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Directory of Authors from the Journal and their last article.

Brad J HoganView 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

Bradley D FiglerView 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

Brooke EdwardsView Articles

Volume 22, Number 2Review Articles

Implementation of a Centralized, Cost-effective Call Center in a Large Urology Community Practice

Original Research

Gary M KirshStephen F KappaChris McClainKrista WallacePaul CinquinaDon LawsonMary M SmithEarl WalzBrooke Edwards

Call centers provide front-line care and service to patients. This study compared call-answering efficiency and costs between the implementation of an internal, centralized call center (January to July 2019) and previously outsourced call-center services (January to July 2018) for a large urology community practice. Retrospective review of call metrics and cost data was performed. Internal call-center leadership, training, and culture was examined through survey of staff and management. A total of 299,028 calls with an average of 5751 calls per week were answered during the study periods. The Average Speed of Answer (ASA) was 1:42 (min:s) for the outsourced call center and 0:14 for the internal call center (P < 0.001), with 70% of outsourced calls answered under 2 minutes compared with 99% of calls for the internal call center (P < 0.001). The Average Handle Time (AHT) for each outsourced call was 5:32 versus 3:41 for the internal call center (P < 0.001). The total operating expenses were 7.7% lower for the internal call center. Surveys revealed the importance of engaged leadership and staff training with feedback, simplified work algorithms, and expanded clinical roles. We found that internal, centralized call centers may provide a call-answering solution with greater efficiency and lower total operating expense versus an outsourced call center for large surgical practices. A culture that emphasizes continuous improvement and empowers call-center staff with expanded clinical roles may ultimately enhance patient communication and service. [Rev Urol. 2020;22(2):67–74] © 2020 MedReviews®, LLC

Cost effectivenessCall centerTelehealthOrganizational efficiency