Accurate Diagnosis Of Prostate Cancer Research Articles

Biplanar or Monoplanar Prostate Biopsy: Should Transrectal and Transperineal Ap-proaches be Combined for Prostate Cancer Detection?

The accurate diagnosis of prostate cancer (PCa) remains challenging, particularly because standard biopsy techniques do not routinely include anterior zone, leading to potential missed diagnoses in this region. This study evaluates the accuracy and safety of biplanar stereotactic biopsy for diagnosing anterior clinically significant PCa (csPCa). After propensity score matching analysis, data from 256 patients were retrospectively analyzed, including 128 in the biplanar group (transrectal targeted biopsy with transperineal systematic biopsy) and 128 in the monoplanar group (transperineal targeted biopsy with transperineal systematic biopsy). PCa detection rates, lesion locations, csPCa, clinically insignificant PCa (ciPCa), and complication incidences were compared. Univariable and multivariable logistic regression models evaluated factors influencing biopsy outcomes. No significant differences were observed in overall PCa detection, ciPCa, posterior lesions, or postoperative complications between biplanar and monoplanar groups. The biplanar group demonstrated a higher detection rate for anterior csPCa (P=0.025). The overall International Society of Urological Pathology grade group (ISUP GG) distributions for Prostate Imaging Reporting and Data System (PI-RADS) scores 3 to 5 were not significantly different. Logistic regression identified age and PSA levels as independent predictors of higher detection rates, while univariable analysis showed that prostate volume had a significantly smaller effect on PCa detection rates in the biplanar group compared to the monoplanar group. Postoperative complications showed no statistically significant differences. In conclusion, biplanar stereotactic biopsy was superior to monoplanar biopsy in detecting anterior csPCa. Both methods demonstrated no significant differences in overall PCa detection rates and safety.

Exploring the role of multimodal [18F]F-PSMA-1007 PET/CT and multiparametric MRI data in predicting ISUP grading of primary prostate cancer.

The study explores the role of multimodal imaging techniques, such as [18F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research. This study conducted a retrospective analysis of 341 prostate cancer patients enrolled between 2019 and 2023, with data collected from five imaging modalities: [18F]F-PSMA-1007 PET, CT, Diffusion Weighted Imaging (DWI), T2 Weighted Imaging (T2WI), and Apparent Diffusion Coefficient (ADC). The study compared the performance of five single-modality data sets, PET/CT dual-modality fusion data, mpMRI tri-modality fusion data, and five-modality fusion data within deep learning networks, analyzing how different modalities impact the accuracy of ISUP grading prediction. To address the issue of limited data, a few-shot deep learning network was employed, enabling training and cross-validation with only a small set of labeled samples. Additionally, the results were compared with those from preoperative biopsies and clinical prediction models to further assess the reliability of the experimental findings. The experimental results demonstrate that the multimodal model (combining [18F]F-PSMA-1007 PET/CT and multiparametric MRI) significantly outperforms other models in predicting ISUP grading of prostate cancer. Meanwhile, both the PET/CT dual-modality and mpMRI tri-modality models outperform the single-modality model, with comparable performance between the two multimodal models. Furthermore, the experimental data confirm that the few-shot learning network introduced in this study provides reliable predictions, even with limited data. This study highlights the potential of applying multimodal imaging techniques (such as PET/CT and mpMRI) in predicting ISUP grading of prostate cancer. The findings suggest that this integrated approach can enhance the accuracy of prostate cancer diagnosis and contribute to more personalized treatment planning. Furthermore, incorporating few-shot learning into the model development process allows for more robust predictions despite limited data, making this approach highly valuable in clinical settings with sparse data.

Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men.

Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process. In this study, we aimed to externally validate a previously developed urine and plasma biomarker test in a biopsy-naïve population. Urine and blood samples were prospectively collected from 362 biopsy-naïve men with suspected PC before they underwent transrectal prostate biopsies. The expression levels of a 10-gene mRNA panel were quantified using reverse transcription/quantitative polymerase chain reaction of both urine and plasma. These gene expression levels, combined with clinical features and plasma prostate-specific antigen (PSA) levels, were used to predict the presence of International Society of Urological Pathology grade group ≥ 2 PC. Complete data were available for 314 patients. The sensitivity and specificity of the biomarker test were 87% (95% CI: 79-93%) and 42% (95% CI: 36-49%), respectively. The area under the curve was 0.76 (95% CI: 0.7-0.82) for the biomarker test probability and 0.65 (95% CI: 0.59-0.72) for PSA (p = 0.02). The test's negative predictive value was 89% (CI: 81-94%). This study did not replicate the previously reported high accuracy of the biomarker test, highlighting the need for further refinement and robust external validation to ensure reliable performance across diverse patient populations.

Cancer detection in patients with prostate-specific antigen levels within the grey zone: can synthetic magnetic resonance imaging aid in the differentiation between prostate cancer and noncancerous lesions?

The detection of prostate cancer (PCa) via conventional magnetic resonance imaging (MRI) in patients with prostate-specific antigen (PSA) levels within the grey zone remains challenging. Whether synthetic MRI can provide supplementary benefits for the accurate diagnosis of PCa in this specific population is still unknown. This study aims to investigate the diagnostic performance of synthetic MRI for differentiating PCa lesions from noncancerous lesions in patients with PSA levels within the grey zone (4-10 ng/mL). Clinical and MRI data, including synthetic MRI data of patients suspected of having PCa between August 2020 and August 2022, were retrospectively collected from The First Affiliated Hospital of Sun Yat-sen University and Sun Yat-sen University Cancer Center. Patients with PSA levels ranging from 4-10 ng/mL were enrolled. Pathology was obtained either from transrectal ultrasound-guided biopsy or radical prostatectomy. Regions of interest were manually drawn by two independent radiologists, and the values of quantitative parameters, including longitudinal relaxation time (T1), transverse relaxation time (T2), proton density (PD), and apparent diffusion coefficient (ADC), were separately measured. Interobserver agreement was evaluated using the interclass correlation coefficient (ICC). The differences in quantitative parameter values between PCa and noncancerous lesions were assessed using an independent sample t-test or the Mann-Whitney U test. Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of each parameter (T1, T2, PD, and ADC values), as well as their combination. P<0.05 indicated statistical significance. A total of 130 patients were enrolled in this study, with a mean age of 67.32±8.87 years. The interobserver agreement of all the T1, T2, PD, and ADC values was classified as good or above (ICC =0.60-1.00). The means of the T1, T2, PD, and ADC values were significantly different between PCa and noncancerous lesions (P=0.022, P<0.001, P=0.035, P<0.001, respectively). Notably, the ADC value demonstrated superior diagnostic performance compared to that of the other parameters, with an area under the curve (AUC) of 0.854 [95% confidence interval (CI): 0.781-0.909]. The combination of T1, T2, PD, and ADC values had a greater diagnostic performance (AUC =0.853, 95% CI: 0.781-0.909) than the T1 (AUC =0.622), T2 (AUC =0.721), or PD (AUC =0.608) values for differentiating PCa lesions from non-cancerous lesions. However, compared to the difference in the ADC value, no significant difference was found (P=0.982). Quantitative parameters, including T1, T2, and PD, derived from synthetic MRI can be applied to differentiate PCa lesions from noncancerous lesions in patients with PSA levels within the grey zone. However, when these parameters were combined with the ADC, the diagnostic performance did not improve compared to that with the ADC value alone.

Advanced Imaging for Localized Prostate Cancer

Background/Objectives: Prostate cancer is a prevalent malignancy often presenting without early symptoms. Advanced imaging technologies have revolutionized its diagnosis and management. This review discusses the principles, benefits, and clinical applications of multiparametric magnetic resonance imaging (mpMRI), micro-ultrasound (microUS), and prostate-specific membrane antigen positron emission tomography–computed tomography (PSMA PET/CT) in localized prostate cancer. Methods: We conducted a comprehensive literature review of recent studies and guidelines on mpMRI, microUS, and PSMA PET/CT in prostate cancer diagnosis, focusing on their applications in biopsy-naïve patients, those with previous negative biopsies, and patients under active surveillance. Results: MpMRI has demonstrated high sensitivity and negative predictive value in detecting clinically significant prostate cancer (csPCa). MicroUS, a newer technology, has shown promising results in early studies, with sensitivity and specificity comparable to mpMRI. PSMA PET/CT has emerged as a highly sensitive and specific imaging modality, particularly valuable for staging and detecting metastatic disease. All three technologies have been incorporated into urologic practice for prostate cancer diagnosis and management, with each offering unique advantages in different clinical scenarios. Conclusions: Advanced imaging techniques, including mpMRI, microUS, and PSMA PET/CT, have significantly improved the accuracy of prostate cancer diagnosis, staging, and management. These technologies enable more precise targeting of suspicious lesions during biopsy and therapy planning. However, further research, especially randomized controlled trials, is needed to fully establish the optimal use and inclusion of these imaging modalities in various stages of prostate cancer care.

Serum levels of prostate specific antigen, free PSA, [-2]proPSA, fPSA/tPSA ratio, Prostate Health Index, and glycosylation patterns of free PSA in patients with benign prostatic hyperplasia pharmacotherapy.

The medication used to treat benign prostate hyperplasia (BPH), a common condition in men over 50 years of age, can alter the levels of biomarkers used in prostate cancer detection. Commonly used medications for BPH include alpha-blockers, 5-alpha reductase inhibitors (5-ARIs), and muscarinic antagonists. We studied the impact of these drugs on total prostate-specific antigen (tPSA), free PSA (fPSA), [-2]proPSA, fPSA/tPSA ratio, and the Prostate Health Index (PHI), as well as novel potential biomarkers in the form of glycan composition of fPSA. Serum samples were collected from 564 males with BPH, with a mean age of 68.5 years. The samples were used to measure levels of tPSA, fPSA, and [-2]proPSA. The fPSA/tPSA and PHI were then calculated. The glycan composition of fPSA was analyzed using lectin-based glycoprofiling. Pharmacotherapy data was collected from the patients' medical records. Alpha-blocker monotherapy was associated with higher fPSA and fPSA/tPSA ratio, and decreased PHI. Levels of tPSA were not impacted. Alpha-blocker and 5-ARI dual therapy was associated with reduced levels of fPSA, [-2]proPSA, and PHI. Therapy combining alpha-blockers and antimuscarinic agents did not significantly influence biomarker levels apart from an increase in a Maackia amurensis lectin-recognized glycan originating in fPSA. BPH pharmacotherapy notably affects prostate cancer biomarkers. Recognizing the impact of pharmacotherapy is crucial for achieving an accurate diagnosis of prostate cancer and for planning treatment.

Diagnostic efficacy of [99mTc]Tc-PSMA SPECT/CT for prostate cancer: a meta-analysis

BackgroundPrompt and accurate diagnosis of prostate cancer (PCa) is of paramount importance for effective treatment planning. While Gallium-68 labeled prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) has proven efficacy in detecting PCa, limited availability poses challenges. As a potential alternative, [99mTc]Tc-PSMA single photon emission computed tomography (SPECT)/computed tomography (CT) holds promise. This systematic review and meta-analysis aimed to evaluate the diagnostic value of [99mTc]Tc-PSMA SPECT/CT for prostate cancer.MethodsA comprehensive search of PubMed, Cochrane, EMBASE, Scopus, Ovid, and Web of Science databases was conducted until July 2024. Sensitivity and specificity data were extracted to assess the diagnostic accuracy of [99mTc]Tc-PSMA SPECT/CT, while the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate study quality. Statistical analyses were performed using STATA 18, with MetaDisc 1.4 employed to detect threshold effects. Diagnostic accuracy indicators, including sensitivity, specificity, diagnostic odds ratio (DOR), negative likelihood ratio (LR-), and positive likelihood ratio (LR+), were pooled. The area under the curve (AUC) of the combined model was calculated using summary receiver-operating characteristic (SROC) curves.ResultsSeven studies meeting the inclusion criteria were identified from an initial pool of 1467 articles, with no publication bias observed. The pooled sensitivity, specificity, and AUC of [99mTc]Tc-PSMA SPECT/CT were found to be 0.89 (95% CI, 0.84–0.93), 0.92 (95% CI, 0.67–0.99), and 0.93 (95% CI, 0.90–0.95), respectively. Additionally, the comprehensive diagnostic odds ratio, diagnostic score, positive likelihood ratio, and negative likelihood ratio were calculated as 95.24 (95% CI, 17.30-524.41), 4.56 (95% CI, 2.85–6.26), 11.35 (95% CI, 2.31–55.71), and 0.12 (95% CI, 0.08–0.18), respectively.ConclusionsIn conclusion, our findings demonstrate that [99mTc]Tc-PSMA SPECT/CT exhibits favorable diagnostic performance for prostate cancer and can provide valuable supplementary information, particularly in regions and settings where [68Ga]Ga-PSMA PET/CT availability is limited, such as remote areas. These results highlight the potential of [99mTc]Tc-PSMA SPECT/CT as a valuable tool in the diagnosis and management of prostate cancer, warranting further investigation and validation in larger patient cohorts.

Promoter hypermethylation of Y-chromosome gene PRKY as a potential biomarker for the early diagnosis of prostate cancer.

Aim: To develop a methylation marker of Y-chromosome gene in the early diagnosis of prostate cancer (PCa).Materials & methods: We utilized bioinformatics analysis to identify the expression and promoter methylation of Y-chromosome gene PRKY in PCa and other common malignancies. Single-center experiments were conducted to validate the diagnostic value of PRKY promoter methylation in PCa.Results: PRKY expression was significantly down-regulated in PCa and its mechanism may be related to promoter methylation. PRKY promoter methylation is highly specific for the diagnosis of early PCa, which may be superior to prostate-specific antigen, mpMRI and other excellent molecular biomarkers.Conclusion: PRKY promoter methylation may be a potential marker for the early and accurate diagnosis of PCa.

From pixels to pathology: A novel dual-pathway multi-scale hierarchical upsampling network for MRI-based prostate zonal segmentation

Prostate cancer is a prevalent and life-threatening disease characterized by abnormal cell growth within the prostate gland. Early and accurate diagnosis of prostate cancer is crucial for effective treatment planning. Magnetic Resonance Imaging (MRI) is valuable for diagnosing and assessing prostate cancer. Medical professionals use MRI to create segmentation for detecting prostate cancer. However, existing segmentation methods are limited in accurately delineating anatomical structures and tumor regions within the prostate. This research proposes an innovative methodology for advancing MRI-based prostate segmentation. The objective is to encompass anatomical and tumor zones within the prostate, facilitating precise diagnosis and treatment planning. The proposed dual-pathway multi-scale hierarchical upsampling network introduces significant modifications compared to the traditional UNet-based architecture. It outperforms previous studies, demonstrating superior performance in anatomical segmentation on both the ProstateX and Prostate158 datasets. It achieves an intersection over union of 0.8449 and a dice similarity coefficient of 0.9872 on ProstateX, as well as an intersection over union of 0.8065 and a dice similarity coefficient of 0.9831 on Prostate158, suppressing previous research by a significant margin. These results highlight the potential of this approach to advance the utility of MRI in diagnosing and planning the treatment of prostate-related pathologies, benefiting both patients and healthcare practitioners.

Salivary MiR-193A-5P, MiR-375, MiR-141, MiR-25 and MiR-449B levels can be used as a new non-invasive diagnostic panel for early and accurate diagnosis of prostate cancer

Salivary MiR-193A-5P, MiR-375, MiR-141, MiR-25 and MiR-449B levels can be used as a new non-invasive diagnostic panel for early and accurate diagnosis of prostate cancer

Artificial Intelligence and Histopathological Diagnosis of Prostate Cancer

As of today, the diagnosis and management of prostate cancer involve the interpretation of data from multiple modalities and tools such as serum prostate-specific antigen levels, magnetic resonance imaging-guided biopsies, genomic biomarkers, and Gleason grading which are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time-consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. In this short review, we have made a case for the use of AI in the histopathological diagnosis of prostate cancer.

Smartphone-Based Free-to-Total Prostate Specific Antigen Ratio Detection System Using a Colorimetric Reaction Integrated with Proximity-Induced Bio-Barcode and CRISPR/Cas12a Assay.

The free-to-total prostate-specific antigen (f/t-PSA) ratio is of great significance in the accurate diagnosis of prostate cancer. Herein, a smartphone-based detection system is reported using a colorimetric reaction integrated with proximity-induced bio-barcode and the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a assay for f/t-PSA ratio detection. DNA/antibody recognition probes are designed to bind f-PSA or t-PSA and induce the release of the DNA bio-barcode. The CRISPR/Cas12a system is activated by the DNA bio-barcode to release Ag+ from the C-Ag+-C structure of the hairpin DNA. The released Ag+ is used to affect the tetramethylbenzidine (TMB)-H2O2-based colorimetric reaction catalyzed by Pt nanoparticles (NPs), as the peroxidase-like activity of the Pt NPs can be efficiently inhibited by Ag+. A smartphone with a self-developed app is used as an image reader and analyzer to analyze the colorimetric reaction and provide the results. A limit of detection of 0.06 and 0.04 ng mL-1 is achieved for t-PSA and f-PSA, respectively. The smartphone-based method showed a linear response between 0.1 and 100 ng mL-1 of t-PSA or f-PSA. In tests with clinical samples, the smartphone-based method successfully diagnosed prostate cancer patients from benign prostatic hyperplasia patients and healthy cases with high sensitivity and specificity.

Multi-Parametric Evaluation of Prostate Cancer on MRI

Background: Prostate cancer is the most common malignancy among men globally and the second leading cause of cancer-related deaths. The increasing prevalence, particularly in aging populations, underscores the urgent need for accurate diagnostic tools to facilitate early detection, precise staging, and effective management. Traditional diagnostic methods, including Digital Rectal Examination (DRE) and Prostate-Specific Antigen (PSA) testing, have limitations in sensitivity and specificity, leading to unnecessary procedures and missed diagnoses. Objective: This study aims to evaluate the efficacy of multiparametric Magnetic Resonance Imaging (mpMRI) in the diagnosis and staging of prostate cancer, comparing its accuracy with conventional diagnostic methods and examining its role in improving patient outcomes. Methods: A retrospective descriptive cross-sectional study was conducted at the Diagnostic Center of Combined Military Hospital, Lahore, from January 2021 to June 2023. Sixty male patients suspected of having prostate cancer, based on elevated PSA levels (&gt;10 ng/mL) or clinical symptoms, were included. mpMRI scans were performed using a Siemens Avanto MRI Machine 1.5T, incorporating T2-weighted imaging, Diffusion-Weighted Imaging (DWI), and Dynamic Contrast-Enhanced (DCE) MRI. The Prostate Imaging Reporting and Data System (PI-RADS) was employed for interpretation. Statistical analysis was conducted using SPSS 26.0, with chi-square tests applied to assess the association between mpMRI findings and prostate cancer diagnosis. Results: mpMRI identified prostate cancer with a sensitivity of 85% and specificity of 88.3%, as evidenced by abnormal MRI findings in 53 out of 60 patients. Elevated PSA levels were confirmed in 90% of the cases. T2-weighted imaging demonstrated hypointensity in the peripheral zone in 70% of patients, while DWI and ADC mapping showed restricted diffusion in 85% of the cases. DCE-MRI revealed post-contrast enhancement in 40% of the participants. The PI-RADS scoring system effectively graded the severity of prostate lesions, with 38.3% of cases categorized as PI-RADS IV. Conclusion: Multiparametric MRI significantly enhances the accuracy of prostate cancer diagnosis and staging, surpassing traditional diagnostic methods in sensitivity and specificity. By providing detailed anatomical and functional information, mpMRI facilitates early detection, accurate localization, and appropriate management of prostate cancer, potentially improving patient outcomes.

Intracellular MicroRNA Imaging and Specific Discrimination of Prostate Cancer Circulating Tumor Cells Using Multifunctional Gold Nanoprobe-Based Thermophoretic Assay.

Circulating tumor cells (CTCs) have emerged as powerful biomarkers for diagnosis of prostate cancer. However, the effective identification and concurrently accurate imaging of CTCs for early screening of prostate cancer have been rarely explored. Herein, we reported a multifunctional gold nanoprobe-based thermophoretic assay for simultaneous specific distinguishing of prostate cancer CTCs and sensitive imaging of intracellular microRNA (miR-21), achieving the rapid and precise detection of prostate cancer. The multifunctional gold nanoprobe (GNP-DNA/Ab) was modified by two types of prostate-specific antibodies, anti-PSMA and anti-EpCAM, which could effectively recognize the targeting CTCs, and meanwhile linked double-stranded DNA for further visually imaging intracellular miR-21. Upon the specific internalization of GNP-DNA/Ab by PC-3 cells, target aberrant miR-21 could displace the signal strand to recover the fluorescence signal for sensitive detection at the single-cell level, achieving single PC-3 cell imaging benefiting from the thermophoresis-mediated signal amplification procedure. Taking advantage of the sensitive miR-21 imaging performance, GNP-DNA/Ab could be employed to discriminate the PC-3 and Jurkat cells because of the different expression levels of miR-21. Notably, PC-3 cells were efficiently recognized from white blood cells, exhibiting promising potential for the early diagnosis of prostate cancer. Furthermore, GNP-DNA/Ab possessed good biocompatibility and stability. Therefore, this work provides a great tool for aberrant miRNA-related detection and specific discrimination of CTCs, achieving the early and accurate diagnosis of prostate cancer.

MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images

Micro-ultrasound (micro-US) is a novel 29-MHz ultrasound technique that provides 3-4 times higher resolution than traditional ultrasound, potentially enabling low-cost, accurate diagnosis of prostate cancer. Accurate prostate segmentation is crucial for prostate volume measurement, cancer diagnosis, prostate biopsy, and treatment planning. However, prostate segmentation on micro-US is challenging due to artifacts and indistinct borders between the prostate, bladder, and urethra in the midline. This paper presents MicroSegNet, a multi-scale annotation-guided transformer UNet model designed specifically to tackle these challenges. During the training process, MicroSegNet focuses more on regions that are hard to segment (hard regions), characterized by discrepancies between expert and non-expert annotations. We achieve this by proposing an annotation-guided binary cross entropy (AG-BCE) loss that assigns a larger weight to prediction errors in hard regions and a lower weight to prediction errors in easy regions. The AG-BCE loss was seamlessly integrated into the training process through the utilization of multi-scale deep supervision, enabling MicroSegNet to capture global contextual dependencies and local information at various scales. We trained our model using micro-US images from 55 patients, followed by evaluation on 20 patients. Our MicroSegNet model achieved a Dice coefficient of 0.939 and a Hausdorff distance of 2.02 mm, outperforming several state-of-the-art segmentation methods, as well as three human annotators with different experience levels. Our code is publicly available at https://github.com/mirthAI/MicroSegNet and our dataset is publicly available at https://zenodo.org/records/10475293.

Comparing the Diagnostic Performance of Micro- Ultrasound-Guided Biopsy Versus Multiparametric Magnetic Resonance Imaging-Targeted Biopsy in the Detection of Clinically Significant Prostate Cancer: A Systematic Review and Meta-Analysis

Background Micro-ultrasound is a novel, high-resolution imaging modality that aims to improve the accuracy of prostate cancer diagnosis compared with TRUS-guided biopsy. While traditional ultrasound systems operate at 8 to 12 MHz, micro-ultrasound operates at 29 MHz, allowing enhanced recognition of microstructures with 300% higher resolution. Micro-ultrasound can potentially identify and target in real-time suspicious lesions, improving sensitivity and the negative predictive value for clinically significant prostate cancer. It may be a low-cost alternative to multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer. Methods A systematic review and meta-analysis was performed comparing the diagnostic performance of microultrasound- guided prostate biopsies with mpMRI-targeted prostate biopsies in the detection of clinically significant prostate cancer. PubMed, EMBASE, SCOPUS, and Cochrane CENTRAL databases were searched to identify relevant studies published up to July 2022. Results A total of 15 studies were included for the systematic review, with 12 of those studies being included for the meta-analysis. The pooled sensitivity and specificity for micro-ultrasound-guided biopsies detecting clinically significant prostate cancer were 89% (95% CI 83 to 93) and 31% (95% CI 23 to 40) respectively (I2 = 0%). In comparison, the pooled sensitivity and specificity for mpMRI-targeted biopsies detecting clinically significant prostate cancer were 86% (95% CI 73 to 93) and 32% (95% CI 18 to 50) respectively (I2 = 16%). There was no statistically significant difference in the sensitivity or specificity between micro-ultrasound and mpMRI. Subgroup analysis found no difference in MRI subgroups based on blinding (P = 0.383). Conclusion Micro-ultrasound-guided biopsies are comparable to mpMRI targeted biopsies with no difference in the detection of clinically significant prostate cancer between the 2 modalities. Large, multicentre, prospective studies are required to further substantiate the use of micro-ultrasound as an alternative to or in conjunction with mpMRI in the detection of prostate cancer.

Portable and Rapid Dual-Biomarker Detection Using Solution-Gated Graphene Field Transistors in the Accurate Diagnosis of Prostate Cancer.

Prostate-specific antigen (PSA) is the common serum-relevant biomarker for early prostate cancer (PCa) detection in clinical diagnosis. However, it is difficult to accurately diagnose PCa in the early stage due to the low specificity of PSA. Herein, a new solution-gated graphene field transistor (SGGT) biosensor with dual-gate for dual-biomarker detection is designed. The sensing mechanism is that the designed aptamers immobilized on the surface of the gate electrodes can capture PSA and sarcosine (SAR) biomolecules and induce the capacitance changes of the electric double layers of SGGT. The limit of detections of PSA and SAR biomarkers can reach 0.01 fg mL-1 , which is three-to-four orders of magnitude lower than previously reported assays. The detection time of PSA and SAR is ≈4.5 and ≈13 min, which is significantly faster than the detection time (1-2h) of conventional methods. The clinical serum samples testing demonstrates that the biosensor can distinguish the PCa patients from the control group and the diagnosis accuracy can reach 100%. The SGGT biosensor can be integrated into the portable platform and the diagnostic results can directly display on the smartphone/Pad. Therefore, the integrated portable platform of the biosensor can distinguish cancer types through the dual-biomarker detection.

NRD-Net: a noise-resistant distillation network for accurate diagnosis of prostate cancer with bi-parametric MRI images

NRD-Net: a noise-resistant distillation network for accurate diagnosis of prostate cancer with bi-parametric MRI images

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy.

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

Accurate diagnosis of prostate cancer by combining Proclarix with magnetic resonance imaging.

To assess of the clinical performance of Proclarix® (a novel Conformité Européenne [CE]-marked biomarker test aiding in the identification of clinically significant prostate cancer [csPCa]) alone or in combination with multiparametric magnetic resonance imaging (mpMRI) to predict csPCa (International Society of Urological Pathology Grade Group ≥2). The study included blood samples from 721 men undergoing mpMRI followed by biopsy at University College London, London, and Vall d'Hebron University Hospital, Barcelona. Samples were tested blindly. The Proclarix-MRI model combining prostate volume, Proclarix and mpMRI results was trained using the UCL cohort (n= 159) and validated in the Vall d'Hebron cohort (n= 562). Its diagnostic performance was established in correlation to biopsy outcome and compared to available clinical parameters and risk calculators. Clinical performance of the Proclarix-MRI model in the validation cohort did not significantly differ from the training cohort and resulted in a sensitivity for csPCa of 90%, 90% negative predictive value and 66% positive predictive value. The Proclarix-MRI score's specificity (68%) was significantly (P< 0.001) better than the MRI-European Randomized study of Screening for Prostate Cancer risk score (51%), Proclarix (27%) or mpMRI (28%) alone. In addition, Proclarix by itself was found to be useful in the MRI Prostate Imaging-Reporting and Data System (PI-RADS) score 3 subgroup by outperforming prostate-specific antigen density in terms of specificity (25% vs 13%, P= 0.004) at 100% sensitivity. When combined with mpMRI and prostate volume, Proclarix reliably predicted csPCa and ruled out men with no or indolent cancer. A large reduction of two thirds of unneeded biopsies was achieved. Proclarix can further be used with high confidence to reliably detect csPCa in men with an indeterminate PI-RADS score 3 mpMRI. Despite these encouraging results, further validation is needed.