R Yates Coley1, Scott L Zeger1, Mufaddal Mamawala2, Kenneth J Pienta2, H Ballentine Carter3. 1. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. 2. The Johns Hopkins University School of Medicine, The James Brady Urological Institute, Johns Hopkins Hospital, Baltimore, MD, USA. 3. The Johns Hopkins University School of Medicine, The James Brady Urological Institute, Johns Hopkins Hospital, Baltimore, MD, USA. Electronic address: hcarter@jhmi.edu.
Abstract
BACKGROUND: Active surveillance (AS) is an alternative to curative intervention, but overtreatment persists. Imperfect alignment of prostate biopsy and Gleason score after radical prostatectomy (RP) may be a contributing factor. OBJECTIVE: To develop a statistical model that predicts the post-RP Gleason score (pathologic Gleason score [PGS]) using clinical observations made in the course of AS. DESIGN, SETTING, AND PARTICIPANTS: Repeated prostate-specific antigen measurements and biopsy Gleason scores from 964 very low-risk patients in the Johns Hopkins Active Surveillance cohort were used in the analysis. PGS observations from 191 patients who underwent RP were also included. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A Bayesian joint model based on accumulated clinical data was used to predict PGS in these categories: 6 (grade group 1), 3+4 (grade group 2), 4+3 (grade group 3), and 8-10 (grade groups 4 and 5). The area under the receiver operating characteristic curve (AUC) and calibration of predictions was assessed in patients with post-RP Gleason score observations. RESULTS AND LIMITATIONS: The estimated probability of harboring a PGS >6 was <20% for most patients who had not experienced grade reclassification or elected surgery. Among patients with post-RP Gleason score observations, the AUC for predictions of PGS >6 was 0.74 (95% confidence interval, 0.66-0.81), and the mean absolute error was 0.022. CONCLUSIONS: Although the model requires external validation prior to adoption, PGS predictions can be used in AS to inform decisions regarding follow-up biopsies and remaining on AS. Predictions can be updated as additional data are observed. The joint modeling framework also accommodates novel biomarkers as they are identified and measured on AS patients. PATIENT SUMMARY: Measurements taken in the course of active surveillance can be used to accurately predict patients' underlying prostate cancer status. Predictions can be communicated to patients via a decision support tool and used to guide clinical decision making and reduce patient anxiety.
BACKGROUND: Active surveillance (AS) is an alternative to curative intervention, but overtreatment persists. Imperfect alignment of prostate biopsy and Gleason score after radical prostatectomy (RP) may be a contributing factor. OBJECTIVE: To develop a statistical model that predicts the post-RP Gleason score (pathologic Gleason score [PGS]) using clinical observations made in the course of AS. DESIGN, SETTING, AND PARTICIPANTS: Repeated prostate-specific antigen measurements and biopsy Gleason scores from 964 very low-risk patients in the Johns Hopkins Active Surveillance cohort were used in the analysis. PGS observations from 191 patients who underwent RP were also included. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: A Bayesian joint model based on accumulated clinical data was used to predict PGS in these categories: 6 (grade group 1), 3+4 (grade group 2), 4+3 (grade group 3), and 8-10 (grade groups 4 and 5). The area under the receiver operating characteristic curve (AUC) and calibration of predictions was assessed in patients with post-RP Gleason score observations. RESULTS AND LIMITATIONS: The estimated probability of harboring a PGS >6 was <20% for most patients who had not experienced grade reclassification or elected surgery. Among patients with post-RP Gleason score observations, the AUC for predictions of PGS >6 was 0.74 (95% confidence interval, 0.66-0.81), and the mean absolute error was 0.022. CONCLUSIONS: Although the model requires external validation prior to adoption, PGS predictions can be used in AS to inform decisions regarding follow-up biopsies and remaining on AS. Predictions can be updated as additional data are observed. The joint modeling framework also accommodates novel biomarkers as they are identified and measured on AS patients. PATIENT SUMMARY: Measurements taken in the course of active surveillance can be used to accurately predict patients' underlying prostate cancer status. Predictions can be communicated to patients via a decision support tool and used to guide clinical decision making and reduce patientanxiety.