MP53-12 HOW DO PSA-BASED MARKERS PREDICT CLINICALLY SIGNIFICANT PROSTATE CANCER (CSPCA) ON PROSTATE BIOPSY INDEPENDENT OF PROSTATE SIZE

Abstract

You have accessJournal of UrologyCME1 May 2022MP53-12 HOW DO PSA-BASED MARKERS PREDICT CLINICALLY SIGNIFICANT PROSTATE CANCER (CSPCA) ON PROSTATE BIOPSY INDEPENDENT OF PROSTATE SIZE Max Kuster, Michelle Ou, Jacob Gaines, Eric Macdonald, and Simon Hall Max KusterMax Kuster More articles by this author , Michelle OuMichelle Ou More articles by this author , Jacob GainesJacob Gaines More articles by this author , Eric MacdonaldEric Macdonald More articles by this author , and Simon HallSimon Hall More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002628.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Both PSA density (PSAD) and PSA based makers (4k and PHI) are used to guide prostate biopsy decisions. In this study we examined the correlation between percent free PSA (%fPSA) and PSAD, as well as their potential combined use in determining csPCa risk. METHODS: We identified 331 patients from an IRB-approved prostate MRI database without a prior diagnosis of prostate cancer, who had an mpMRI, %fPSA and PSAD at the time of imaging, and a subsequent prostate biopsy within 1.5 years. The relationship between %fPSA and PSAD was analyzed using Pearson correlation. Logistic regression and ROC curves were used to test the diagnostic ability of %fPSA, PSAD, and %fPSA + PSAD. Detection rates of csPCa, defined as Gleason grade (GG) group ≥2, were compared among cohorts. RESULTS: The Pearson correlation coefficient for %fPSA and PSAD was -.349 (p < .001). ROC analysis predicting csPCa yielded an AUC of .712 for PSAD, .638 for %fPSA, and .718 for the combined PSAD+%fPSA model. We noted that there were “outliers” in terms of low %fPSA and low PSAD and vice versa. As such, we analyzed low risk (Cohort 1: %fPSA>20 and PSAD <0.15, N=54) and high risk (Cohort 2: %fPSA<10 and PSAD>0.15, N=79) groups against the restrictive associated “outlier” groups (Cohort 3: %fPSA>20 & PSAD>0.15, N=14 and Cohort 4: %fPSA<10 & PSAD<0.15, N=7). The low-risk cohort showed a csPCa rate of 25.9% while the high-risk cohort showed a rate of 74.7%, a significantly higher rate (p < .001). Cohort 3 showed a significantly higher csPCa rate and average GG group (78.6% and 2.64) than Cohort 1 (25.9% and 1.04, both p < .001). There were no significant differences in csPCa between the two low-%fPSA cohorts (2 and 4), the two high-PSAD cohorts (2 and 3), the two low-PSAD cohorts (1 and 4), or the two outlier cohorts (3 and 4). CONCLUSIONS: This analysis notes an inverse correlation between PSAD and %fPSA and may explain the ability of PSA-based markers utilizing %fPSA to predict cancer risk independent of prostate size. The higher AUC of PSAD compared with %fPSA and the negligible change in diagnostic efficacy when adding %fPSA to a PSAD-based model suggest that PSAD is a more powerful metric than %fPSA for the prediction of csPCa. Source of Funding: none © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e899 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Max Kuster More articles by this author Michelle Ou More articles by this author Jacob Gaines More articles by this author Eric Macdonald More articles by this author Simon Hall More articles by this author Expand All Advertisement PDF DownloadLoading ...