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Prediction of the Pathologic Gleason Score to Inform a Personalized Management Program for Prostate Cancer

      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.

      Keywords

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      References

        • Dall’Era M.A.
        • Albertsen P.C.
        • Bangma C.
        • et al.
        Active surveillance for prostate cancer: a systematic review of the literature.
        Eur Urol. 2012; 62: 976-983
        • Chen R.C.
        • Rumble R.B.
        • Loblaw D.A.
        • et al.
        Active surveillance for the management of localized prostate cancer (Cancer Care Ontario Guideline): American Society of Clinical Oncology clinical practice guideline endorsement.
        J Clin Oncol. 2016; 34: 2182-2190
        • Womble P.R.
        • Montie J.E.
        • Ye Z.
        • et al.
        Contemporary use of initial active surveillance among men in Michigan with low-risk prostate cancer.
        Eur Urol. 2015; 67: 44-50
        • Tosoian J.J.
        • Mamawala M.
        • Epstein J.I.
        • et al.
        Intermediate and longer-term outcomes from a prospective active-surveillance program for favorable-risk prostate cancer.
        J Clin Oncol. 2015; 33: 3379-3385
        • Popiolek M.
        • Rider J.R.
        • Andren O.
        • et al.
        Natural history of early, localized prostate cancer: a final report from three decades of follow-up.
        Eur Urol. 2013; 63: 428-435
        • Ankerst D.P.
        • Xia J.
        • Thompson Jr., I.M.
        • et al.
        Precision medicine in active surveillance for prostate cancer: development of the Canary-Early Detection Research Network Active Surveillance Biopsy Risk Calculator.
        Eur Urol. 2015; 68: 1083-1088
        • Kattan M.W.
        • Eastham J.A.
        • Wheeler T.M.
        • et al.
        Counseling men with prostate cancer: a nomogram for predicting the presence of small, moderately differentiated, confined tumors.
        J Urol. 2003; 170: 1792-1797
        • Steyerberg E.W.
        • Roobol M.J.
        • Kattan M.W.
        • van der Kwast T.H.
        • de Koning H.J.
        • Schroder F.H.
        Prediction of indolent prostate cancer: validation and updating of a prognostic nomogram.
        J Urol. 2007; 177 (discussion 112): 107-112
        • Nakanishi H.
        • Wang X.
        • Ochiai A.
        • et al.
        A nomogram for predicting low-volume/low-grade prostate cancer: a tool in selecting patients for active surveillance.
        Cancer. 2007; 110: 2441-2447
        • Tosoian J.J.
        • Sundi D.
        • Trock B.J.
        • et al.
        Pathologic outcomes in favorable-risk prostate cancer: comparative analysis of men electing active surveillance and immediate surgery.
        Eur Urol. 2016; 69: 576-581
        • Epstein J.I.
        • Walsh P.C.
        • Carmichael M.
        • Brendler C.B.
        Pathologic and clinical findings to predict tumor extent of nonpalpable (stage T1c) prostate cancer.
        JAMA. 1994; 271: 368-374
        • Eggener S.E.
        • Scardino P.T.
        • Walsh P.C.
        • et al.
        Predicting 15-year prostate cancer specific mortality after radical prostatectomy.
        J Urol. 2011; 185: 869-875
        • Pierorazio P.M.
        • Walsh P.C.
        • Partin A.W.
        • Epstein J.I.
        Prognostic Gleason grade grouping: data based on the modified Gleason scoring system.
        BJU Int. 2013; 111: 753-760
        • Song X.
        • Davidian M.
        • Tsiatis A.A.
        A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data.
        Biometrics. 2002; 58: 742-753
      1. Plummer M. JAGS version 4.0.0 user manual. SourceForge Web site. https://sourceforge.net/projects/mcmc-jags/files/Manuals/4.x/jags_user_manual.pdf.

        • Agresti A.
        Categorical Data Analysis.
        ed 3. Wiley, Hoboken, NJ2012
        • Steyerberg E.W.
        • Vickers A.J.
        • Cook N.R.
        • et al.
        Assessing the performance of prediction models: a framework for traditional and novel measures.
        Epidemiology. 2010; 21: 128-138
        • Hanley J.A.
        • McNeil B.J.
        The meaning and use of the area under a receiver operating characteristic (ROC) curve.
        Radiology. 1982; 143: 29-36
        • Alam R.
        • Carter H.B.
        • Landis P.
        • Epstein J.I.
        • Mamawala M.
        Conditional probability of reclassification in an active surveillance program for prostate cancer.
        J Urol. 2015; 193: 1950-1955
        • Laird N.M.
        • Ware J.H.
        Random-effects models for longitudinal data.
        Biometrics. 1982; 38: 963-974
        • Akaike H.
        Information theory and an extension of the maximum likelihood principle.
        in: Parzen E. Tanabe K. Kitagawa G. Selected Papers of Hirotugu Akaike. Springer Series in Statistics: Perspectives in Statistics. Springer, New York, NY1998: 199-213