MP47-15 THE AI-R.E.N.A.L. + SCORE SURPASSES THE HUMAN EXPERT-GENERATED R.E.N.A.L. SCORE IN PREDICTING ONCOLOGIC OUTCOMES OF RENAL TUMORS

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP47-15 THE AI-R.E.N.A.L. + SCORE SURPASSES THE HUMAN EXPERT-GENERATED R.E.N.A.L. SCORE IN PREDICTING ONCOLOGIC OUTCOMES OF RENAL TUMORS Nour Abdallah, Tarik Benidir, Nicholas Heller, Andrew Wood, Fabian Isensee, Resha Tejpaul, Dillon Corrigan, Chalairat Suk-Ouichai, Onuralp Ergun, Alex You, Erick Remer, Samuel Haywood, Venkatesh Kirshnamurthi, Steven Campbell, Nikolaos Papanikolopoulos, and Christopher Weight Nour AbdallahNour Abdallah More articles by this author , Tarik BenidirTarik Benidir More articles by this author , Nicholas HellerNicholas Heller More articles by this author , Andrew WoodAndrew Wood More articles by this author , Fabian IsenseeFabian Isensee More articles by this author , Resha TejpaulResha Tejpaul More articles by this author , Dillon CorriganDillon Corrigan More articles by this author , Chalairat Suk-OuichaiChalairat Suk-Ouichai More articles by this author , Onuralp ErgunOnuralp Ergun More articles by this author , Alex YouAlex You More articles by this author , Erick RemerErick Remer More articles by this author , Samuel HaywoodSamuel Haywood More articles by this author , Venkatesh KirshnamurthiVenkatesh Kirshnamurthi More articles by this author , Steven CampbellSteven Campbell More articles by this author , Nikolaos PapanikolopoulosNikolaos Papanikolopoulos More articles by this author , and Christopher WeightChristopher Weight More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003293.15AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The R.E.N.A.L. nephrometry score is a widely accepted surgical aid for extirpation of a renal mass. Excluding the tumor's location (anterior/posterior), the score categorizes variables that are otherwise continuous in nature to facilitate it. It has not yet been universally adopted at the point of care due to its ambiguity, time consumption, and interobserver variability. We previously showed that the artificial intelligence (AI)-generated R.E.N.A.L. score was non-inferior to the human-generated score in predicting perioperative and oncologic outcomes. We hypothesize that we can surpass the predictive ability of human-generated R.E.N.A.L. scores by creating an AI-generated R.E.N.A.L. score+ (AI+ score) with continuous variables rather than ordinal. METHODS: We had 300 patients with preoperative computed tomography scan showing suspected renal cancer at a single institution. Human score was tabulated by 3 trained medical personnel blinded to AI-generated scores. Deep neural network approach was used to automatically segment kidneys into normal parenchyma and tumor, and geometric algorithms were used to estimate the score's components as continuous variables rather than ordinal. Operating characteristic curves (ROC) were created from logistic regression models, and areas under the curve (AUC) were compiled to evaluate the discriminatory ability of the AI+ score vs ordinally-derived AI and human-generated scores for the oncologic outcomes. We assessed the relative importance of the AI+ score components with respect to the odds of outcome through fit logistic regression models using standardized values. RESULTS: The median age was 60 years (IQR 51-68), and 40% were female. The median tumor size was 4.2 cm (2.6-6.12), and 92% were malignant, including 27%, 37%, and 23% with high-stage, high-grade, and necrosis, respectively. The AI+ score had a superior discriminatory ability for each oncologic outcome, including the prediction of malignancy, high stage, high grade, and tumor necrosis (Figure 1). The “R” component had the highest predictive odds ratio for oncologic outcomes. CONCLUSIONS: The AI+ score was superior in predicting meaningful oncologic outcomes compared to ordinal AI-generated and human-generated scores in a non-ambiguous time-efficient manner. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e649 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nour Abdallah More articles by this author Tarik Benidir More articles by this author Nicholas Heller More articles by this author Andrew Wood More articles by this author Fabian Isensee More articles by this author Resha Tejpaul More articles by this author Dillon Corrigan More articles by this author Chalairat Suk-Ouichai More articles by this author Onuralp Ergun More articles by this author Alex You More articles by this author Erick Remer More articles by this author Samuel Haywood More articles by this author Venkatesh Kirshnamurthi More articles by this author Steven Campbell More articles by this author Nikolaos Papanikolopoulos More articles by this author Christopher Weight More articles by this author Expand All Advertisement PDF downloadLoading ...