MP09-04 DIAGNOSTIC PERFORMANCE OF A NOVEL RADIOMIC MODEL FOR PREDICTING POST-TREATMENT PROSTATE CANCER RECURRENCE: A COMPARISON TO CAPRA AND MSKCC NOMOGRAMS

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP09-04 DIAGNOSTIC PERFORMANCE OF A NOVEL RADIOMIC MODEL FOR PREDICTING POST-TREATMENT PROSTATE CANCER RECURRENCE: A COMPARISON TO CAPRA AND MSKCC NOMOGRAMS Linda Huynh, Olivia Taylor, Shuo Wang, Thomas Ahlering, and Michael Baine Linda HuynhLinda Huynh More articles by this author , Olivia TaylorOlivia Taylor More articles by this author , Shuo WangShuo Wang More articles by this author , Thomas AhleringThomas Ahlering More articles by this author , and Michael BaineMichael Baine More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: mpMRI-derived radiomic features have been shown to capture sub-visual patterns for quantitative characterization of tumor phenotype. We seek to compare the diagnostic performance of a mpMRI-based radiomic model to currently available nomograms for prediction of post-radical prostatectomy (RP) biochemical recurrence (BCR). METHODS: mpMRI was obtained from 76 patients who had underwent RP for treatment of localized PCa. All patients had ≥2 years follow-up and those with neo-adjuvant or adjuvant treatment were excluded. Radiomic analysis and cross-validation of mpMRI features yielded features significantly correlated with BCR, defined as two consecutive serum PSA ≥0.2ng/ml. These features were aggregated to construct a radiomic model, which was compared to the risk scores generated by inputting patients’ clinicodemographic features into the USCF Cancer of the Prostate Risk Assessment (UCSF-CAPRA) score and Memorial Sloan Kettering Cancer Center (MSKCC) Pre-Radical Prostatectomy nomogram. The performance of each model was compared utilizing receiver-operator curve (ROC) analysis and area under the curve (AUC) was reported. RESULTS: Table 1 illustrates the clinicopathologic characteristics. In feature extraction and ranking, six radiomic features were determined to be important and non-redundant in predicting PCa recurrence (Figure 1). These features were aggregated into the radiomic model and repeated five-fold cross validation yielded a model with AUC of 0.95±0.06, 33% sensitivity, and 100% specificity. UCSF-CAPRA and MSKCC nomograms yielded AUC of 0.72±0.07 and 0.82±0.07, respectively. CONCLUSIONS: The mpMRI-derived radiomic model performed well when compared to the UCSF-CAPRA score and MSKCC Pre-Radical Prostatectomy nomogram. Future projects will incorporate patient demographics and disease characteristics available at the time of initial PCa diagnosis to improve the radiomic model accuracy. Source of Funding: N/A © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e105 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Linda Huynh More articles by this author Olivia Taylor More articles by this author Shuo Wang More articles by this author Thomas Ahlering More articles by this author Michael Baine More articles by this author Expand All Advertisement PDF downloadLoading ...