Mp07-07 Radiomics-Based Prognostic Model Guided by Artificial Intelligence for Predicting Clinical Outcomes in Individuals With High-Grade Prostate Cancer

You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence I (MP07)1 May 2024MP07-07 RADIOMICS-BASED PROGNOSTIC MODEL GUIDED BY ARTIFICIAL INTELLIGENCE FOR PREDICTING CLINICAL OUTCOMES IN INDIVIDUALS WITH HIGH-GRADE PROSTATE CANCER Nawar Touma, Maxence Larose, Raphaël Brodeur, Félix Desroches, Nicolas Raymond, Daphnée Bédard-Tremblay, Danahé Leblanc, Fatemeh Rasekh, Hélène Hovington, Bertrand Neveu, Martin Vallières, Louis Archambault, and Frédéric Pouliot Nawar ToumaNawar Touma , Maxence LaroseMaxence Larose , Raphaël BrodeurRaphaël Brodeur , Félix DesrochesFélix Desroches , Nicolas RaymondNicolas Raymond , Daphnée Bédard-TremblayDaphnée Bédard-Tremblay , Danahé LeblancDanahé Leblanc , Fatemeh RasekhFatemeh Rasekh , Hélène HovingtonHélène Hovington , Bertrand NeveuBertrand Neveu , Martin VallièresMartin Vallières , Louis ArchambaultLouis Archambault , and Frédéric PouliotFrédéric Pouliot View All Author Informationhttps://doi.org/10.1097/01.JU.0001008728.41882.d7.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To develop a radiomics-based prognostic model using machine learning to predict lymph node invasion (LNI), biochemical recurrence (BCR), metastasis-free survival (MFS), definitive androgen deprivation therapy (dADT)-free survival (FS), castration resistant prostate cancer (CRPC)-FS, and prostate cancer specific survival (PCSS) in individuals diagnosed with high-grade prostate cancer (PCa). METHODS: A total of 295 individuals with high-grade PCa (Gleason score≥8) underwent radical prostatectomy (RP) with preoperative positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) combined with computed tomography (CT) imaging at our tertiary care health center. Clinical data (CD), including age, prostate-specific antigen (PSA) level, Gleason score, and clinical stage served to build prognostic models to which handcrafted radiomics (HCR) or deep learning-based radiomics (DLR) were added to enhance performance. We trained the models using a subset of the cohort (250 individuals) and optimized them using a stratified 5-fold cross-validation. The selected model was then validated using a test set of 45 individuals. Performance on the test set was evaluated using the area under the curve of the receiver operator characteristic (AUC-ROC) and the concordance index (C-index). A comparison with commonly used nomograms (MSKCC and CAPRA-S) was also performed. RESULTS: Median follow-up was 64.7 (range 29.3-89.6) months. Median age was 66 (range 48-80) years. Median PSA was 7.4 (range 1.1-155.3) ng/ml. 230 (88%) and 31 (12%) had clinical T1-T2 and T3a disease, respectively. The majority (63.7%) had Gleason 8 at biopsy. At RP, 86 (29%) individuals had LNI. At follow-up, 160 had BCR. In the training set, using CD with radiomics yielded better performance for prediction of LNI (AUC=72±5 vs 70±7 MSKCC and 62±4 CAPRA-S) and BCR-FS (CI=65±6 vs 64±3 MSKCC and 63±4 CAPRA-S). Nomograms outperformed our combined radiomics-CD model for prediction of other outcomes, although performances were similar to our built model using CD only. CONCLUSIONS: Integrating imaging data into prognostic tools through artificial intelligence enhances clinical predictions for LNI and BCR, enabling more accurate prognostication. With minimal training, we achieved better results than commonly used nomograms for clinically important endpoints. Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e107 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Nawar Touma More articles by this author Maxence Larose More articles by this author Raphaël Brodeur More articles by this author Félix Desroches More articles by this author Nicolas Raymond More articles by this author Daphnée Bédard-Tremblay More articles by this author Danahé Leblanc More articles by this author Fatemeh Rasekh More articles by this author Hélène Hovington More articles by this author Bertrand Neveu More articles by this author Martin Vallières More articles by this author Louis Archambault More articles by this author Frédéric Pouliot More articles by this author Expand All Advertisement PDF downloadLoading ...