Algorithms for classification of sequences and segmentation of prostate gland: an external validation study.

OBJECTIVES: The aim of the study was to externally validate two AI models for the classification of prostate mpMRI sequences and segmentation of the prostate gland on T2WI. MATERIALS AND METHODS: MpMRI data from 719 patients were retrospectively collected from two hospitals, utilizing nine MR scanners from four different vendors, over the period from February 2018 to May 2022. Med3D deep learning pretrained architecture was used to perform image classification,UNet-3D was used to segment the prostate gland. The images were classified into one of nine image types by the mode. The segmentation model was validated using T2WI images. The accuracy of the segmentation was evaluated by measuring the DSC, VS,AHD.Finally,efficacy of the models was compared for different MR field strengths and sequences. RESULTS: 20,551 image groups were obtained from 719 MR studies. The classification model accuracy is 99%, with a kappa of 0.932. The precision, recall, and F1 values for the nine image types had statistically significant differences, respectively (all P < 0.001). The accuracy for scanners 1.436 T, 1.5 T, and 3.0 T was 87%, 86%, and 98%, respectively (P < 0.001). For segmentation model, the median DSC was 0.942 to 0.955, the median VS was 0.974 to 0.982, and the median AHD was 5.55 to 6.49 mm,respectively.These values also had statistically significant differences for the three different magnetic field strengths (all P < 0.001). CONCLUSION: The AI models for mpMRI image classification and prostate segmentation demonstrated good performance during external validation, which could enhance efficiency in prostate volume measurement and cancer detection with mpMRI. CLINICAL RELEVANCE STATEMENT: These models can greatly improve the work efficiency in cancer detection, measurement of prostate volume and guided biopsies.

miR-21-5p prevents doxorubicin-induced cardiomyopathy by downregulating BTG2.

Cardiomyocyte apoptosis has been characterized as one of the major mechanisms underlying doxorubicin (DOX)-induced cardiomyopathy. MicroRNA-21-5p (miR-21-5p) was reported to mitigate ischemia-induced cardiomyocyte apoptosis and cardiac injury. However, to our knowledge, the functional role of miR-21-5p in DOX-induced cardiomyopathy is unclear. In this study, we explored the role of miR-21-5p in DOX-induced cardiac injury. The expression level of miR-21-5p was detected by quantitative real-time polymerase chain reaction (qRT-PCR). Dual luciferase reporter assay was used to verify the potential target gene of miR-21-5p. The apoptosis rate of NRCMs was detected by TUNEL staining assay. Western blot analysis was used to detect the protein expression levels of Bax, Bcl-2, Caspase3, cleaved-Caspase3 and BTG2. For animal studies, mice were injected with AAV9-miR-21-5p or AAV9-Empty viruses, and treated with DOX at a dose of 5 mg/kg per week through intraperitoneally administration. After 4 weeks of DOX treatment, mice were subjected to echocardiography to measure the left ventricular ejection fraction (EF) and fractional shortening (FS). Results showed that miR-21-5p was upregulated in both DOX-treated primary cardiomyocytes and mouse heart tissues. Interestingly, enhanced miR-21-5p expression inhibited DOX-induced cardiomyocyte apoptosis and oxidative stress, while decreased miR-21-5p expression promoted cardiomyocyte apoptosis and oxidative stress. Furthermore, cardiac overexpression of miR-21-5p protected against DOX-induced cardiac injury. The mechanistic study indicated that BTG2 was a target gene of miR-21-5p. The anti-apoptotic effect of miR-21-5p could be inhibited by BTG2 overexpression. Conversely, inhibition of BTG2 rescued the pro-apoptotic effect of miR-21-5p inhibitor. Taken together, our study showed that miR-21-5p could prevent DOX-induced cardiomyopathy by downregulating BTG2.

Influence of Sleeve Gastrectomy on Skeletal Muscular Fat Infiltration Measured by MRI in Patients with Metabolic Syndrome: Preliminary Results.

INTRODUCTION: Several researchers have focused on the role of skeletal muscle in metabolic problems in recent years. We aimed to evaluate influence of sleeve gastrectomy on skeletal muscular fat infiltration determined by magnetic resonance imaging in patients with metabolic syndrome (MetS). METHODS: Sixty five MetS patients (male/female, 20/45; mean age, 35.5 years ± 6.6 [standard deviation]; age range, 22-59 years) enrolled in our study. Prior to and 1 year after sleeve gastrectomy, patients underwent routine measurement of skeletal muscular fat concentration (denoted by proton density fat fraction, PDFF) and chemical indexes. The associations of skeletal muscular fat concentration with other variables were determined using multiple linear regression analysis. RESULTS: Difference between skeletal muscular PDFF at baseline (4.46 ± 2.01%) and PDFF 1-year after sleeve gastrectomy (3.00 ± 1.47%) was significant. Multivariable predictors of baseline skeletal muscular PDFF by descending order of standardized coefficient were fasting serum glucose (0.459; p = 0.001), age (0.395; p < 0.001), systolic pressure (0.319; p = 0.029), insulin (0.030; p = 0.026), white cell count (0.302; p = 0.007), diastolic pressure (-0.301; p = 0.046), and total alkaline phosphatase (-0.474; p < 0.001) all at baseline. Furthermore, multivariable predictors of change in PDFF were serum total cholesterol (3.510; p < 0.001), total alkaline phosphatase (0.535; p < 0.001), estrogen (0.457; p < 0.001), diastolic pressure (0.352; p < 0.001), systolic pressure (-0.409; p < 0.001), high-density lipoprotein cholesterol (-0.719; p < 0.001), insulin (-0.774; p < 0.001), C-reactive protein (-0.900; p < 0.001), triglyceride (-1.756; p < 0.001), and low-density lipoprotein cholesterol (-2.854; p < 0.001) all at baseline. CONCLUSION: Sleeve gastrectomy could alleviate myosteatosis in MetS patients during 1-year follow-up. The extent of remission on skeletal muscular fat infiltration after sleeve gastrectomy was influenced by baseline metabolic problems related to serum glucose, serum lipid, and blood pressure level.

Diagnostic power of vertebral hydroxyapatite concentration measurements in spectral CT for osteoporosis-associated fractures and impact of intravenous contrast administration.

OBJECTIVES: To evaluate the diagnostic power of using vertebral hydroxyapatite concentration measurements in unenhanced and contrast-enhanced spectral CT for detecting and predicting the risk of osteoporosis-associated fractures. METHODS: L1 of 210 patients (105 men, 105 women; mean age, 64 years, range, 19-103 years) who had undergone spectral CT examinations from January 1, 2018, to March 1, 2019, were retrospectively analyzed. Patient data for 3 years after spectral CT were retrieved from electronic medical record information systems to obtain the incidence of osteoporotic fractures. Baseline vertebral cancellous hydroxyapatite concentration from unenhanced and contrast-enhanced late-arterial-phase images was measured. The receiver operating characteristic curves were used to evaluate the diagnostic power for detecting and predicting the 3-year risk of osteoporosis-associated fractures using hydroxyapatite concentrations in both phases. RESULTS: The hydroxyapatite concentrations in both phases had good diagnostic power to detect fractures at baseline. The sensitivity and specificity for predicting one or more osteoporosis-associated fractures within 3 years after spectral CT were 76.80% and 93.10%, respectively, using the cutoff of 74.79 mg/cm3 in vertebral hydroxyapatite concentration in the unenhanced CT phase, and 82.87% and 82.76%, respectively, using the cutoff of 84.65 mg/cm3 in the late-arterial phase. Furthermore, there was no significant difference in the diagnosis between unenhanced and enhanced CT-derived hydroxyapatite concentrations (p = 0.360). CONCLUSIONS: Both unenhanced and enhanced spectral CT-derived hydroxyapatite concentrations can accurately detect and predict future risk of osteoporosis-associated fractures. The hydroxyapatite concentration assessed in the late-arterial phase may have a similar diagnostic efficacy to that in the unenhanced phase. KEY POINTS: • A cutoff of 74.79 mg/cm3 of vertebral hydroxyapatite concentration in the unenhanced CT scans had 76.80% sensitivity and 93.10% specificity to predict one or more osteoporosis-associated fractures within 3 years after spectral CT examinations. • A cutoff of 84.65 mg/cm3 of vertebral hydroxyapatite concentration in the late-arterial-enhanced CT scans had 82.87% sensitivity and 82.76% specificity to predict one or more osteoporosis-associated fractures within 3 years after spectral CT examinations. • The hydroxyapatite concentration assessed in the late-arterial phase may have a similar diagnostic efficacy to that in the unenhanced phase.

The diagnostic performance in clinically significant prostate cancer with PI-RADS version 2.1: simplified bpMRI versus standard mpMRI.

OBJECTIVES: To compare the diagnostic performance for the detection of clinically significant prostate cancer (csPCa) between bpMRI with only axial T2WI (simplified bpMRI) and standard-multiparametric MRI (mpMRI). METHODS: A total of 569 patients who underwent mpMRI followed by biopsy or prostatectomy were enrolled in this retrospective study. According to PI-RADS v2.1, three radiologists (A, B, C) from three centers blinded to clinical variables were assigned scores on lesions with simplified bpMRI and then with mpMRI 2 weeks later. Diagnostic performance of simplified bpMRI was compared with mpMRI using histopathology as reference standard. RESULTS: For all the three radiologists, the diagnostic sensitivity was significantly higher with mpMRI than with simplified bpMRI (P < 0.001 to P = 0.035); and although specificity was also higher with mpMRI than with simplified bpMRI for radiologist B and radiologist C, it was statistically significant only for radiologist B (P = 0.011, P = 0.359, respectively). On the contrary, for radiologist A, specificity was higher with simplified bpMRI than with mpMRI (P = 0.001). The area under the receiver operating characteristic curve (AUC) was significantly higher for mpMRI than for simplified bpMRI except for radiologist A (radiologist A: 0.903 vs 0.913, P = 0.1542; radiologist B: 0.861 vs 0.834 P = 0.0013; and radiologist C: 0.884 vs 0.848, P = 0.0003). Interobserver reliability of PI-RADS v2.1 showed good agreement for both simplified bpMRI (kappa = 0.665) and mpMRI (kappa = 0.739). CONCLUSION: Although the detection of csPCa with simplified bpMRI was comparatively lower than that with mpMRI, the diagnostic performance was still high in simplified bpMRI. Our data justify using mpMRI outperforms simplified bpMRI for prostate cancer screening and imply simplified bpMRI as a potential screening tool.

Random sketch learning for deep neural networks in edge computing.

Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50-90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications.

Circulating Non-coding RNAs and Cardiovascular Diseases.

The discovery of noncoding RNAs (ncRNAs) including short microRNAs, long ncRNAs and circular RNAs has broaden our knowledge about mammalian genomes and transcriptomes. A growing number of evidence on aberrantly regulated ncRNAs in cardiovascular diseases has indicated that ncRNAs are critical contributors to cardiovascular pathophysiology. Moreover, multiple recent studies have reported that ncRNAs can be detected in the bloodstream that differs between health subjects and diseased patients and some of them are remarkably stable. Although our knowledge about the origin and function of the circulating ncRNAs is still limited, these molecules have been regarded as promising noninvasive biomarker for risk stratification, diagnosis and prognosis of various cardiovascular diseases. In this chapter, we have described biological characteristics of circulating ncRNAs and discussed current trends and future prospects for the usage of circulating ncRNAs as biomarkers for common cardiovascular diseases.

Circulating microRNAs in Response to Exercise Training in Healthy Adults.

Circulating microRNAs (miRNAs, miRs) have great potential as cardiac biomarkers and they are also being explored for their roles in intercellular communication and gene expression regulation. The analysis of circulating miRNAs in response to exercise would provide a deeper understanding of the molecular response to physical activity and valuable information for clinical practice. Here, eight male college students were recruited to participate in cardiopulmonary exercise testing (CPET) and 1 h acute exercise training (AET). Blood samples were collected and serum miRNAs involved in angiogenesis, inflammation and enriched in muscle and/or cardiac tissues were analyzed before and after cardiopulmonary exercise and acute exercise. The miRNAs we detected were miR-1, miR-20a, miR-21, miR-126, miR-133a, miR-133b, miR-146, miR155, miR-208a, miR-208b, miR-210, miR-221, miR-222, miR-328, miR-378, miR-499, and miR-940. We found that serum miR-20a was decreased significantly after CPET and serum miR-21 was increased after AET. In addition, no robust correlation was identified between the changes of these miRNAs and makers of cardiac function and exercise capacity, which indicates a distinct adaptation of these miRNAs to exercise. Future studies are highly needed to define the potential use of these circulating miRNAs as useful biomarkers of exercise training, and disclose the biological function of circulating miRNAs as physiological mediators of exercise-induced cardiovascular adaptation.

Distributed Learning Based Joint Communication and Computation Strategy of IoT Devices in Smart Cities.

With the development of global urbanization, the Internet of Things (IoT) and smart cities are becoming hot research topics. As an emerging model, edge computing can play an important role in smart cities because of its low latency and good performance. IoT devices can reduce time consumption with the help of a mobile edge computing (MEC) server. However, if too many IoT devices simultaneously choose to offload the computation tasks to the MEC server via the limited wireless channel, it may lead to the channel congestion, thus increasing time overhead. Facing a large number of IoT devices in smart cities, the centralized resource allocation algorithm needs a lot of signaling exchange, resulting in low efficiency. To solve the problem, this paper studies the joint policy of communication and computing of IoT devices in edge computing through game theory, and proposes distributed Q-learning algorithms with two learning policies. Simulation results show that the algorithm can converge quickly with a balanced solution.

Computational Prediction and Analysis of Associations between Small Molecules and Binding-Associated S-Nitrosylation Sites.

Interactions between drugs and proteins occupy a central position during the process of drug discovery and development. Numerous methods have recently been developed for identifying drug⁻target interactions, but few have been devoted to finding interactions between post-translationally modified proteins and drugs. We presented a machine learning-based method for identifying associations between small molecules and binding-associated S-nitrosylated (SNO-) proteins. Namely, small molecules were encoded by molecular fingerprint, SNO-proteins were encoded by the information entropy-based method, and the random forest was used to train a classifier. Ten-fold and leave-one-out cross validations achieved, respectively, 0.7235 and 0.7490 of the area under a receiver operating characteristic curve. Computational analysis of similarity suggested that SNO-proteins associated with the same drug shared statistically significant similarity, and vice versa. This method and finding are useful to identify drug⁻SNO associations and further facilitate the discovery and development of SNO-associated drugs.

Simultaneous channel estimation and signal detection in wireless ultraviolet communications combating inter-symbol-interference.

One primary challenge in wireless ultraviolet communications (UVCs) is the inter-symbol-interference (ISI), which may block the detection of current informative signal, especially when channel-related characteristics are unknown. In this paper, we propose a UV channel-related Bayesian scheme that can simultaneously estimate the channel characteristics and detect informative signals, which therefore can address the ISI disturbance. By investigating the UV single-scattering photon model, the dynamic behaviors of the channel state information (CSI), which involve the uncertain signal and the unknown channel parameters are formulated. Hence, a sequential Bayesian process is suggested to estimate the UV CSI. Numerical analysis shows that the proposed scheme can obtain a promising estimation performance (i.e., the relative errors are less than 4%), and gain an extra 4dB detection performance compared with imperfect maximum-likelihood sequence detection (MLSD) scheme.

Bacterial Relay for Energy-Efficient Molecular Communications.

In multi-cellular organisms, molecular signaling spans multiple distance scales and is essential to tissue structure and functionality. Molecular communications is increasingly researched and developed as a key subsystem in the Internet-of-Nano-Things paradigm. While short range microscopic diffusion communications is well understood, longer range channels can be inefficient and unreliable. Static and mobile relays have been proposed in both conventional wireless systems and molecular communication contexts. In this paper, our main contribution is to analyze the information delivery energy efficiency of bacteria mobile relays. We discover that these mobile relays offer superior energy efficiency compared with pure diffusion information transfer over long diffusion distances. This paper has widespread implications ranging from understanding biological processes to designing new efficient synthetic biology communication systems.

Feasibility of low-dose coronary computed tomographic angiography used in atrial fibrillation patients.

OBJECTIVE: The diagnosis of coronary artery disease (CAD) in atrial fibrillation (AF) patients using coronary computed tomography angiography (CCTA) requires a large exposure dosage or repeated examinations. This study evaluates the feasibility of using low-dose CCTA in the double prospectively ECG-triggered high-pitch spiral acquisition mode (Double Flash Spiral mode). METHODS: Twenty-eight AF patients with suspected CAD were recruited. Double Flash Spiral mode (tube voltage 100 kVp) and iterative reconstruction was used for CCTA examination. Two radiologists cross-evaluated the CCTA image quality. The effective radiation dose was measured for each patient. RESULTS: Twenty-eight AF patients (10 female, 18 male, mean age 68.8 ± 13.9 y, body mass index 24.3 ± 2.3 kg/cm2) were recruited and 337 artery segments were evaluated. In total, 98.5% (332/337) of the coronary artery segments and 96.4% (27/28) of the AF patients were rated as diagnostically evaluable. Of these 27 diagnosable patients, 17 patients (63%) were diagnosed with multi-vessel stenosis. Besides, 5 of 28 patients (17.9%) have left atrial appendage thrombus. The quality of the integrated image was significantly better than either of the individual first or the second scans, based on segments (P < 0.001) and patients (P < 0.05). The mean effective radiation dose was 1.5 mSv ±0.4 mSv. CONCLUSIONS: Using the Double Flash Spiral mode at low radiation dose (mean 1.5 mSv), 98.5% of the coronary segments and 96.4% of the scans were of sufficient diagnostic quality.

Adaptive limited feedback for interference alignment in MIMO interference channels.

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

Could Magnetic Resonance Imaging Help to Identify the Presence of Prostate Cancer Before Initial Biopsy? The Development of Nomogram Predicting the Outcomes of Prostate Biopsy in the Chinese Population.

PURPOSE: This study was designed to investigate the effectiveness of magnetic resonance imaging (MRI) in diagnosing prostate cancer (PCa) and high-grade prostate cancer (HGPCa) before transrectal ultrasound (TRUS)-guided biopsy. METHODS: The clinical data of 894 patients who received TRUS-guided biopsy and prior MRI test from a large Chinese center was reviewed. Based on Prostate Imaging Reporting and Data System (PI-RADS) scoring, all MRIs were re-reviewed and assigned as Grade 0-2 (PI-RADS 1-2; PI-RADS 3; PI-RADS 4-5). We constructed two models both in predicting PCa and HGPCa (Gleason score ≥ 4 + 3): Model 1 with MRI and Model 2 without MRI. Other clinical factors include age, digital rectal examination, PSA, free-PSA, volume, and TRUS. RESULTS: PCa and HGPCa were present in 434 (48.5 %) and 218 (24.4 %) patients. An MRI Grade 0, 1, and 2 were assigned in 324 (36.2 %), 193 (21.6 %) and 377 (42.2 %) patients, which was associated with the presence of PCa (p < 0.001) and HGPCa (p < 0.001). Particularly in patients aged ≤55 years, the assignment of MRI Grade 0 was correlated with extremely low rate of PCa (1/27) and no HGPCa. The c-statistic of Model 1 and Model 2 for predicting PCa was 0.875 and 0.841 (Z = 4.2302, p < 0.001), whereas for predicting HGPCa was 0.872 and 0.850 (Z = 3.265, p = 0.001). Model 1 exhibited higher sensitivity and specificity at same cutoffs, and decision-curve analysis also suggested the favorable clinical utility of Model 1. CONCLUSIONS: Prostate MRI before biopsy could predict the presence of PCa and HGPCa, especially in younger patients. The incorporation of MRI in nomograms could increase predictive accuracy.

The efficiency of multiparametric magnetic resonance imaging (mpMRI) using PI-RADS Version 2 in the diagnosis of clinically significant prostate cancer.

OBJECTIVES: To investigate the efficiency of multiparametric MRI (mpMRI) based on Prostate Imaging Reporting and Data System (PI-RADS) Version 2 (v2) in detecting clinically significant prostate cancer (PCa) and to test the interobserver consistency. METHODS: Based on PI-RADS v2, two radiologists reviewed the images of 372 patients who underwent prostate biopsy and prebiopsy mpMRI. RESULTS: There's significant correlation between higher PI-RADS score and the presence of clinical significant PCa (P<.001). PI-RADS score 3 was the best cutoff point with sensitivity and specificity over 80%. The diagnostic concordance was moderate (kappa=0.478). CONCLUSIONS: PI-RADS v2 demonstrated good accuracy in detecting clinically significant PCa, however the interobserver consistency needs to be improved.

On the interference alignment with limited feedback for device-to-device networks.

In this paper, we analyze the performance of interference alignment of device-to-device (D2D) uplink underlay cellular networks. By fully considering the impact of imperfect precoding vectors caused by limited feedback, the exact closed-form expressions of average throughput for cellular network and D2D network are derived in terms of transmit power and the number of feedback bits. The accuracy of the average throughput is verified by simulation results. Our analytic results provide great promises to practical system designs.

Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications.

Nanoscale molecular communication is a viable way of exchanging information between nanomachines. In this investigation, a low-complexity and noncoherent signal detection technique is proposed to mitigate the inter-symbol-interference (ISI) and additive noise. In contrast to existing coherent detection methods of high complexity, the proposed noncoherent signal detector is more practical when the channel conditions are hard to acquire accurately or hidden from the receiver. The proposed scheme employs the molecular concentration difference to detect the ISI corrupted signals and we demonstrate that it can suppress the ISI effectively. The difference in molecular concentration is a stable characteristic, irrespective of the diffusion channel conditions. In terms of complexity, by excluding matrix operations or likelihood calculations, the new detection scheme is particularly suitable for nanoscale molecular communication systems with a small energy budget or limited computation resource.

Prevalence and Risk Factors of Prostate Cancer in Chinese Men with PSA 4-10 ng/mL Who Underwent TRUS-Guided Prostate Biopsy: The Utilization of PAMD Score.

PURPOSE: To elucidate the characteristics and risk factors for positive biopsy outcomes in Chinese patients with prostate specific antigen (PSA) 4-10 ng/mL and develop a risk-stratification score model. METHODS: The data of 345 patients who underwent transrectal ultrasound-guided prostate biopsy between 2011 and 2013 was retrospectively analyzed. Digital rectal examination (DRE), prostate volume (PV), magnetic resonance imaging (MRI), and smoking status were also collected. Positive biopsy outcomes were defined as prostate cancer (PCa) and high grade PCa (HGPCa, Gleason Score ≥ 7). RESULTS: The median PSA was 7.15 (IQR 5.91-8.45) ng/mL. Overall 138 patients (40.0%) were shown to have PCa, including 100 patients (29.0%) with HGPCa. Smaller PV, elder age, MRI results, and positive DRE were proved to be predictive factors for positive biopsy outcomes in both univariate and multivariate analysis. We developed a "PAMD" score which combined the four factors to categorize patients into three risk groups, and the model performed good predictive sensitivity and specificity. CONCLUSION: The prevalence of prostate cancer in Chinese patients with PSA 4-10 ng/mL was 40%, including 29% patients with high grade disease. DRE, age, MRI, and PV were predictive factors for positive biopsy outcomes, and the PAMD score model could be utilized for risk-stratification and decision-making.