Pd36-06 Characterizing Prostate Cancer Risk With 68 Ga-Psma-11 Pet Imaging and AI

You have accessJournal of UrologySurgical Technology & Simulation: Artificial Intelligence III (PD36)1 May 2024PD36-06 CHARACTERIZING PROSTATE CANCER RISK WITH 68GA-PSMA-11 PET IMAGING AND AI Marcus Hacker, Simon Wail, Lubos Dolezel, David Iommi, Thomas Beyer, and Laszlo Papp Marcus HackerMarcus Hacker , Simon WailSimon Wail , Lubos DolezelLubos Dolezel , David IommiDavid Iommi , Thomas BeyerThomas Beyer , and Laszlo PappLaszlo Papp View All Author Informationhttps://doi.org/10.1097/01.JU.0001008916.72488.6a.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Prostate cancer (PC) is one of the most common types of cancer in men. Routine diagnosis of PC relies on image-guided biopsy sampling. However, biopsy-based risk estimations are accurate in only 60-70% cases. In this study we demonstrate the feasibility of providing non-invasive imaging-based PC risk predictions through artificial intelligence (AI) approaches. METHODS: A total of 50 patients who underwent 68Ga-PSMA-11 PET/CT imaging and image-guided biopsy sampling were included. Each CT image was automatically delineated by an nnU-Net deep learning (DL) model to detect the prostate. The resulting prostate mask was utilized to identify regions in the corresponding PET image in which another nnU-Net DL model identified suspicious lesions to generate a whole prostate lesion probability map (P-MAP). This P-MAP together with the corresponding PET image were analyzed to extract fuzzy radiomic features that conform to the Imaging Biomarker Standardization Initiative (IBSI). Each case was labelled based on their invasive biopsy Gleason Scores (GS) to distinguish high (GS >= 4+3) versus low (GS <= 3+4) risk cases. A supervised, automated machine learning approach (Dedicaid Ltd, Austria) was used to build 100 mixed-stacked ensemble learners relying on the radiomic features and their corresponding high-low risk labels. These 100 model instances were merged to compose a super-learner model (SLM).Another 68Ga-PSMA-11 PET/MRI cohort (n=24) served as an independent test set for estimating the performance of the SLM. Here, the reference labels were derived from whole-mount histopathology. Independent test performance was estimated by confusion matrix analytics. Following the evaluation with SLM, sensitivity (SNS), specificity (SPC), positive and negative predictive values (PPV and NVP) and area under the receiver operator characteristics curve (AUC) were calculated for the test cases. RESULTS: Independent SLM testing yielded 88% SNS, 82% SPC, 88% PPV, 82% NPV, and 89% AUC. CONCLUSIONS: This study demonstrates that DL in combination with super learners that employ standardized IBSI fuzzy radiomics accurately characterize high-vs-low risk PC in 68Ga-PSMA-11 PET imaging with a significantly higher predictive performance compared to standard clinical processes involving invasive tissue sampling. Source of Funding: Telix Pharmaceuticals © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e794 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Marcus Hacker More articles by this author Simon Wail More articles by this author Lubos Dolezel More articles by this author David Iommi More articles by this author Thomas Beyer More articles by this author Laszlo Papp More articles by this author Expand All Advertisement PDF downloadLoading ...