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Directory of Authors from the Journal and their last article.

Marybeth McCallView Articles

Volume 17, Number 1Review Articles

Finding the Wolf in Sheep’s Clothing: The 4Kscore Is a Novel Blood Test That Can Accurately Identify the Risk of Aggressive Prostate Cancer

Diagnosis and Screening Update

Dipen J ParekhSanoj PunnenMASNicola Pavan

Better biomarkers that can discriminate between aggressive and indolent phenotypes of prostate cancer are urgently needed. In the first 20 years of the prostate-specific antigen (PSA) era, screening for prostate cancer has successfully reduced prostate cancer mortality, but has led to significant problems with overdiagnosis and overtreatment. As a result, many men are subjected to unnecessary prostate biopsies and overtreatment of indolent cancer in order to save one man from dying of prostate cancer. A novel blood test known as the 4Kscore® Test (OPKO Lab, Nashville, TN) incorporates a panel of four kallikrein protein biomarkers (total PSA, free PSA, intact PSA, and human kallikrein-related peptidase 2) and other clinical information in an algorithm that provides a percent risk for a high-grade (Gleason score ≥ 7) cancer on biopsy. In 10 peer-reviewed publications, the four kallikrein biomarkers and algorithm of the 4Kscore Test have been shown to improve the prediction not only of biopsy histopathology, but also surgical pathology and occurrence of aggressive, metastatic disease. Recently, a blinded prospective trial of the 4Kscore Test was conducted across the United States among 1012 men. The 4Kscore Test replicated previous European results showing accuracy in predicting biopsy outcome of Gleason score ≥ 7. In a recent case-control study nested within a population-based cohort from Västerbotten, Sweden, the four kallikrein biomarkers of the 4Kscore Test also predicted the risk for aggressive prostate cancer that metastasized within 20 years after the test was administered. These results indicate that men with an abnormal PSA or digital rectal examination result, and for whom an initial or repeat prostate biopsy is being considered, would benefit from a reflex 4Kscore Test to add important information to the clinical decision-making process. A high-risk 4Kscore Test result may be used to select men with a high probability of aggressive prostate cancer who would benefit from a biopsy of the prostate to prevent an adverse and potentially lethal outcome from prostate cancer. Men with a low 4Kscore Test result may safely defer biopsy. [Rev Urol. 2015;17(1):3-13 doi: 10.3909/riu0668] © 2015 MedReviews®, LLC

Prostate cancerBiomarkerScreeningHigh-grade prostate cancer

Matthew C FerroniView Articles

Volume 19, Number 2Review Articles

The Use of Intraoperative Cell Salvage in Urologic Oncology

Surgical Update

Andres F CorreaMatthew C FerroniTimothy D LyonBenjamin J DaviesMichael C Ost

Intraoperative cell salvage (IOCS) has been used in urologic surgery for over 20 years to manage intraoperative blood loss and effectively minimize the need for allogenic blood transfusion. Concerns about viability of transfused erythrocytes and potential dissemination of malignant cells have been addressed in the urologic literature. We present a comprehensive review of the use of IOCS in urologic oncologic surgery. IOCS has been shown to preserve the integrity of erythrocytes during processing and effectively provides cell filtration to mitigate the risk of tumor dissemination. Its use is associated with reduction in the overall need for allogenic blood transfusion, which clinically reduces the risk of hypersensitivity reactions and disease transmission, and may have important implications on overall oncologic outcomes. In the context of a variety of urologic malignancies, including prostate, urothelial, and renal cancer, the use of IOCS appears to be safe, without risk of tumor spread leading to metastatic disease or differences in cancer-specific and overall survival. IOCS has been shown to be an effective intraoperative blood management strategy that appears safe for use in urologic oncology surgery. The ability to reduce the need for additional allogenic blood transfusion may have significant impact on immune-mediated oncologic outcomes. [Rev Urol. 2017;19(2):89–96 doi: 10.3909/riu0721] © 2017 MedReviews®, LLC

Urologic oncologyCell salvagetransfusion

Matthew J WhitfieldView 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