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Directory of Authors from the Journal and their last article.

Michael S CooksonView Articles

Volume 9, Number 4Review Articles

Estrogenic Side Effects of Androgen Deprivation Therapy

Treatment Update

Michael S CooksonJames A EasthamTheresa A GuiseMichael G OefeleinMatthew R SmithCelestia Higano

Androgen deprivation therapy (ADT) is part of standard therapy for locally advanced or metastatic prostate cancer and is frequently used in men with a rising prostate-specific antigen following radical prostatectomy or radiation therapy. In some men, ADT may be administered for years or even decades. The intended therapeutic effect of ADT is testosterone deficiency. Because estrogen is a normal metabolite of testosterone, ADT also results in estrogen deficiency. ADT has a variety of adverse effects, many of which are primarily related to estrogen deficiency. Bone mineral density may decrease by 4% to 13% per year in men receiving ADT. The fracture rate for patients on ADT averages 5% to 8% per year of therapy. Hot flashes, gynecomastia, and breast tenderness are common side effects associated with ADT. In the clinic, minimum baseline testing should include weight measurement, blood pressure reading, and fasting lipid panel and serum glucose tests. Currently, there are no large outcome trials in men on ADT testing the available therapies for adverse effects. No therapies are specifically approved for treatment of adverse effects in men on ADT. Although some therapies can be used for a single indication (based upon small studies), there is currently no agent to treat the multiple estrogenic side effects of ADT. [Rev Urol. 2007;9(4):163-180]

Androgen deprivation therapyCardiovascular diseaseGynecomastiaOsteoporosis fractureMale hot flashes

Michael S ManakView Articles

Volume 22, Number 4Review Articles

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

Original Research

David M. AlbalaGrannum R SantJonathan S VarsanikMichael S ManakMatthew J WhitfieldBrad J HoganWendell R SuCJ JiangAshok C Chander

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell–based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients. [Rev Urol. 2020;22(4):159–167] © 2021 MedReviews®, LLC

Prostate cancerArtificial intelligencePhenotypic biomarkersMachine visionMachine learning

Michael SkouterisView Articles

Volume 20, Number 1Original Research

Transrectal Ultrasound–guided Versus Transperineal Mapping Prostate Biopsy: Complication Comparison

Transrectal ultrasound guided prostate biopsy • Transperieneal mapping prostate biopsy • Complications

Vladimir MouravievE David CrawfordVassilios M SkouterisPaul AranguaMarios Panagiotis MetsinisMichael SkouterisGeorge ZacharopoulosNelson N Stone

Herein, the authors compare morbidity in men who underwent both transrectal ultrasound–guided (TRUS) prostate biopsy and transperineal mapping biopsy (TPMB) at two institutions with extensive experience in both procedures. We also identified strategies and predictive factors to reduce morbidity for both procedures. In our study, 379 men from two institutions, of which 265 (69.9%) had a prior TRUS-guided biopsy, also had TPMB performed via a template with biopsies taken at 5-mm intervals. Men in the TRUS group had a median of 12 cores sampled whereas the TPMB group had 51.5 (range, 16-151). The median biopsy density was 1.1 core/cc prostate volume. Median age and prostate-specific antigen (PSA) level were 65 years (range, 34-86) and 5.5 ng/mL (range, 0.02-118). Of these men, 11 of 265 (4.2%) who had TRUS biopsy developed urinary tract infection compared with 3 of 379 (0.79%) of those with mapping biopsy. Infection was 14.8% in TRUS biopsy group with 13 or more cores versus 2.9% in those with 12 or less (OR, 5.8; 95% CI, 1.6-21.2; P = 0.003). No men developed retention after TRUS biopsy whereas 30 of 379 (7.9%) did following TPMB. Older age, larger prostate volume (PV), and higher core number were associated with retention. On linear regression only age (P = 0.010) and PV (P = 0.016) remained as significant associations. Men older than 65 years had 12.8% versus 3.9% (OR, 3.7; 95% CI, 1.6-8.4, P = 0.001) and PV greater than 42 cc had 13.4% versus 2.7% (OR, 5.7; 95% CI, 2.1-15.1) retention incidence. In the present study TPMB is rarely associated with infection (0.78%) but more commonly with urinary retention (7.9%). Men older than 65 years and with PV greater than 42 cc were at four to five times greater retention risk. Consideration should be given to discharging these men with a urinary catheter following TPMB. [Rev Urol. 2018;20(1):19–25 doi: 10.3909/riu0785] © 2018 MedReviews, LLC®

ComplicationsTransrectal ultrasound guided prostate biopsyTransperieneal mapping prostate biopsy