = .24) or csCDR (36.5% vs 37.9%; P = .85) between MRI-U/S and MRI-IB biopsy. In the per-lesion analyses, there was no statistically significant difference in adjusted overall CDR (45.7% vs 50.1%; P = .49) between MRI-U/S and MRI-IB biopsy, but MRI-IB biopsy was associated with a higher csCDR than MRI-U/S biopsy (32.8% vs 21.4%; P = .02).

CONCLUSION

We observed no statistically significant differences in cancer detection rates between MRI-U/S fusion biopsy and MRI-IB biopsy in per-patient analyses. However, MRI-IB biopsy was associated with higher csCDR when considering targeted biopsy cores only. These results suggest that systematic cores should be obtained when performing MRI-U/S fusion biopsy.

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.

Accuracy of novel urinary biomarker tests in the diagnosis of prostate cancer: A systematic review and network meta-analysis

Objective

The purpose of this study was to conduct a network meta-analysis comparing the diagnostic value of different urinary markers for prostate cancer.

Methods

As of June 2022, the literature was retrieved by searching Pubmed, EMBASE, Web of Science databases and other databases. The methodological quality of included studies was assessed using the Cochrane Collaboration’s risk of bias tool, and publication bias was assessed using funnel plots. The surface under the cumulative ranking curve (SUCRA) values ​​was used to determine the most effective diagnostic method and the data were analyzed accordingly using data analysis software.

Results

A total of 16 articles was included including 9952 patients. The ranking results of network meta-analysis showed that the diagnostic performance of the four urine markers Selectmdx, MIPS, PCA3 and EPI was better than that of PSA. Among them, the specificity, positive predictive value and diagnostic accuracy of Selectmdx ranked first in the SUCRA ranking (SUCRA values: 85.2%, 88.3%, 97.1%), and the sensitivity ranked second in the SUCRA ranking (SUCRA value: 54.4%), and the negative predictive value ranked fourth in SUCRA (SUCRA value: 51.6%). The most sensitive screening tool was MIPS (SUCRA value: 67.1%), and it was also the second screening tool ranked higher in specificity, positive predictive value, negative predictive value and diagnostic accuracy (SUCRA value: 56.5%, respectively)., 57.1%, 67.9%, 74.3%). The high negative predictive value SUCRA ranking is EPI (SUCRA value: 68.0%), its sensitivity ranks third (SUCRA value: 45.6%), and its specificity, positive predictive value and diagnostic accuracy are ranked fourth (SUCRA values are: 45%, 38.2%, 35.8%).

Conclusion

According to the network ranking diagram, we finally concluded that Selectmdx and MIPS can be used as the most suitable urine markers for prostate cancer screening and diagnosis. To further explore the diagnostic value of different urinary markers in the screening of PCa patients.

Systematic Review Registration

https://inplasy.com/, identifier INPLASY202290094.

Computer classification and construction of a novel prognostic signature based on moonlighting genes in prostate cancer

Advanced prostate cancer (PRAD) patients have poor prognosis and rising morbidity despite the ongoing iteration of molecular therapeutic agents. As newly discovered proteins with several functions, Moonlighting proteins have showed an important role in tumor progression but has not been extensively investigated in PRAD. Our study aimed to identify moonlighting-related prognostic biomarkers and prospective PRAD therapy targets. 103 moonlighting genes were gathered from previous literatures. A PRAD classification and multivariate Cox prognostic signature were constructed using dataset from The Cancer Genome Atlas (TCGA). Subsequently, we tested our signature’s potential to predict biochemical failure-free survival (BFFS) using GSE21032, a prostate cancer dataset from Gene Expression Omnibus (GEO). The performance of this signature was demonstrated by Kaplan-Meier (KM), receiver operator characteristic (ROC), areas under ROC curve (AUC), and calibration curves. Additionally, immune infiltration investigation was conducted to determine the impact of these genes on immune system. This signature’s influence on drug susceptibility was examined using CellMiner’s drug database. Both training and validation cohorts demonstrated well predictive capacity of this 9-gene signature for PRAD. The 3-year AUCs for TCGA-PRAD and GSE21032 were 0.802 and 0.60 respectively. It can effectively classify patients into various biochemical recurrence risk groups. These genes were also assessed to be connected with tumor mutation burden (TMB), immune infiltration and therapy. This work created and validated a moonlighting gene signature, revealing fresh perspectives on moonlighting proteins in predicting prognosis and improving treatment of PRAD.

Comparison of systematic randomized 12-core transrectal ultrasonography-guided prostate biopsy with magnetic resonance imaging-transrectal ultrasonography fusion-targeted prostate biopsy

We aimed to compare the complications and pathological outcomes between systematic 12-core transrectal ultrasonography guided prostate biopsy (TRUS-PB) and magnetic resonance imaging-TRUS fusion targeted prostate biopsy (MRI-TRUS FTPB). We examined 10,901 patients who underwent prostate biopsy from May 2003 to December 2017 retrospectively. Among them, 10,325 patients underwent 12-core TRUS-PB and 576 patients underwent MRI-TRUS FTPB. The clinicopathological features and complications in both groups were compared. After propensity score matching, there were no significant differences in the clinical features and complication rates between both groups (P > .05). In the multivariate analyses, the prostate volume was shown to be the only significant predictor of overall complications, infectious complications, bleeding related complications, and Clavien-Dindo grade ≥ 2 complications after prostate biopsy (P < .001). The present study demonstrates the safety of MRI-TRUS FTPB in terms of complications, compared with that of TRUS-PB. Although the combination of MRI-TRUS FTPB and 12-core TRUS-PB provides enhanced diagnostic power, MRI-TRUS FGB alone could provide a reasonable diagnostic value for prostate cancer if the apparent diffusion coefficient suspicious grade of prostate cancer is ≥4. When the Likert suspicious grade of prostate cancer on the apparent diffusion coefficient map of multiparametric MRI was 3, 13.9% (27/194) of the patients were diagnosed with clinically significant prostate cancer (csPCa); 44.4% (12/27) of them were confirmed as csPCa at the MRI-targeted cores. When the apparent diffusion coefficient suspicious grade was ≥4, 43.0% (108/251) were diagnosed with csPCa; 76.8% (83/108) of them were confirmed to have csPCa at the MRI-targeted cores.

Urinary PCA3 a Superior Diagnostic Biomarker for Prostate Cancer among Ghanaian Men

Introduction. Prostate cancer is one of the most commonly diagnosed cancers in men. Prostate-specific antigen (PSA) has been the biomarker of choice for screening and diagnosis of prostate cancer. However, inefficiencies exist with its diagnostic capabilities. This study thus evaluated the diagnostic and prognostic potential of urinary PCA3 as an alternative biomarker for prostate cancer in the Ghanaian population. Methods. A hospital-based cross-sectional study was conducted at the Urology Department of the 37 Military Hospital, Accra, Ghana. A total of 237 participants aged 40 years and above with any form of suspected prostate disorder were recruited into the study after written informed consent was obtained. Total serum PSA levels was measured using the electrochemiluminescence method and transrectal ultrasound-guided systematic core needle biopsies were obtained from each study participant. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracies of serum PSA, DRE, and PCA3 as diagnostic tools for prostate cancer. These three diagnostic tools were also evaluated in various combinations to ascertain the combinations with the best diagnostic accuracy. Results. Prostate cancer was diagnosed in 26.6% of the participants. Benign prostate hyperplasia and prostatitis were diagnosed in 48.5% and 24.9% participants, respectively. DRE had a sensitivity of 93.7% and a specificity of 12.1%. PSA had a sensitivity of 92.1% and a specificity of 16.1%. PCA3 had a sensitivity of 57.1% and a specificity of 85.6% and showed a better accuracy ( $\text{AUC}=83.0$ ) compared to PSA ( $\text{AUC}=60.0$ ) and DRE ( $\text{AUC}=65.0$ ) as individual diagnostic tools. The combination of DRE+PCA3 score had the best diagnostic accuracy ( $\text{AUC}=0.80$ ) with a sensitivity and specificity of 60.3% and 80.5%, respectively. Conclusion. The urinary PCA3 assay showed a better diagnostic performance compared to serum PSA and DRE. PCA3 as a stand-alone and in combination with DRE could be a suitable complimentary marker in diagnosis and management of prostate cancer.

Evaluation of Physicochemical Properties, Pharmacokinetics, Biodistribution, Toxicity, and Contrast-Enhanced Cancer MRI of a Cancer-Targeting Contrast Agent, MT218

OBJECTIVES

Preclinical assessments were performed according to the US Food and Drug Administration guidelines to determine the physicochemical properties, pharmacokinetics, clearance, safety, and tumor-specific magnetic resonance (MR) imaging of MT218, a peptidic gadolinium-based MR imaging agent targeting to extradomain B fibronectin for MR molecular imaging of aggressive tumors.

MATERIALS AND METHODS

Relaxivity, chelation stability, binding affinity, safety-related target profiling, and effects on CYP450 enzymes and transporters were evaluated in vitro. Magnetic resonance imaging was performed with rats bearing prostate cancer xenografts, immunocompetent mice bearing murine pancreatic cancer allografts, and mice bearing lung cancer xenografts at different doses of MT218. Pharmacological effects on cardiovascular, respiratory, and central nervous systems were determined in rats and conscious beagle dogs. Pharmacokinetics were tested in rats and dogs. Biodistribution and excretion were studied in rats. Single and repeated dosing toxicity was evaluated in rats and dogs. In vitro and in vivo genotoxicity, in vitro hemolysis, and anaphylactic reactivity were also performed.

RESULTS

At 1.4 T, the r1 and r2 relaxivities of MT218 were 5.43 and 7.40 mM -1 s -1 in pure water, 6.58 and 8.87 mM -1 s -1 in phosphate-buffered saline, and 6.54 and 8.70 mM -1 s -1 in aqueous solution of human serum albumin, respectively. The binding affinity of MT218 to extradomain B fragment is 3.45 μM. MT218 exhibited no dissociation of the Gd(III) chelates under physiological conditions. The peptide degradation half-life ( t1/2 ) of MT218 was 1.63, 5.85, and 2.63 hours in rat, dog, and human plasma, respectively. It had little effect on CYP450 enzymes and transporters. MT218 produced up to 7-fold increase of contrast-to-noise ratios in the extradomain B fibronectin-rich tumors with a dose of 0.04 mmol/kg for at least 30 minutes. MT218 had little pharmacological effect on central nervous, cardiovascular, or respiratory systems. MT218 had a mean plasma elimination half-life ( t1/2 ) of 0.31 and 0.89 hours in rats and dogs at 0.1 mmol/kg, respectively. No detectable Gd deposition was observed in the brain at 6 hours postinjection of MT218 at 0.1 mmol/kg in rats. MT218 was not mutagenic and had no mortality or morbidity in the rats or dogs up to 1.39 and 0.70 mmol/kg/d, respectively. The no observed adverse effect level of MT218 in Sprague-Dawley rats was 1.39 mmol/kg for single dosing and 0.46 mmol/kg/d for repeated dosing. The no observed adverse effect level in dogs was 0.07 mmol/kg/d. MT218 exhibited no genotoxicity, hemolysis, and anaphylactic reactivity.

CONCLUSION

The preclinical assessments showed that the targeted contrast agent MT218 has high r1 and r2 relaxivities, satisfactory physicochemical properties, pharmacokinetic, and safety profiles and produces effective tumor enhancement in multiple cancer types in rats and mice at reduced doses.

Clinical use of the mRNA urinary biomarker SelectMDx test for prostate cancer

Background

Molecular biomarker tests are developed as diagnostic tools for prostate cancer (PCa) diagnosis. The SelectMDx (MDxHealth, Nijmegen, The Netherlands) test is a urinary-based biomarker test intended to be used to predict presence of high-grade PCa upon biopsy in men with elevated serum prostate-specific antigen (PSA) levels. Previous validation of the SelectMDx test revealed that 53% of the unnecessary biopsies (biopsies indicating no- or GG1 PCa) could be avoided using the SelectMDx test as a decision-tool to select men for prostate biopsy. The objective of this study is to examine the use of the commercially available SelectMDx test under routine, real-life practice.

Methods

Men that underwent a SelectMDx test between May 2019 and December 2020 and that were originating from countries that perform the SelectMDx test on a regular basis were included in this study, resulting in 5157 cases from 10 European countries. Clinical parameters, urinary RNA scores, and test outcomes were compared between PSA groups, age groups, countries, and the validation cohort (described previously [4]) using the Mann–Whitney U test, Chi-Square test, Benjamini–Hochberg and Kruskal–Wallis tests.

Results

40.72% of the cases received a negative SelectMDx result. The test is also used in patients outside the intended-use population (PSA < 3 and >10 ng/mL). Clinical parameters (age, PSA density, DRE outcome) varied between patient population from individual countries and the validation cohort, resulting in differences in the potential number of saved biopsies using the test.

Conclusions

The potential number of reduced biopsies in clinical use was 40,72% using the SelectMDx test, assuming a negative SelectMDx test resulted in the decision not to biopsy the patient. This is higher compared to the validation cohort, which is explained by differences in patient population.

Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion

Purpose

To assess the association of radiomics features based on multiparametric MRI (mpMRI) with the proportion of intraductal carcinoma of prostate (IDC-P) and validate the predictive models.

Materials and Methods

We retrospectively included pre-treatment MR images of prostate cancer (PCa) with IDC components of high proportion (≥10%, hpIDC-P), low proportion (<10%, lpIDC-P), and pure acinar adenocarcinoma (PAC) from our institution for training and internal validation and cooperated cohort for external validation. Normalized images of T2WI, diffusion weighted imaging (DWI) and apparent diffusion coefficient (ADC) map, and dynamic contrast enhanced (DCE) sequences were used for radiomics modeling. The clinical model was built based on serum total prostate specific antigen (tPSA) and Gleason score (GS), and the integrated model was the combination of Rad-score and clinicopathological data. The discrimination ability was assessed by area under the receiver operating characteristic curve (ROC-AUC) in the internal and external validation sets and compared by DeLong test.

Results

Overall, 97 patients with hpIDC-P, 87 lpIDC-P, and 78 PAC were included for training and internal validation, and 11, 16, and 19 patients for external validation. The integrated model for predicting hpIDC-P got the best ROC-AUC of 0.88 (95%CI = 0.83-0.93) in internal and 0.86 (95%CI = 0.72-1.0) in external validation, which both outperformed clinical models (AUC=0.78, 95% CI = 0.72-0.85, AUC=0.69, 95% CI = 0.5-0.85, respectively) based solely on GS, and the radiomics model (AUC=0.85, 95% CI = 0.79-0.91) was slightly inferior to the integrated model and better than the clinical model in internal dataset. The integrated model for predicting lpIDC-P outperformed both radiomics and clinical models in the internal dataset, while slightly inferior to the integrated model for predicting hpIDC-P.

Conclusions

Radiomics signature improved differentiation of both hpIDC-P and lpIDC-P versus PAC when compared with the clinical model based on Gleason score, and was validated in an external cohort.

The Application of Biopsy Density in Transperineal Templated-Guided Biopsy Patients With PI-RADS<3

Background

In patients with multiparameter magnetic resonance imaging (mpMRI) low-possibility but highly clinical suspicion of prostate cancer, the biopsy core is unclear. Our study aims to introduce the biopsy density (BD; the ratio of biopsy cores to prostate volume) and investigates the BD-predictive value of prostate cancer and clinically significant prostate cancer (csPCa) in PI-RADS<3 patients.

Methods

Patients underwent transperineal template–guided prostate biopsy from 2012 to 2022. The inclusion criteria were PI-RADS<3 with a positive digital rectal examination or persistent PSA abnormalities. BD was defined as the ratio of the biopsy core to the prostate volume. Clinical data were collected, and we grouped the patients according to pathology results. Kruskal–Wallis test and chi-square test were used in measurement and enumeration data, respectively. Logistics regression was used to choose the factor associated with positive biospy and csPCa. The receiver operating characteristic (ROC) curve was used to evaluate the ability to predict csPCa.

Results

A total of 115 patients were included in our study. Biopsy was positive in 14 of 115 and the International Society of Urological Pathology grade groups 2–5 were in 7 of all the PCa patients. The BD was 0.38 (0.24-0.63) needles per milliliter. Binary logistics analysis suggested that PSAD and BD were correlated with positive biopsy. Meanwhile, BD and PSAD were associated with csPCa. The ROC curve illustrated that BD was a good parameter to predict csPCa (AUC=0.80, 95% CI: 0.69-0.91, p<0.05). The biopsy density combined with PSAD increased the prediction of csPCa (AUC=0.90, 95% CI: 0.85-0.97, p<0.05). The cut-off value of the BD was 0.42 according to the Youden index.

Conclusion

In PI-RADS<3 patients, BD and PSAD are related to csPCa. A biopsy density of more than 0.42 needles per millimeter can increase the csPCa detection rate, which should be considered as an alternative biopsy method when we perform prostate biopsy in patients with PI-RADS<3.

Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy

This study is a head-to-head comparison between mPSAD and MRI-PMbdex. The MRI-PMbdex was created from 2432 men with suspected PCa; this cohort comprised the development and external validation cohorts of the Barcelona MRI predictive model. Pre-biopsy 3-Tesla multiparametric MRI (mpMRI) and 2 to 4-core transrectal ultrasound (TRUS)-guided biopsies for suspicious lesions and/or 12-core TRUS systematic biopsies were scheduled. Clinically significant PCa (csPCa), defined as Gleason-based Grade Group 2 or higher, was detected in 934 men (38.4%). The area under the curve was 0.893 (95% confidence interval [CI]: 0.880−0.906) for MRI-PMbdex and 0.764 (95% CI: 0.774−0.783) for mPSAD, with p < 0.001. MRI-PMbdex showed net benefit over biopsy in all men when the probability of csPCa was greater than 2%, while mPSAD did the same when the probability of csPCa was greater than 18%. Thresholds of 13.5% for MRI-PMbdex and 0.628 ng/mL2 for mPSAD had 95% sensitivity for csPCa and presented 51.1% specificity for MRI-PMbdex and 19.6% specificity for mPSAD, with p < 0.001. MRI-PMbdex exhibited net benefit over mPSAD in men with prostate imaging report and data system (PI-RADS) <4, while neither exhibited any benefit in men with PI-RADS 5. Hence, we can conclude that MRI-PMbdex is more accurate than mPSAD for the proper selection of candidates for prostate biopsy among men with suspected PCa, with the exception of men with a PI-RAD S 5 score, for whom neither tool exhibited clinical guidance to determine the need for biopsy.

Comparison of a Personalized Prostate Biopsy Pattern With Traditional Transrectal Prostate Biopsy: Different Cancer Detection Rate

Background: In terms of prostate biopsy approaches, it is difficult to reach the ventral central region of the prostate with the traditional transrectal prostate biopsy, while with the transperineal biopsy, the tumor in the dorsolateral region of the prostate is easily missed. However, until now, no studies have investigated the biopsy accuracy in the selective application of transrectal or transperineal biopsies according to the lesion site.

Methods: We developed a personalized prostate biopsy pattern and the biopsy approach was selected individually according to the lesion site. We compared it with the traditional transrectal prostate biopsy method to evaluate the efficiency. Patients (n = 351) who underwent prostate biopsy at Qilu Hospital of Shandong University from January 2018 to October 2020 were divided into two groups, including the traditional transrectal prostate biopsy group (n = 236) and the personalized group (n = 115). The data from patients, including clinical characteristics, biopsy results, and complications, were analyzed.

Results: The clinical characteristics of the two groups were similar. The total detection rate of prostate cancer in the personalized group was 49.6%, which was significantly higher than 38.1% in the traditional group (p = 0.023). When prostate-specific antigen was &lt;20 ng/ml, the detection rates of the two groups were 30.4 and 19.3%, respectively (p = 0.039). The PI-RADS was positively associated with high-grade prostate cancer in the personalized group. Patients with complications in the traditional transrectal systematic method group accounted for 6.8%, and those in the personalized group complications through the transrectal and transperineal approaches accounted for 7.1 and 4.1%, respectively. The most common complications in the transrectal group were fever and rectal bleeding, and those in the transperineal group were hematuria and urinary retention.

Conclusion: Compared with traditional transrectal prostate biopsy, the personalized biopsy pattern improved the detection rate of prostate cancer. The complications of the transrectal approach were much higher than those in the transperineal approach.

Proclarix, A New Biomarker for the Diagnosis of Clinically Significant Prostate Cancer: A Systematic Review

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved the early detection of clinically significant prostate cancer (csPCa). However, an appropriate selection of men for mpMRI or prostate biopsy is still challenging, which is why new biomarkers or predictive models are recommended to determine those patients who will benefit from prostate biopsy. Proclarix is a new test that provides the risk of csPCa based on thrombospondin-1 (THBS1), cathepsin D (CTSD), prostate-specific antigen (PSA), and percentage of free PSA (%fPSA), as well as age. This systematic review analyzes the current clinical status of Proclarix and future development.

EVIDENCE ACQUISITION

A systematic review of the literature was carried out by two independent reviewers. The Medical Subject Heading (MeSH) terms 'prostate', 'thrombospondin-1', 'cathepsin-D' and 'Proclarix' were used. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison and Outcomes (PICO) selection criteria were followed. Finally, four articles analyzed the clinical usefulness of Proclarix.

EVIDENCE SYNTHESIS

Proclarix has been developed in men with PSA levels between 2 and 10 ng/mL, normal digital rectal examination (DRE), and prostate volume (PV)​ ≥ 35 cm³. Proclarix is associated with the PCa grade group and is more effective than %fPSA in detecting csPCa. Two studies analyzed the efficacy of Proclarix in men undergoing guided and systematic biopsies, obtaining similar results to PSA density.

CONCLUSION

Initial studies have shown the potential benefit of Proclarix in patients with specific characteristics. Future studies are needed to verify the clinical usefulness of Proclarix in men with suspected PCa before and after mpMRI.

Accuracy of SelectMDx compared to mpMRI in the diagnosis of prostate cancer: a systematic review and diagnostic meta-analysis

BACKGROUND

The SelectMDx test is a promising biomarker that is developed based on detecting urinary messenger RNA in combination with clinical prostate cancer (PCa) risk factors. We aimed to compare SelectMDx and mpMRI as a diagnostic test in detecting PCa and high grade(HG)-PCa in men suspected to have PCa.

METHODS

According to PRISMA, a systematic search was performed using major web databases for studies published before September 30, 2021. Studies that compared sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SelectMDx and/or mpMRI were included. The bivariate random model that plotted sensitivity, specificity, PPV, NPV, and likelihood ratio (LR) for PCa and HG-PCa detection was applied to compare SelectMDx, mpMRI, and combination strategies (both positive and one or both positive).

RESULTS

Seven studies comprising 1328 patients who had undergone SelectMDx and mpMRI to detect PCa were included. Regarding PCa detection, SelectMDx had a pooled sensitivity of 81%, specificity of 69.8%, PPV of 64.7%, NPV of 85%, and LRs of +2.68 to -0.27, while mpMRI had a pooled sensitivity of 80.8%, specificity of 73.4%, PPV of 72.4%, NPV of 83.5%, and LRs of +3.03 to -0.26. The one or both positive strategy had the highest sensitivity (96.3%), NPV (95.7%), and the lowest -LR (0.06). While the both positive strategy had the highest specificity (80.9%), the PPV (76.5%) and +LR (3.68). In the scenario of PI-RADS 3 lesions not being biopsied in case of a negative SelectMDx (n = 44), unnecessary biopsies would be reduced by 42% (44/105) while the risk of missing HG-PCa would be 9% (4/44).

CONCLUSION

The performance of SelectMDx is comparable to that of mpMRI with regards to PCa and HG-PCa detection. In addition, this biomarker could help refine the clinical decision-making regarding the necessity of a biopsy in patients suspected to has been PCa.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Improving the Early Detection of Clinically Significant Prostate Cancer in Men in the Challenging Prostate Imaging-Reporting and Data System 3 Category

Background

Prostate Imaging-Reporting and Data System (PI-RADS) category 3 is a challenging scenario for detection of clinically significant prostate cancer (csPCa) and some tools can improve the selection of appropriate candidates for prostate biopsy.

Objective

To assess the performance of the European Randomized Study of Screening for Prostate Cancer (ERSPC) magnetic resonance imaging (MRI) model, the new Proclarix test, and prostate-specific antigen density (PSAD) in selecting candidates for prostate biopsy among men in the PI-RADS 3 category.

Design, setting, and participants

We conducted a head-to-head prospective analysis of 567 men suspected of having PCa for whom guided and systematic biopsies were scheduled between January 2018 and March 2020 in a single academic institution. A PI-RADS v.2 category 3 lesion was identified in 169 men (29.8%).

Outcome measurement and statistical analysis

csPCa, insignificant PCa (iPCa), and unnecessary biopsy rates were analysed. csPCa was defined as grade group ≥2. Receiver operating characteristic (ROC) curves, decision curve analysis curves, and clinical utility curves were plotted.

Results and limitations

PCa was detected in 53/169 men (31.4%) with a PI-RADS 3 lesion, identified as csPCa in 25 (14.8%) and iPCa in 28 (16.6%). The area under the ROC curve for csPCa detection was 0.703 (95% confidence interval [CI] 0.621–0.768) for Proclarix, 0.657 (95% CI 0.547–0.766) for the ERSPC MRI model, and 0.612 (95% CI 0.497–0.727) for PSAD (p = 0.027). The threshold with the highest sensitivity was 10% for Proclarix, 1.5% for the ERSPC MRI model, and 0.07 ng/ml/cm³ for PSAD, which yielded sensitivity of 100%, 91%, and 84%, respectively. Some 21.3%, 26.2%, and 7.1% of biopsies would be avoided with Proclarix, PSAD, and the ERSPC MRI model, respectively. Proclarix showed a net benefit over PSAD and the ERSPC MRI model. Both Proclarix and PSAD reduced iPCa overdetection from 16.6% to 11.3%, while the ERSPC MRI model reduced iPCa overdetection to 15.4%.

Conclusions

Proclarix was more accurate in selecting appropriate candidates for prostate biopsy among men in the PI-RADS 3 category when compared to PSAD and the ERSPC MRI model. Proclarix detected 100% of csPCa cases and would reduce prostate biopsies by 21.3% and iPCa overdetection by 5.3%.

Patient summary

We compared three methods and found that the Proclarix test can optimise the detection of clinically significant prostate cancer in men with a score of 3 on the Prostate Imaging-Reporting and Data System for magnetic resonance imaging scans.

Prostate cancer biomarkers: a practical review based on different clinical scenarios

Traditionally, diagnosis and staging of prostate cancer (PCa) have been based on prostate-specific antigen (PSA) level, digital rectal examination (DRE), and transrectal ultrasound (TRUS) guided prostate biopsy. Biomarkers have been introduced into clinical practice to reduce the overdiagnosis and overtreatment of low-grade PCa and increase the success of personalized therapies for high-grade and high-stage PCa. The purpose of this review was to describe available PCa biomarkers and examine their use in clinical practice. A nonsystematic literature review was performed using PubMed and Scopus to retrieve papers related to PCa biomarkers. In addition, we manually searched websites of major urological associations for PCa guidelines to evaluate available evidence and recommendations on the role of biomarkers and their potential contribution to PCa decision-making. In addition to PSA and its derivates, thirteen blood, urine, and tissue biomarkers are mentioned in various PCa guidelines. Retrospective studies have shown their utility in three main clinical scenarios: (1) deciding whether to perform a biopsy, (2) distinguishing patients who require active treatment from those who can benefit from active surveillance, and (3) defining a subset of high-risk PCa patients who can benefit from additional therapies after RP. Several validated PCa biomarkers have become commercially available in recent years. Guidelines now recommend offering these tests in situations in which the assay result, when considered in combination with routine clinical factors, is likely to affect management. However, the lack of direct comparisons and the unproven benefits, in terms of long-term survival and cost-effectiveness, prevent these biomarkers from being integrated into routine clinical use.

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.

Influence of operative time and blood loss on surgical margins and functional outcomes for laparoscopic versus robotic-assisted radical prostatectomy: a prospective analysis

Introduction

The aim of this article was to analyze whether operative time and blood loss during radical prostatectomy (RP) can significantly influence surgical margins (SM) status and post-operative functional outcomes.

Material and methods

We prospectively analyzed prostate cancer (PC) patients undergoing RP, using robot-assisted (RARP) or laparoscopic (LRP) procedures. Blood loss was defined using the variation in hemoglobin (Hb, g/dl) values from the day before surgery and no later than 4 hours after surgery.

Results

From a whole population of 413 cases considered for RP, 67% underwent LRP and 33.0% RARP. Positive SM (SM+) were found in 33.9% of cases. Mean surgical operative time was 172.3 ±76 min (range 49–485), whereas blood loss was 2.3 ±1.2 g/dl (range 0.3–7.6). Operative time and blood loss at RP were not significantly correlated (r = -0.028275; p = 0.684). SM+ rates significantly (p = 0.002) varied by operative time; a higher SM+ rate was found in cases with an operative time <120 min (41.2%) and >240 min (53.4%). The risk of SM+ significantly increased 1.70 and 1.94 times in cases with an operative time <120 min and >240 min, respectively, independently to the surgical approach. The rate of erectile disfunction (ED) varied from 22.4% to 60.3% between <120 min and >240 min procedures (p = 0.001). According to blood loss, SM+ rates slightly but significantly (p = 0.032) varied; a higher rate of SM+ was found in cases with a Hb variation between 2–4 g/dl (35.9%).

Conclusions

Independently to the surgical approach, operative time, more than blood loss at RP, represents a significant variable able to influence SM status and post-operative ED.

Radiomics in prostate cancer: an up-to-date review

Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications.

Preoperative red cell distribution width is associated with postoperative lymphovascular invasion in prostate cancer patients treated with radical prostatectomy: A retrospective study

PURPOSE

To investigate the relationship between baseline clinicopathological and laboratory variables especially hematological parameters and lymphovascular invasion (LVI) in patients who underwent radical prostatectomy (RP).

METHODS

We retrospectively evaluated 348 prostate cancer (PCa) patients who underwent RP in our center between May 2018 and June 2021. We divided them into non-LVI and LVI groups based on LVI status, and compared clinicopathological characteristics between non-LVI and LVI groups. Clinicopathological parameters including age, body mass index (BMI), history of hypertension and diabetes mellitus, neoadjuvant hormonal therapy (NHT), pathological stage T (pT) and lymph node status (pN), ISUP (international society of urological pathology) grade, positive surgical margin (PSM) rate, and hematological parameters containing prostate-specific antigen (PSA), whole blood parameters and inflammatory indexes were collected. The association between the clinicopathological parameters and the presence of LVI was identified by multivariate logistic regression analysis.

RESULTS

The pathological results of the RP specimen consisted of 53 (15.2%) patients with LVI and 295 (84.8%) cases without LVI. The level of PSA, percentages of advanced pT and grade, pN1, and PSM were significantly higher in the LVI group when compared with the non-LVI counterpart (p<0.001, p<0.001, p<0.001, p<0.001, p=0.007, respectively). Among the whole blood parameters, only red cell distribution width (RDW) was significantly different (41.2 ± 2.5 vs. 42.1 ± 3.1, p=0.035). Multivariate regression analysis demonstrated that RDW and NHT were negatively correlated with the presence of LVI (OR = 0.870, p=0.024; OR = 0.410, p=0.025), while PSA, ISUP, and pT were positively correlated with the presence of LVI (OR=1.013, p=0.005; OR =1.589, p=0.001; OR=1.655, p=0.008) after adjusting for confounding factors.

CONCLUSIONS

RDW rather than other whole blood parameters was independently and negatively associated with the presence of LVI in PCa patients, suggesting that RDW might play an essential role in PCa invasion.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

The Efficacy of Proclarix to Select Appropriate Candidates for Magnetic Resonance Imaging and Derived Prostate Biopsies in Men with Suspected Prostate Cancer

PURPOSE

To analyze how Proclarix is valuable to appropriately select candidates for multiparametric magnetic resonance imaging (mpMRI) and derived biopsies, among men with suspected prostate cancer (PCa). Proclarix is a new marker computing the clinically significant PCa (csPCa) risk, based on serum thosmbospondin-1, cathepsin D, prostate-specific antigen (PSA) and percent free PSA, in addition to age, that has been developed in men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and normal digital rectal examination (DRE).

MATERIALS AND METHODS

Proclarix score (0%-100%) is analyzed in a prospective frozen serum collection of 517 correlative men scheduled for guided and/or systematic biopsies after mpMRI. Outcome variables were csPCa detection (grade group ≥2), insignificant PCa (iPCa) overdetection and avoided mpMRIs.

RESULTS

The area under the curve of Proclarix was 0.701 (95% CI 0.637-0.765) among 281 men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and -normal DRE, and 0.754 (95% CI 0.701-0.807) in the others, p=0.038. Net benefit of Proclarix existed in all men. After selecting 10% threshold, Proclarix was integrated in an algorithm which also used the serum PSA level and DRE. A reduction of 25.4% of mpMRIs request was observed and 17.7% of prostate biopsies. Overdetection of iPCa was reduced in 18.2% and 2.6% of csPCa were misdiagnosed.

CONCLUSIONS

Proclarix is valuable in all men with suspected PCa. An algorithm integrating Proclarix score, serum PSA, and DRE can avoid mpMRI requests, unnecessary prostate biopsies and iPCa overdetection, with minimal loss of csPCa detection.

Evaluation of the Predictive Role of Blood-Based Biomarkers in the Context of Suspicious Prostate MRI in Patients Undergoing Prostate Biopsy

The aim of this study was to assess the predictive value of pre-biopsy blood-based markers in patients undergoing a fusion biopsy for suspicious prostate magnetic resonance imaging (MRI). We identified 365 consecutive patients who underwent MRI-targeted and systematic prostate biopsy for an MRI scored Prostate Imaging–Reporting and Data System Version (PI-RADS) ≥ 3. We evaluated the neutrophil/lymphocyte ratio (NLR), derived neutrophil/lymphocyte ratio (dNLR), platelet/lymphocyte ratio (PLR), systemic immune inflammation index (SII), lymphocyte/monocyte ratio (LMR,) de Ritis ratio, modified Glasgow Prognostic Score (mGPS), and prognostic nutrition index (PNI). Uni- and multivariable logistic models were used to analyze the association of the biomarkers with biopsy findings. The clinical benefits of biomarkers implemented in clinical decision-making were assessed using decision curve analysis (DCA). In total, 69% and 58% of patients were diagnosed with any prostate cancer and Gleason Grade (GG) ≥ 2, respectively. On multivariable analysis, only high dNLR (odds ratio (OR) 2.61, 95% confidence interval (CI) 1.23–5.56, p = 0.02) and low PNI (OR 0.48, 95% CI 0.26–0.88, p = 0.02) remained independent predictors for GG ≥ 2. The logistic regression models with biomarkers reached AUCs of 0.824–0.849 for GG ≥ 2. The addition of dNLR and PNI did not enhance the net benefit of a standard clinical model. Finally, we created the nomogram that may help guide biopsy avoidance in patients with suspicious MRI. In patients with PI-RADS ≥ 3 lesions undergoing MRI-targeted and systematic biopsy, a high dNLR and low PNI were associated with unfavorable biopsy outcomes. Pre-biopsy blood-based biomarkers did not, however, significantly improve the discriminatory power and failed to add a clinical benefit beyond standard clinical factors.

Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.

The Circular RNA circXPO1 Promotes Tumor Growth via Sponging MicroRNA-23a in Prostate Carcinoma

It has been shown that circular RNA XPO1 (circXPO1) is involved in cancer (e.g., lung adenocarcinoma and osteosarcoma) progression by sponging microRNAs. Nevertheless, the role of circXPO1 and its interaction with microRNAs in prostate cancer remains unknown. In this study, the results of quantitative real-time PCR showed that circXPO1 levels were dramatically increased in human prostate cancer tissue and cell lines compared with those in normal tissue and cell line. Furthermore, cell proliferation, colony formation, and cell invasion assays showed that circXPO1 promoted the malignant behavior of pancreatic cells in vitro. Mechanistically, bioinformatics prediction, a dual-luciferase reporter assay, and pull-down assay suggested that circXPO1 physically targets miR-23a and negatively regulates its expression in pancreatic cancer cells. miR-23a mimics and inhibitors effectively reversed the effects of circXPO1 on the malignant behavior of prostate cancer cells in vitro. Consistent results were observed in the xenograft tumor model. In conclusion, circXPO1 promotes prostate cancer progression via targeting miR-23a, thus suggesting the circXPO1/miR-23a axis can be used as a potential therapeutic target for prostate cancer treatment.