Authors

Main Content

Top Content

Directory of Authors from the Journal and their last article.

Jonathan S VarsanikView 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

Joseph E Dall'EraView Articles

Volume 8, Number 4Review Articles

Penile Rehabilitation After Radical Prostatectomy: Important Therapy or Wishful Thinking?

Management Update

Jesse N MillsRandall B MeachamJoseph E Dall'EraHari K Koul

On the basis of data accumulated thus far, it is reasonable to discuss the implementation of a “penile rehabilitation” program with patients undergoing radical prostatectomy. Central to discussions of penile rehabilitation after radical prostatectomy is evidence demonstrating significant fibrotic changes in the corpus cavernosum that occur postoperatively. Several studies have been published evaluating the efficacy of various pro-erectogenic agents. The limited data regarding intracavernous injections and vacuum constriction devices suggest that an increased percentage of treated patients experienced a return of natural erections compared with patients who received no treatment. Longer, prospective, randomized, placebo-controlled studies will be needed to confirm the utility of these treatments. Data from contemporary studies evaluating the chronic use of oral phosphodiesterase type 5 inhibitors suggest a beneficial effect on endothelial cell function among men suffering from erectile dysfunction due to a variety of causes. Limited data suggest that this effect might be seen among post-prostatectomy patients, implying a possible role for these agents in enhancing the return of sexual function in such individuals. [Rev Urol. 2006;8(4):209-215]

Radical prostatectomyErectile dysfunctionPenile rehabilitationPDE-5 inhibitorsHypoxiaIntracorporeal injectionVacuum constriction device