Predicting Prostate Cancer Recurrence After Radical Prostatectomy.

Autor: Jeffers A; Precision Health Economics, Austin, Texas., Sochat V; Department of Biomedical Data Sciences, Stanford University School of Medicine, Stanford, California., Kattan MW; Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio., Yu C; Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, Ohio., Melcon E; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California., Yamoah K; Department of Urology, University of Pennsylvania, Philadelphia, Pennsylvania., Rebbeck TR; Department of Epidemiology, Harvard University School of Public Health, Boston, Massachusetts., Whittemore AS; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California.
Jazyk: angličtina
Zdroj: The Prostate [Prostate] 2017 Feb; Vol. 77 (3), pp. 291-298. Date of Electronic Publication: 2016 Oct 24.
DOI: 10.1002/pros.23268
Abstrakt: Background: Prostate cancer prognosis is variable, and management decisions involve balancing patients' risks of recurrence and recurrence-free death. Moreover, the roles of body mass index (BMI) and race in risk of recurrence are controversial [1,2]. To address these issues, we developed and cross-validated RAPS (Risks After Prostate Surgery), a personal prediction model for biochemical recurrence (BCR) within 10 years of radical prostatectomy (RP) that includes BMI and race as possible predictors, and recurrence-free death as a competing risk.
Methods: RAPS uses a patient's risk factors at surgery to assign him a recurrence probability based on statistical learning methods applied to a cohort of 1,276 patients undergoing RP at the University of Pennsylvania. We compared the performance of RAPS to that of an existing model with respect to calibration (by comparing observed and predicted outcomes), and discrimination (using the area under the receiver operating characteristic curve (AUC)).
Results: RAPS' cross-validated BCR predictions provided better calibration than those of an existing model that underestimated patients' risks. Discrimination was similar for the two models, with BCR AUCs of 0.793, 95% confidence interval (0.766-0.820) for RAPS, and 0.780 (0.745-0.815) for the existing model. RAPS' most important BCR predictors were tumor grade, preoperative prostate-specific antigen (PSA) level and BMI; race was less important [3]. RAPS' predictions can be obtained online at https://predict.shinyapps.io/raps.
Conclusion: RAPS' cross-validated BCR predictions were better calibrated than those of an existing model, and BMI information contributed substantially to these predictions. RAPS predictions for recurrence-free death were limited by lack of co-morbidity data; however the model provides a simple framework for extension to include such data. Its use and extension should facilitate decision strategies for post-RP prostate cancer management. Prostate 77:291-298, 2017. © 2016 Wiley Periodicals, Inc.
(© 2016 Wiley Periodicals, Inc.)
Databáze: MEDLINE
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