Development of a novel nomogram to predict clinically significant prostate cancer using ExoDx urinary biomarker and multiparametric MRI.

Abstract

e17158 Background: The ExoDx urinary biomarker test has been validated for the investigation elevated prostate specific antigen (PSA) levels. The performance of this test in lieu of or in parallel with the radiographic “biomarker” multiparametric magnetic resonance imaging (mpMRI) has not been fully elucidated. The objective of the present study was to develop novel nomogram that incorporates mpMRI, ExoDx Prostate Intelliscore (EPI), patient demographics, and clinical information to assess a patient’s risk of having a clinically significant prostate biopsy (i.e. Gleason Grade Group ≥2) to optimize patient decision making regarding prostate biopsy. Methods: A retrospective study at a single academic medical center was conducted. Patients who underwent ExoDx urinary analysis, had mpMRI imaging, and had a prostate biopsy between 10/1/2019 and 07/31/2024 were included. Demographic data collected included age, race, insurance type/status. Clinical data collected included ExoDx Score (EPI) [continuous variable], multiparametric PI-RADS version 2.1 score, PSA, prostate volume, radiographic prostatitis, number of regions of interest on MRI, and adverse features such as extraprostatic extension, seminal vesicle invasion, and pelvic lymphadenopathy on mpMRI. Primary outcome of interest was clinically significant prostate cancer (i.e. ≥ GG2) on biopsy. Statistical tests of association were performed along with univariable and multivariable Cox regression analysis; independent predictors were screened in a stepwise regression for collinearity. Cross validation testing of the model was performed. Results: 387 patients had EPI scores, and 142 patients who underwent prostate biopsy were included for analysis after EPI score (p< 0.05), PIRADS >3 (p< 0.05), and prostate volume (p< 0.05) were independent prognostic factors in predicting an increased risk of clinically significant prostate cancer on biopsy. A nomogram was developed with accuracy of 69% (95% CI: 0.6072, 0.765), positive predictive value of 47.4%, and a negative predictive value of 72.4%. Conclusions: Our novel nomogram integrates independent urinary and radiographic biomarkers along with demographic information to predict clinically significant prostate cancer. The modest performance is likely due to modest sample size and would benefit from validation with a larger cohort.