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Directory of Authors from the Journal and their last article.

Michael J ConsoloView Articles

Volume 18, Number 4Review Articles

Barriers to Accessing Urethroplasty

Treatment Update

David O SussmanGordon A BrownMichael J ConsoloKirin K SyedChristopher RobisonJacob McFaddenDavid I ShalowitzBradley D Figler

Urethroplasty is an effective treatment for men with anterior urethral strictures, but is utilized less frequently than ineffective treatments such as internal urethrotomy. We sought to identify provider-level barriers to urethroplasty. An anonymous online survey was emailed to all Mid-Atlantic American Urological Association members. Six scenarios in which urethroplasty was the most appropriate treatment were presented. Primary outcome was recommendation for urethroplasty in ≥ three clinical scenarios. Other factors measured include practice zip code, urethroplasty training, and proximity to a urethroplasty surgeon. Multivariate logistic regression identified factors associated with increased likelihood of urethroplasty recommendation. Of 670 members emailed, 109 (16%) completed the survey. Final analysis included 88 respondents. Mean years in practice was 17.2. Most respondents received formal training in urethroplasty: 43 (49%) in residency, 5 (6%) in fellowship, and 10 (11%) in both; 48 respondents (55%) had a urethroplasty surgeon in their practice, whereas 18 (20%) had a urethroplasty surgeon within 45 minutes of his or her primary practice location. The only covariate that was associated with an increased likelihood of recommending urethroplasty in ≥ three scenarios was formal urethroplasty training. Most members (68%) reported no barriers to referring patients for urethroplasty; the most common barriers cited were long distance to urethroplasty surgeon (n = 13, 15%) and concern about complications (n = 8, 9%). Urethroplasty continues to be underutilized in men with anterior urethral strictures, potentially due to lack of knowledge dissemination and access to a urethroplasty surgeon. Appropriate urethroplasty utilization may increase with greater exposure to urethroplasty in training. [Rev Urol. 2016;18(4):188-193 doi: 10.3909/riu0731] © 2016 MedReviews®, LLC

UrethroplastyUrethral stricturesBarriersPhysician practice patterns

Michael J KemeterView Articles

Volume 16, Number 4Review Articles

A Guide for Clinicians in the Evaluation of Emerging Molecular Diagnostics for Newly Diagnosed Prostate Cancer

Diagnosis Update

Michael J KemeterPhillip G FebboSteven E CanfieldAdam S KibelH Jeffrey LawrenceJudd W Moul

Prostate-specific antigen (PSA) screening is associated with a decline in prostate cancerrelated mortality. However, screening has also led to overdiagnosis and overtreatment of clinically insignificant tumors. Recently, certain national guidelines (eg, US Preventive Services Task Force) have recommended against PSA screening, which may lead to a reverse-stage migration. Although many prostate tumors are indolent at presentation, others are aggressive and are appropriate targets for treatment interventions. Utilization of molecular markers may improve our ability to measure tumor biology and allow better discrimination of indolent and aggressive tumors at diagnosis. Many emerging commercial molecular diagnostic assays have been designed to provide more accurate risk stratification for newly diagnosed prostate cancer. Unfamiliarity with molecular diagnostics may make it challenging for some clinicians to navigate and interpret the medical literature to ascertain whether particular assays are appropriately developed and validated for clinical use. Herein, the authors provide a framework for practitioners to use when assessing new tissue-based molecular assays. This review outlines aspects of assay development, clinical and analytic validation and clinical utility studies, and regulatory issues, which collectively determine whether tests (1) are actionable for specific clinical indications, (2) measurably influence treatment decisions, and (3) are sufficiently validated to warrant incorporation into clinical practice. [Rev Urol. 2014;16(4):172-180 doi: 10.3909/riu0644] © 2014 MedReviews®, LLC

Prostate cancerBiomarkerClinical utilityGenomic prostate scoreAdverse pathologyClinical validationMolecular diagnostics

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