Mp18-06 Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy

You have accessJournal of UrologyImaging/Uroradiology I (MP18)1 May 2024MP18-06 MULTI-INSTITUTIONAL DEVELOPMENT AND VALIDATION OF A RADIOMIC MODEL TO PREDICT PROSTATE CANCER RECURRENCE FOLLOWING RADICAL PROSTATECTOMY Linda M. Huynh, Benjamin Bonebrake, Joshua Tran, Jacob Marasco, Thomas E. Ahlering, Shuo Wang, and Michael Baine Linda M. HuynhLinda M. Huynh , Benjamin BonebrakeBenjamin Bonebrake , Joshua TranJoshua Tran , Jacob MarascoJacob Marasco , Thomas E. AhleringThomas E. Ahlering , Shuo WangShuo Wang , and Michael BaineMichael Baine View All Author Informationhttps://doi.org/10.1097/01.JU.0001008672.83391.ed.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Multi-parametric magnetic resonance imaging (mpMRI)-derived radiomics have been shown to capture sub-visual patterns for quantitative characterization of prostate cancer (PC) phenotypes. The present study seeks to develop, test, and compare the performance of an MRI-derived radiomic model for the prediction of PC recurrence following definitive treatment with radical prostatectomy (RP). METHODS: mpMRI was obtained from 251 patients who had a minimum of 2 years follow-up following RP at two institutions. The prostate was manually delineated as the region of interest and 924 radiomic features were extracted. All features were tested for stability via intraclass correlation coefficient (ICC) and image normalization via histogram matching. RESULTS: Fourteen important and non-redundant features were found to be predictors of PC recurrence at a mean±SD of 3.2±2.2 years and were aggregated into a radiomic model. Five-fold, ten-run cross-validation yielded a receiver-operator characteristic area under the curve (ROC-AUC) of 0.89±0.04 in the training set (n=225). In comparison, the University of California San Fransisco Cancer of the Prostate Risk Assessment score (UCSF-CAPRA) and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical Prostatectomy nomograms yielded AUC of 0.66±0.05 and 0.67±0.05, respectively (p<0.01). Finally, when the radiomic model was applied to the test set (n=26), ROC-AUC was 0.78 and sensitivity, specificity, positive predictive value, and negative predictive value were 60%, 86%, 52% and 89%, respectively. Accuracy of the radiomic model in predicting PC recurrence was 81%. CONCLUSIONS: The present study is a proof of concept for the use of an mpMRI-derived radiomic model in predicting PC recurrence in 251 prostate cancer patients, yielding fourteen radiomic features significantly associated with recurrence following RP. When these features were aggregated into a radiomic signature, this signature predicted recurrence well in cross-validation and predicted patients at risk for PC recurrence with 81% accuracy. Download PPT Source of Funding: NA © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e302 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Linda M. Huynh More articles by this author Benjamin Bonebrake More articles by this author Joshua Tran More articles by this author Jacob Marasco More articles by this author Thomas E. Ahlering More articles by this author Shuo Wang More articles by this author Michael Baine More articles by this author Expand All Advertisement PDF downloadLoading ...