Impact of Prostate Health Index Results for Prediction of Biopsy Grade Reclassification During Active Surveillance

• Type of Material: Article
• Authors: Christopher Filson, Emory University; Kehao Zhu, Fred Hutchinson Cancer Research Center; Yijian Huang, Emory University; Yingye Zheng, Fred Hutchinson Cancer Research Center; Lisa F. Newcomb, University of Washington; Sierra Williams, Emory University; James D. Brooks, Stanford University; Peter R. Carroll, University of California, San Francisco; Atreya Dash, VA Puget Sound Health Care Systems; William J. Ellis, University of Washington; Martin E. Cleave, University of British Columbia; Michael Liss, University of Texas, San Antonio; Frances Martin, Eastern Virginia Medical School; Jesse K. McKenney, Robert J. Tomsich Pathology and Laboratory Medicine Institute; Todd M. Morgan, University of Michigan; Andrew A. Wagner, Beth Israel Deaconess Medical Center; Lori J. Sokoll, Johns Hopkins University; Martin Sanda, Emory University; Daniel W. Chan, Johns Hopkins University; Daniel W. Lin, University of Washington
• Language: English
• Date: 2022-07-05
• Publisher: American Urological Association Education and Research, Inc.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2022 by American Urological Association Education and Research, Inc.
• Final Published Version (URL): https://doi.org/10.1097/JU.0000000000002852
• Title of Journal or Parent Work: The Journal of Urology
• Volume: 208
• Issue: 5
• Start Page: 1037
• End Page: 1045
• Grant/Funding Information: NIH/NCI Early Detection Research Network, U24 CA115102 (DWC).; PHI assay reagents were provided by Beckman Coulter, Inc; NIH U01 CA113913 (YH, LN, MGS and DWL); NIH U01 CA224255 (DL); American Cancer Society MRSG 18-015-01-CPHPS (CPF)
• Keywords: biomarker; active surveillance; prostate cancer
• Research Categories: Health Sciences, Oncology

Abstract

Objective: We assessed whether Prostate Health Index (phi) results improve prediction of grade reclassification for men on active surveillance. Methods/Materials: We identified men in Canary Prostate Active Surveillance Study with Grade Group (GG) 1 cancer. Outcome was grade reclassification to GG2+ cancer. We considered decision rules to maximize specificity with sensitivity set at 95%. We derived rules based on clinical data (R1) vs clinical data + phi (R3). We considered an “or”-logic rule combining clinical score and phi (R4), and a “two-step” rule using clinical data followed by risk stratification based on phi (R2). Rules were applied to a validation set, where values of R2 - R4 vs R1 for specificity and sensitivity were evaluated. Results: We included 1532 biopsies (n=610 discovery; n=922 validation) among 1142 men. Grade reclassification was seen in 27% of biopsies (23% discovery, 29% validation). Among discovery set, at 95% sensitivity, R2 yielded highest specificity at 27% vs 17% for R1. In validation set, R3 had best performance vs R1 with Δsensitivity = −4% and Δspecificity = +6%. 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) but not for subsequent biopsies (ΔAUC = −0.012, 95%CI −0.031–0.006). R3 did not have better discrimination vs R1 among the biopsy cohort overall (ΔAUC = 0.007, 95%CI −0.007–0.020). Conclusions: Among active surveillance patients, using phi with clinical data modestly improved prediction of grade reclassification on confirmatory biopsy and did not improve prediction on subsequent biopsies.