MP62-10 PROSTATE HEALTH INDEX IN PREDICTING GRADE RECLASSIFICATION ON BIOPSY FOR MEN ON ACTIVE SURVEILLANCE (2021)

You have accessJournal of UrologyProstate Cancer: Localized: Active Surveillance II (MP62)1 Sep 2021MP62-10 PROSTATE HEALTH INDEX IN PREDICTING GRADE RECLASSIFICATION ON BIOPSY FOR MEN ON ACTIVE SURVEILLANCE Christopher Filson, Lori Sokoll, Yingye Zheng, Yijian Huang, Lisa Newcomb, Kehao Zhu, Sierra Williams, Martin Sanda, Daniel Chan, and Daniel Lin Christopher FilsonChristopher Filson More articles by this author , Lori SokollLori Sokoll More articles by this author , Yingye ZhengYingye Zheng More articles by this author , Yijian HuangYijian Huang More articles by this author , Lisa NewcombLisa Newcomb More articles by this author , Kehao ZhuKehao Zhu More articles by this author , Sierra WilliamsSierra Williams More articles by this author , Martin SandaMartin Sanda More articles by this author , Daniel ChanDaniel Chan More articles by this author , and Daniel LinDaniel Lin More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002102.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Risk calculators using clinical data enhance prediction of biopsy grade reclassification for active surveillance patients. We assessed whether using Prostate Health Index (PHI) results could improve discrimination by developing prediction rules using clinical data and PHI. METHODS: We identified men in Canary Prostate Active Surveillance Study (PASS) cohort with Grade Group (GG1) cancer at enrollment and underwent repeat biopsy. Serum PHI was measured from pre-biopsy specimens. Outcome was biopsy grade reclassification to GG2+ cancer. With a discovery set, we adopted linear combinations of clinical data (R1) and clinical data + PHI (R3) via logistic regression and logic combinations of clinical data + PHI (R2, R4) to derive prediction rules attaining highest specificity with threshold set at sensitivity of 95% (Table). Rules were applied to a validation set, where incremental values of R2-R4 vs R1 for specificity (Δspec) and sensitivity (Δsens) and area under the Receiver Operating Characteristic (AUC of ROC) curves were evaluated. RESULTS: We included 1532 biopsies (n=610 discovery; n=922 validation) from 1142 men (median f/u 54 months (IQR 26–97) until treatment/last contact). Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among discovery set, at 95% sensitivity, R2 yielded highest cross-validated specificity at 27% vs specificity of 17% for R1. In the validation set, R1 yielded specificity of 15% (95% CI 11–18%) with increased sensitivity of 97% (95% CI 95–99%), whereas R2 had decreased sensitivity (Δsens = -7%, 95% CI -1–-4%) and Δspec = 14% (95% CI 10–18%). The prediction rule with most consistent sensitivity/specificity between discovery/validation sets was R3. Among the validation set, R3 vs R1 had Δsens = -4% (95% CI -6–2%); Δspec = 6% (95% CI 4–8%). R3 did not have better discrimination vs R1 overall (AUC 0.727 vs R1 0.720, ΔAUC = 0.007, 95%CI -0.007–0.020), nor for subsequent biopsies (AUC 0.681 vs R1 0.693, ΔAUC=-0.012, 95%CI -0.031–0.006). There was slight improvement for R3 vs R1 for confirmatory biopsy (AUC 0.745 vs R1 0.724, ΔAUC=0.021, 95%CI 0.002–0.041). CONCLUSIONS: Combining PHI with validated clinical data enhanced prediction of confirmatory biopsy outcome modestly but did not improve predicting reclassification on repeat biopsy overall. Source of Funding: NIH, ACS © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1095-e1096 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Christopher Filson More articles by this author Lori Sokoll More articles by this author Yingye Zheng More articles by this author Yijian Huang More articles by this author Lisa Newcomb More articles by this author Kehao Zhu More articles by this author Sierra Williams More articles by this author Martin Sanda More articles by this author Daniel Chan More articles by this author Daniel Lin More articles by this author Expand All Advertisement PDF downloadLoading ...