Authors

Main Content

Top Content

Directory of Authors from the Journal and their last article.

Jonathan E HowardView Articles

Volume 21, Number 2Review Articles

Nocturia in Patients With Multiple Sclerosis

Management Review

Benjamin M BruckerRoger R DmochowskiW Stuart ReynoldsBenoit PeyronnetXavier GaméLauren B KruppGérard AmarencoJean-Nicolas CornuLana Zhovtis RyersonCarrie Lyn SammarcoJonathan E HowardRobert W Charlson

The prevalence of nocturia in patients with multiple sclerosis (MS) is high, ranging from 20.9% to 48.8% in this population. Its underlying pathophysiology is complex and different from the non-neurogenic population. In the MS population, the pathophysiology may involve neurogenic lower urinary tract dysfunction (NLUTD) such as detrusor overactivity (NDO), detrusor-sphincter dyssynergia, or detrusor underactivity resulting in reduced bladder capacity. Nocturnal polyuria is also a significant contributor to the pathogenesis of nocturia in MS patients and may be the result of specific mechanisms such as nocturnal hypertension through autonomic cardiovascular dysfunction or lack of diurnal variation of antidiuretic hormone production (ADH) due to demyelinating lesions of the spinal cord. Nocturia might be particularly burdensome in MS patients by contributing to fatigue, a common and highly debilitating symptom in this population. There is likely a complex and multidirectional relationship between nocturia, other sleep disorders, and fatigue in the MS population that has yet to be explored. The assessment of nocturia in MS should rely upon a thorough history and physical examination. Urinalysis should be done to rule out urinary tract infection, a frequency-volume chart might help elucidating the underlying mechanisms, and post-void residual volume may be of interest to screen for urinary retention that could be asymptomatic in MS patients. Other tests such as urodynamics or polysomnography are indicated in selected patients. The treatment should be tailored to the underlying cause. The first steps involve behavioral interventions and treatment of cofactors. When possible, the predominant mechanism should be addressed first. In case of predominant NDO, antimuscarinics and beta-3 agonists should be offered as a first-line treatment and intradetrusor injections of botulinum toxin as a second-line treatment. In cases of incomplete bladder emptying, clean-intermittent self-catheterization is often used as part of multiple other interventions. In cases of nocturnal polyuria, desmopressin may be offered, inclusive of use of newer formulations (desmopressin acetate nasal spray, desmopressin orally disintegrated tablet) in countries where they are approved. [Rev Urol. 2019;21(2/3):63–73] © 2019 MedReviews®, LLC

NocturiaMultiple sclerosisFatigueDesmopressinNocturnal polyuriavoiding diary

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