Background: A definitive diagnosis of prostate cancer requires a biopsy to obtain tissue for pathologic analysis, but this is an invasive procedure and is associated with complications.Purpose: To develop an artificial intelligence (AI)‐based model (named AI‐biopsy) for the early diagnosis of prostate cancer using magnetic resonance (MR) images labeled with histopathology information.Study Type: Retrospective.Population: Magnetic resonance imaging (MRI) data sets from 400 patients with suspected prostate cancer and with histological data (228 acquired in‐house and 172 from external publicly available databases).Field Strength/Sequence: 1.5 to 3.0 Tesla, T2‐weighted image pulse sequences.Assessment: MR images reviewed and selected by two radiologists (with 6 and 17 years of experience). The patient images were labeled with prostate biopsy including Gleason Score (6 to 10) or Grade Group (1 to 5) and reviewed by one pathologist (with 15 years of experience). Deep learning models were developed to distinguish 1) benign from cancerous tumor and 2) high‐risk tumor from low‐risk tumor.Statistical Tests: To evaluate our models, we calculated negative predictive value, positive predictive value, specificity, sensitivity, and accuracy. We also calculated areas under the receiver operating characteristic (ROC) curves (AUCs) and Cohen's kappa.Results: Our computational method (https://github.com/ih-lab/AI-biopsy) achieved AUCs of 0.89 (95% confidence interval [CI]: [0.86–0.92]) and 0.78 (95% CI: [0.74–0.82]) to classify cancer vs. benign and high‐ vs. low‐risk of prostate disease, respectively.Data Conclusion: AI‐biopsy provided a data‐driven and reproducible way to assess cancer risk from MR images and a personalized strategy to potentially reduce the number of unnecessary biopsies. AI‐biopsy highlighted the regions of MR images that contained the predictive features the algorithm used for diagnosis using the class activation map method. It is a fully automatic method with a drag‐and‐drop web interface (https://ai-biopsy.eipm-research.org) that allows radiologists to review AI‐assessed MR images in real time.Level of Evidence: 1Technical Efficacy Stage: 2

Urinary tract infections (UTIs) are among the most common causes of infections worldwide and can be caused by numerous uropathogens. Enterococci are Gram-positive, facultative anaerobic commensal organisms of the gastrointestinal tract that are known uropathogens. Enterococcus spp. has become a leading cause of healthcare associated infections, ranging from endocarditis to UTIs. In recent years, there has been an increase in multidrug resistance due to antibiotic misuse, especially in enterococci. Additionally, infections due to enterococci pose a unique challenge due to their ability to survive in extreme environments, intrinsic antimicrobial resistance, and genomic malleability. Overall, this review aims to highlight the pathogenicity, epidemiology, and treatment recommendations (according to the most recent guidelines) of enterococci.

The use of testosterone to treat the symptoms of late-onset hypogonadal men has increased recently due to patient and physician awareness. However, concerns regarding the effect of testosterone on the prostate, in particular any possible effect on the risk of prostate cancer have prompted further research in this regard. Surprisingly, numerous retrospective or small, randomized trials have pointed to a possible improvement in male lower urinary tract symptoms (LUTS) in patients treated with testosterone. The exact mechanism of this improvement is still debated but may have a close relationship to metabolic syndrome. For the clinician, the results of these studies are promising but do not constitute high levels of evidence. A thorough clinical examination (including history, examination and laboratory testing of testosterone) should be undertaken before considering the diagnosis of late-onset hypogonadism or instigating treatment for it. Warnings still remain on the testosterone supplement product labels regarding the risk of urinary retention and worsening LUTS, and these should be explained to patients.

Objective: To determine if athermal methods are as effective in preventing blood transfusions as the use of cautery across various prostate volumes following prostate tissue resection for benign prostatic hyperplasia using Aquablation. Patients and methods: The current commercial AQUABEAM robot that performs Aquablation therapy was first used in 2014. Since then numerous clinical studies have been conducted in various countries; Australia, Canada, Germany, India, Lebanon, Spain, New Zealand, United Kingdom, and the United States. All of the clinical trial data since 2014 were pooled with the early commercial procedures from France, Germany, and Spain to determine the effectiveness of haemostatic techniques in reducing the transfusion rate in patients after Aquablation. Results: In all, 801 patients were treated with Aquablation therapy from 2014 to early 2019. The mean (SD, range) prostate volume was 67 (33, 20–280) mL and 31 (3.9%) transfusions were reported. The largest contributing factor to transfusion risk was prostate size and method of traction. There was an increasing risk of transfusions in larger prostates when robust traction using a catheter‐tensioning device (CTD) without cautery was used, ranging from 0.8% to 7.8% in prostates ranging from 20 to 280 mL. However, when standard traction (taping the catheter to the leg, gauze knot synched up to the meatus, or no traction at all) was used and where the surgeon performed bladder neck cautery only when necessary, the risk of transfusion was 1.4–2.5% in prostates ranging from 20 to 280 mL. Conclusions: While the athermal subgroup with robust traction with a CTD had comparable transfusion rates for smaller prostates, the risk increased significantly as prostate volume increased. With standard traction methods and selective bladder neck cautery, the risk of transfusion was reduced to 1.9% across all prostate sizes.