MP17-04 PILOT STUDY OF MACHINE LEARNING IN THE TASK OF DISTINGUISHING HIGH AND LOW GRADE HYDRONEPHROSIS IN CHILDREN ON ULTRASOUND (2022)

You have accessJournal of UrologyCME1 May 2022MP17-04 PILOT STUDY OF MACHINE LEARNING IN THE TASK OF DISTINGUISHING HIGH AND LOW GRADE HYDRONEPHROSIS IN CHILDREN ON ULTRASOUND Matthew Sloan, Clark Judge, Hui Li, Li Lan, Mason Vierra, Gabriel Kaufmann, Maryellen Giger, and Mohan Gundeti Matthew SloanMatthew Sloan More articles by this author , Clark JudgeClark Judge More articles by this author , Hui LiHui Li More articles by this author , Li LanLi Lan More articles by this author , Mason VierraMason Vierra More articles by this author , Gabriel KaufmannGabriel Kaufmann More articles by this author , Maryellen GigerMaryellen Giger More articles by this author , and Mohan GundetiMohan Gundeti More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002550.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Hydronephrosis (HN) is one of the most frequently detected congenital anomalies in pediatric urology. It can represent a spectrum of etiologies - from a benign transient state to a serious structural issue requiring antibiotic prophylaxis and/or surgical correction. Current management is driven by various classification systems used to interpret and grade renal ultrasound (US) images. However, these systems are inherently subjective and have been found to have low inter-rater reliability. Machine learning may be a means to develop more objective algorithms to classify anomalies and possibly predict outcomes. This pilot study aims to show that machine learning can determine low-grade (LG) from high-grade (HG) HN. METHODS: US images from patients ages 0-8 diagnosed with HN at a single institution were reviewed and collected under a HIPAA compliant and IRB approved protocol. Patients were excluded if they had less than 2 studies prior to surgical intervention, or if they had structural abnormalities, including ectopic ureter, duplicated system, ureterocele, multicystic dysplastic kidney, posterior urethral valves, neurogenic bladder, and prune belly syndrome. US studies were graded according to the Society for Fetal Urology system by a pediatric radiologist. LG and HG HN were defined as SFU grades I-II and III-IV, respectively. In total, 74 patients (202 images) were identified. Each kidney was outlined by a urology resident and reviewed by an attending urologist with consensus in the transverse and longitudinal view. Twenty-five radiomic features were automatically extracted from and subsequently used in the task of distinguishing LG from HG HN with a support-vector machine (SVM) as classifier. Receiver operating characteristic analysis (ROC) was conducted to assess the performance of each individual radiomic feature and the SVM classifier, with the area under the ROC curve (AUC) serving as the figure of merit in the classification task. RESULTS: From ROC analysis, an AUC value of 0.79 (se=0.05) was obtained in a round-robin evaluation method in the task of distinguishing between LG and HG HN. In addition, from a Mann-Kendal Trend Test between SVM output and clinical HN grade, a statistically significant upward trend was found (p-value < 0.0001). CONCLUSIONS: In this pilot study, machine learning demonstrated potential in distinguishing between LG and HG HN, showing a statistically significant relationship with clinical HN grade. Source of Funding: Institute for Translational Medicine Core Subsidy Award © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e285 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Matthew Sloan More articles by this author Clark Judge More articles by this author Hui Li More articles by this author Li Lan More articles by this author Mason Vierra More articles by this author Gabriel Kaufmann More articles by this author Maryellen Giger More articles by this author Mohan Gundeti More articles by this author Expand All Advertisement PDF downloadLoading ...