David M. Albala - Reviews in Urology

All articles from this author are listed below.

Urologic surgery is most commonly performed in 1 of 3 ambulatory settings—a physician office, a hospital outpatient setting, or an ambulatory surgery center—but differences in value among these settings are poorly understood. This study examines real-world practice patterns of community urologists and documents outcomes and costs for a set of common urologic procedures performed in outpatient settings.

Physician practice patterns Ambulatory surgical procedures urologic diseases ambulatory care

Volume 23, Number 1 Meet The Expert

Meet the Expert: Erika Ferrozzo, MHA

David M. Albala Erika Ferrozzo

In-office dispensaries (IODs) are frequently discussed but rarely implemented in urology private group practices. In this interview, Erika Ferrozzo, MHA, CEO of the Idaho Urologic Institute and a member of the LUGPA Executive Leadership Program’s class of 2023, explains how the Idaho Urologic Institute identified an opportunity that helped its patients experience improved adherence and ease of access to treatment and that earned the practice a year-over-year $250 000 increase in net revenue.

Urology Group practice equipment and supplies

Volume 17, Number 1 Original Research

Hemostasis During Urologic Surgery: Fibrin Sealant Compared With Absorbable Hemostat

Jonathan Batiller Liza Ovington Jessica Shen John Albanese Bogdan Ilie Richard Kocharian Jerome B Riebman David M. Albala

In the United States, fibrin sealants have been used to achieve hemostasis for nearly two decades. Although their clinical utility was first demonstrated in cardiac surgery, their effectiveness and safety have since been demonstrated to extend to a wide array of procedures. Fibrin sealants typically contain two components—fibrinogen and thrombin—that are combined and delivered simultaneously to a target bleeding site in order to achieve hemostasis. However, many commercial formulations contain other additional components, such as antifibrinolytic agents, that have been associated with adverse outcomes. This subanalysis compares the safety and effectiveness of a fibrin sealant versus an absorbable hemostat for achieving hemostasis during urologic procedures with mild to moderate bleeding. [Rev Urol. 2015;17(1):25-30 doi: 10.3909/ricm0647] © 2015 MedReviews®, LLC

Bleeding Hemostasis Hemostatics Fibrin tissue adhesive Urologic surgical procedures Surgical technique

Volume 18, Number 1 Practice Profile

Associated Medical Professionals, Syracuse, NY

Neil F Mariados Christopher M Pieczonka David M. Albala

Volume 18, Number 4 Practice Profile

Advanced Prostate Cancer in Large Group Practices

Gary M Kirsh Neal D Shore David M. Albala

Volume 19, Number 2 Practice Profile

A Discussion on the AUA Castrate-resistant Prostate Cancer Guidelines

David F Penson David M. Albala

Volume 19, Number 3 Practice Profile

Imaging and Treatment Recommendations in Patients With Castrate-resistant Prostate Cancer

David M. Albala

Volume 20, Number 3 KOL Interview

Management Options for Nocturia at a Large Urology Group Practice

David M. Albala David O Sussman

Volume 22, Number 4 Review 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

Ashok C Chander Wendell R Su David M. Albala Grannum R Sant Jonathan S Varsanik Michael S Manak Matthew J Whitfield Brad J Hogan CJ Jiang

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 cancer Artificial intelligence Phenotypic biomarkers Machine vision Machine learning

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