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Directory of Authors from the Journal and their last article.

Vincent LinderView Articles

Volume 19, Number 1Review Articles

Use of the 4Kscore Test to Predict the Risk of Aggressive Prostate Cancer Prior to Prostate Biopsy: Overall Cost Savings and Improved Quality of Care to the US Healthcare System

Health Economics

Yan DongVincent LinderJeffrey D VoigtStephen Zappala

The 4Kscore® Test (BioReference Laboratories, Elmwood Park, NJ) is a blood test that accurately determines the risk of aggressive prostate cancer and significantly reduces prostate biopsies and associated overdiagnosis and overtreatment of indolent cancer. A budget impact model was developed to test the hypothesis that the 4Kscore Test can improve quality of care and deliver cost savings for patients who are suspected of having prostate cancer and would otherwise undergo prostate biopsy under the current standard of care (SOC) in the United States. The direct costs (diagnosis plus treatment) utilized in the model are based on Medicare payment data and were calculated over a 1-year time horizon. The model compares SOC, in which all patients have prostate biopsy, to a “4Kscore strategy,” in which the 4Kscore Test is used to guide the decision to biopsy the prostate. A set of one-way sensitivity analyses was conducted to examine the robustness of the findings. Savings of more than $169 million (15.6% of total SOC costs) were realized in the 4Kscore strategy versus SOC ($917 M versus $1,086 M, respectively) in a cohort of 100,000 patients. Sensitivity analyses demonstrated that the findings are robust. Most cost savings for the 4Kscore strategy were realized in patients who, when managed by SOC, are found to have no prostate cancer or Gleason score 6 pathology. The patients with Gleason score 6 exhibited the greatest benefits from the 4Kscore strategy, avoiding both an unnecessary prostate biopsy and subsequent overtreatment. The 4Kscore Test was shown to significantly reduce costs to the healthcare system while improving patients’ quality of care. Providers and their patients suspected of having prostate cancer should consider using the 4Kscore Test prior to proceeding with prostate biopsy. [Rev Urol. 2017;19(1):1-10 doi: 10.3909/riu0753] © 2017 MedReviews®, LLC

Prostate cancerPSA screening4KscoreProstate biopsyCost savings

Vincy JohnView Articles
Vladimir IoffeView Articles

Volume 18, Number 4Review Articles

Frequency of Gleason Score 7 to 10 in 5100 Elderly Prostate Cancer Patients

Cancer Screening Update

Navin ShahVladimir Ioffe

Men 70 to 80 years of age are known to have an increased incidence of high-grade (Gleason sum score [GSS] 7-10) prostate cancer. We determined the frequency of high-grade prostate cancer among men 70 to 80 years old in our practice. We retrospectively reviewed our 5100 prostate cancer patients who are 70 to 80 years old and who opted for radiation therapy (external radiation, brachytherapy, or combination). Data were gathered on race, prostate-specific antigen value, digital rectal examination (DRE) results, and GSS. Patients were further subdivided by age in two categories, those 70 to 75 years and 76 to 80 years, and also by time period: 2006-2010 and 2011-2015. In patients 70 to 75 years, 1426 patients had a GSS of 6 (41%) and 2042 patients had a GSS of 7 to 10 (59%). In patients 76 to 80 years old, 553 had a GSS of 6 (34%) and 1079 had a GSS of 7 to 10 (66%). In 1432 patients with an abnormal DRE result, the GSS was 6 in 376 (26%) and GSS was 7 to 10 in 1059 (74%). Based on analysis of 5100 prostate cancer patients in our practice, we determined that 61% of patients age 70 to 80 have a high-grade prostate cancer, as do 59% of patients age 70 to 75 years, and 66% of patients between age 76 and 80 years. Because biopsy underestimates the grade in GSS 6 patients by 50%, the actual frequency is approximately 80%. In patients with prostate cancer who had an abnormal DRE result, 74% had a GSS of 7 to 10—approximately 85% when accounting for biopsy under-grading. [Rev Urol. 2016;18(4):181-187 doi: 10.3909/riu0732] © 2016 MedReviews®, LLC

Cancer, prostateGleason scoreAge

Wanda WiltView Articles

Volume 23, Number 1Treatment Review

Consistency in Care Opportunities for Prostate Cancer

Wanda WiltNicole SmithKatie GrantJayme NalleyJody Pinkerton

Through data analysis and multiple interviews and insights, this study attempted to address the inconsistency in care for patients with prostate cancer who shared similar journey time points, demographics, and care center expertise. The Consistency of Care Project aimed to evaluate the impact of efforts to improve targeted metrics surrounding crucial clinical interventions of prostate-specific antigen monitoring, surveillance scanning, and pharmacologic interventions over a 9-month period. For comparison, 15 private urology practices of like size, patient population, and demographics were monitored. Ten of the practices benefitted from reviewed workflow training on the PPS Analytics data platform; access to a PPS Analytics Clinical Analyst, who supported education for identification of actionable patients; consistent data analysis; workflow support; and regular check-in meetings to monitor progress. The 5 control sites were monitored without additional, purposeful intervention. Outcomes support the hypothesis that inconsistency in care can begin to be addressed through focused workflows, strong navigation, and attention to key performance indicators. Attrition rate differences of 32% vs 6% improvements (reengaging patients for care who had no next appointment scheduled). On average, the experimental group increased the metastatic castration-sensitive prostate cancer diagnosis rate by 10%. However, the treatment rates measured a relative increase of 35% but an average of 11% absolute improvement at the supported sites vs 6% at the control sites. Patients with metastatic castration-resistant prostate cancer at the supported sites improved by 20%, compared with those in the control group, who improved by 4%. Care teams with strong workflows, supportive resources, and consistent care pathways—when combined with data analytics—can influence care and drive increased, measurable differences.

Prostate cancerProstatic neoplasmsUrologyNeoplasmpatient navigation

Wayne JG HellstromView Articles

Volume 8, Supplement 4Review Articles

Current Concepts in Ejaculatory Dysfunction

Advances in Alpha-Blocker Therapy in the Management of Urological Disorders

Wayne JG HellstromJeffrey P Wolters

Although erectile dysfunction has recently become the most well-known aspect of male sexual dysfunction, the most prevalent male sexual disorders are ejaculatory dysfunctions. Ejaculatory disorders are divided into 4 categories: premature ejaculation (PE), delayed ejaculation, retrograde ejaculation, and anejaculation/anorgasmia. Pharmacologic treatment for certain ejaculatory disorders exists, for example the off-label use of selective serotonin reuptake inhibitors for PE. Unfortunately, the other ejaculatory disorders are less studied and not as well understood. This review revisits the physiology of the normal ejaculatory response, specifically explores the mechanisms of anejaculation, and presents emerging data. The neurophysiology of the ejaculatory reflex is complex, making classification of the role of individual neurotransmitters extremely difficult. However, recent research has elucidated more about the role of serotonin and dopamine at the central level in the physiology of both arousal and orgasm. Other recent studies that look at differing pharmacokinetic profiles and binding affinities of the 1-antagonists serve as an indication of the centrally mediated role of ejaculation and orgasm. As our understanding of the interaction between central and peripheral modulations and regulation of the process of ejaculation increases, the probability of developing centrally acting pharmaceutical agents for the treatment of sexual dysfunction approaches reality. [Rev Urol. 2006;8(suppl 4):S18-S25]

TamsulosinAlfuzosinRetrograde ejaculationAnejaculationEjaculatory disorders

Wendell R SuView 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