MP55-18 A NOVEL MACHINE LEARNING FRAMEWORK TO AUTOMATED CHARACTERIZE PROSTATE IMAGING REPORTING AND DATA SYSTEM (PIRADS) ON MRI (2023)

You have accessJournal of UrologyCME1 Apr 2023MP55-18 A NOVEL MACHINE LEARNING FRAMEWORK TO AUTOMATED CHARACTERIZE PROSTATE IMAGING REPORTING AND DATA SYSTEM (PIRADS) ON MRI Giovanni e. Cacciamani, masatomo kaneko, yijing yang, vasileios magoulianitis, jintang xue, jiaxin yang, jinyuan liu, maria sarah L. Lenon, Passant Mohamed, Darryl H. Hwang, karan gill, Manju Aron, Vinay Duddalwar, Suzanne L. Palmer, C.-C. Jay Kuo, Inderbir Gill, Andre Luis Abreu, and Chrysostomos L. Nikias Giovanni e. CacciamaniGiovanni e. Cacciamani More articles by this author , masatomo kaneko masatomo kaneko More articles by this author , yijing yang yijing yang More articles by this author , vasileios magoulianitis vasileios magoulianitis More articles by this author , jintang xue jintang xue More articles by this author , jiaxin yang jiaxin yang More articles by this author , jinyuan liu jinyuan liu More articles by this author , maria sarah L. Lenon maria sarah L. Lenon More articles by this author , Passant MohamedPassant Mohamed More articles by this author , Darryl H. HwangDarryl H. Hwang More articles by this author , karan gill karan gill More articles by this author , Manju AronManju Aron More articles by this author , Vinay DuddalwarVinay Duddalwar More articles by this author , Suzanne L. PalmerSuzanne L. Palmer More articles by this author , C.-C. Jay KuoC.-C. Jay Kuo More articles by this author , Inderbir GillInderbir Gill More articles by this author , Andre Luis AbreuAndre Luis Abreu More articles by this author , and Chrysostomos L. NikiasChrysostomos L. Nikias More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003308.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: To develop an automated machine learning model to characterize Prostate Imaging Reporting and Data System (PIRADS) lesions using biparametric (bp) magnetic resonance imaging (MRI). METHODS: Consecutive men who underwent 3T prostate MRI (T2-weighted [T2WI], diffusion-weighted images [DWI], and apparent diffusion coefficient [ADC]) followed by prostate biopsy (PBx) were identified from a PBx database (IRB# HS-13-00663). MRI were acquired and interpreted according to PIRADS v2 or v2.1 by experienced radiologists. The PIRADS 3-5 lesions were manually segmented on T2WI, high-b-value (b≥1000) DWI, and ADC. Men with single lesion (PIRADS≥3) were included. A novel Green Learning framework with a lightweight model and explainable feature extraction process was used (Figure 1). A data-driven automatic and unsupervised process, namely IP-HOPII, by cascading layers of local-to-global Principal Component Analysis on the input voxels was applied. A spatial-spectral representation was derived of the region of interest. Then a feature selection module was employed to filter out the noisy spectral dimensions. Finally, the best features were fed to the Extreme Gradient Boosting classifier. The performances of classifying PIRADS were assessed by concordance rate for three-class classification and receiver operating characteristic (ROC) analysis for binary classification. The sensitivity and specificity were determined at the Youden index. RESULTS: Overall, 259 patients (205 for training and 54 for validation) with single lesion were included: PIRADS 3, 4, and 5, were 152 (59%), 70 (27%), and 37 (14%), respectively. The concordance rate between PIRADS and the model was 60% for PZ and 67% for TZ lesions. For the PZ, the area under the ROC curve (AUC) to characterize PIRADS were 0.73 for 3 vs 4-5, and 0.76 for 3-4 vs 5, respectively. The sensitivity and specificity were 75% and 69% to classify PIRADS≥4 lesions, and 78% and 71% for PIRADS 5 lesions. For the TZ, the AUC for the model was 0.69 for PIRADS 3 vs 4-5, and 0.64 for PIRADS 3-4 vs 5, respectively. The sensitivity and specificity were 62% and 73% to classify PIRADS≥4 lesions, and 60% and 59% for PIRADS 5 lesions. CONCLUSIONS: Machine learning model following the Green Learning paradigm can accurately characterize lesions on prostate bpMRI. Source of Funding: None. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e771 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Giovanni e. Cacciamani More articles by this author masatomo kaneko More articles by this author yijing yang More articles by this author vasileios magoulianitis More articles by this author jintang xue More articles by this author jiaxin yang More articles by this author jinyuan liu More articles by this author maria sarah L. Lenon More articles by this author Passant Mohamed More articles by this author Darryl H. Hwang More articles by this author karan gill More articles by this author Manju Aron More articles by this author Vinay Duddalwar More articles by this author Suzanne L. Palmer More articles by this author C.-C. Jay Kuo More articles by this author Inderbir Gill More articles by this author Andre Luis Abreu More articles by this author Chrysostomos L. Nikias More articles by this author Expand All Advertisement PDF downloadLoading ...