Mp73-12 artificial intelligence-based cancer mapping to aid in prostate cancer therapy

You have accessJournal of UrologyCME1 Apr 2023MP73-12 ARTIFICIAL INTELLIGENCE-BASED CANCER MAPPING TO AID IN PROSTATE CANCER THERAPY Wayne Brisbane, Alan Priester, Sakina Mohammed Mota, Joshua Shubert, Jeremy Bong, James Sayre, Brittany Berry-Pusey, and Shyam Natarajan Wayne BrisbaneWayne Brisbane More articles by this author , Alan PriesterAlan Priester More articles by this author , Sakina Mohammed MotaSakina Mohammed Mota More articles by this author , Joshua ShubertJoshua Shubert More articles by this author , Jeremy BongJeremy Bong More articles by this author , James SayreJames Sayre More articles by this author , Brittany Berry-PuseyBrittany Berry-Pusey More articles by this author , and Shyam NatarajanShyam Natarajan More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003341.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Focal therapy (FT) for prostate cancer is gaining prominence as an alternative to whole-gland treatment. FT efficacy relies on predicting disease margins, but their underestimation and patient-specific optimization remain largely unaddressed problems. An artificial intelligence (AI)-based cancer mapping and decision support tool was built using pre-biopsy MRI, targeted biopsy data, and PSA to aid urologists in identifying cancer margins. A reader study was conducted to compare this AI-based software against the standard of care (SOC) in determining the extent of clinically significant prostate cancer (csPCa). METHODS: Seven urologists and three radiologists from five institutions with 2 – 23 years of expertise each evaluated 50 prostatectomy cases (total of 1000 reads) Cases were prospectively eligible for FT, with GG 2-3 csPCa, ≥1 region of interest (ROI), and disease that appeared localized to a single hemisphere or the anterior gland. Each case included T2-weighted MRI, ROI segmentation, and pathology reports with conventional locations. Readers were asked to produce contours on each image that prioritized the inclusion of all csPCa, excluding non-csPCa tissue as a secondary objective. First, readers manually defined margins using all given data (SOC). Then, after≥4 weeks had passed, readers produced AI-assisted margins using custom software (iQuest, Avenda Health, CA) [Figure B]. Margins from each method [Figure C] were evaluated against WM pathology data [Figure D] as ground truth. Statistical tests were performed using generalized estimating equations. RESULTS: AI margins had superior sensitivity (97.4% vs. 38.2%, p<0.0001) to SOC margins in classifying csPCa [Figure A]. AI-assisted margins also had superior balanced accuracy, i.e. (specificity+sensitivity)/2, to SOC margins (84.7% vs. 67.2%, p<0.0001). On average, AI-assisted margins completely encapsulated csPCa in 72.8% of cases, compared to only 1.6% of cases with SOC methods (p<0.0001). Furthermore, the average time spent fell from 3.5 minutes (SOC) to 2.0 minutes (AI-assisted, p<0.0001). CONCLUSIONS: AI-assisted cancer mapping helps address the systematic underestimation of csPCa by SOC methods. This study establishes that AI-assisted margins greatly improve csPCa encapsulation, which could improve oncological efficacy for focal treatments. Source of Funding: Avenda Health © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1039 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Wayne Brisbane More articles by this author Alan Priester More articles by this author Sakina Mohammed Mota More articles by this author Joshua Shubert More articles by this author Jeremy Bong More articles by this author James Sayre More articles by this author Brittany Berry-Pusey More articles by this author Shyam Natarajan More articles by this author Expand All Advertisement PDF downloadLoading ...