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Directory of Authors from the Journal and their last article.

Ashley V AlfordView Articles

Volume 19, Number 4Review Articles

The Use of Biomarkers in Prostate Cancer Screening and Treatment

Treatment Update

Joseph Renzulli IIAshley V AlfordJoseph M Brito IIIKamlesh K YadavShalini S YadavAshutosh K Tewari

Prostate cancer screening and diagnosis has been guided by prostate-specific antigen levels for the past 25 years, but with the most recent US Preventive Services Task Force screening recommendations, as well as concerns regarding overdiagnosis and overtreatment, a new wave of prostate cancer biomarkers has recently emerged. These assays allow the testing of urine, serum, or prostate tissue for molecular signs of prostate cancer, and provide information regarding both diagnosis and prognosis. In this review, we discuss 12 commercially available biomarker assays approved for the diagnosis and treatment of prostate cancer. The results of clinical validation studies and clinical decision-making studies are presented. This information is designed to assist urologists in making clinical decisions with respect to ordering and interpreting these tests for different patients. There are numerous fluid and biopsy-based genomic tests available for prostate cancer patients that provide the physician and patient with different information about risk of future disease and treatment outcomes. It is important that providers be able to recommend the appropriate test for each individual patient; this decision is based on tissue availability and prognostic information desired. Future studies will continue to emphasize the important role of genomic biomarkers in making individualized treatment decisions for prostate cancer patients. [Rev Urol. 2017;19(4):221–234 doi: 10.3909/riu0772] © 2018 MedReviews®, LLC

Prostate cancerBiomarkers4KscoreProlarisPCA3Prostate Health IndexApifinyMichigan Prostate ScoreSelectMDxConfirmMDxProMarkPTEN/TMPRSS2:ERGDecipher

Ashok C ChanderView 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

Ashutosh K TewariView Articles

Volume 19, Number 4Review Articles

The Use of Biomarkers in Prostate Cancer Screening and Treatment

Treatment Update

Joseph Renzulli IIAshley V AlfordJoseph M Brito IIIKamlesh K YadavShalini S YadavAshutosh K Tewari

Prostate cancer screening and diagnosis has been guided by prostate-specific antigen levels for the past 25 years, but with the most recent US Preventive Services Task Force screening recommendations, as well as concerns regarding overdiagnosis and overtreatment, a new wave of prostate cancer biomarkers has recently emerged. These assays allow the testing of urine, serum, or prostate tissue for molecular signs of prostate cancer, and provide information regarding both diagnosis and prognosis. In this review, we discuss 12 commercially available biomarker assays approved for the diagnosis and treatment of prostate cancer. The results of clinical validation studies and clinical decision-making studies are presented. This information is designed to assist urologists in making clinical decisions with respect to ordering and interpreting these tests for different patients. There are numerous fluid and biopsy-based genomic tests available for prostate cancer patients that provide the physician and patient with different information about risk of future disease and treatment outcomes. It is important that providers be able to recommend the appropriate test for each individual patient; this decision is based on tissue availability and prognostic information desired. Future studies will continue to emphasize the important role of genomic biomarkers in making individualized treatment decisions for prostate cancer patients. [Rev Urol. 2017;19(4):221–234 doi: 10.3909/riu0772] © 2018 MedReviews®, LLC

Prostate cancerBiomarkers4KscoreProlarisPCA3Prostate Health IndexApifinyMichigan Prostate ScoreSelectMDxConfirmMDxProMarkPTEN/TMPRSS2:ERGDecipher

Augusto MaggioniView Articles

Volume 15, Number 4Review Articles

Intracavitary Immunotherapy and Chemotherapy for Upper Urinary Tract Cancer: Current Evidence

Systematic Review

Luca CarmignaniRoberto BianchiGabriele CozziNicola MacchioneCarlo MarenghiSara MelegariMarco RossoElena TondelliAugusto MaggioniAngelica Grasso

A review of the literature was performed to summarize current evidence regarding the efficacy of topical immunotherapy and chemotherapy for upper urinary tract urothelial cell carcinoma (UUT-UCC) in terms of post-treatment recurrence rates. A Medline database literature search was performed in March 2012 using the terms upper urinary tract, urothelial cancer, bacillus Calmette-Guérin (BCG), and mitomycin C. A total of 22 full-text articles were assessed for eligibility, and 19 studies reporting the outcomes of patients who underwent immunotherapy or chemotherapy with curative or adjuvant intent for UUT-UCC were chosen for quantitative analysis. Overall, the role of immunotherapy and chemotherapy for UUT-UCC is not firmly established. The most established practice is the treatment of carcinoma in situ (CIS) with BCG, even if a significant advantage has not yet been proven. The use of BCG as adjuvant therapy after complete resection of papillary UUT-UCC has been studied less extensively, even if recurrence rates are not significantly different than after the treatment of CIS. Only a few reports describe the use of mitomycin C, making it difficult to obtain significant evidence. [Rev Urol. 2013;15(4):145-153 doi: 10.3909/riu0579] © 2014 MedReviews®, LLC

ImmunotherapyChemotherapyBacillus Calmette-GuérinUpper urinary tractUrothelial cell carcinomaMitomycin C

Barbara GowerView Articles

Volume 21, Number 4Review Articles

Impact of Demographic Factors and Systemic Disease on Urinary Stone Risk Parameters Amongst Stone Formers

Original Research

Dean G AssimosKyle WoodWilliam PooreBarbara GowerCarter BoydDustin WhitakerOmotola AshorobiRobert Oster

This article examines via multivariate analysis the associations between demographic factors and systemic diseases on stone risk parameters in a stone-forming population. A retrospective chart review of adult stone formers who completed 24-hour urine collections from April 2004 through August 2015 was performed. Data was collected on age, sex, race, body mass index (BMI), and diagnoses of diabetes and hypertension. CT imaging and renal/abdominal ultrasonography (within ±6 mo) were reviewed for diagnosis of fatty liver disease. Statistical analysis included Pearson and Spearman correlation analysis, and linear and logistic regression analyses, both univariate and multivariate. Five hundred eighty-nine patients were included. Numerous urinary parameters were significant in association with demographic factors or systemic diseases in a multivariate analysis. Older age was associated with decreased calcium (Ca) excretion (P = 0.0214), supersaturation of calcium oxalate (SSCaOx; P = 0.0262), supersaturation of calcium phosphate (SSCaP; P < 0.0001), and urinary pH (P = 0.0201). Men excreted more Ca (P = 0.0015) and oxalate (Ox; P = 0.0010), had lower urine pH (P = 0.0269), and higher supersaturation of uric acid (SSUA; P < 0.0001) than women. Blacks had lower urine volume (P = 0.0023), less Ca excretion (P = 0.0142), less Ox excretion (P = 0.0074), and higher SSUA (P = 0.0049). Diabetes was associated with more Ox excretion (P < 0.0001), lower SSCaP (P = 0.0068), and lower urinary pH (P = 0.0153). There were positive correlations between BMI and Ca excretion (P = 0.0386), BMI and Ox excretion (P = 0.0177), and BMI and SSUA (P = 0.0045). These results demonstrate that demographic factors and systemic disease are independently associated with numerous risk factors for kidney stones. The mechanisms responsible for these associations and disparities (racial differences) need to be further elucidated. [Rev Urol. 2019;21(4):158–165] © 2020 MedReviews®, LLC

ObesityKidney stonesDiabetesSystemic diseaseFatty Liver