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External validation of a biomarker-based preoperative nomogram predicts biochemical recurrence after radical
Shahrokh F Shariat1, Jochen Walz, Claus G Roehrborn
1Cancer Prognostics and Health Outcomes Unit, University of Montreal, Montreal, Quebec, Canada. shahrokh.shariat@utsouthwestern.edu
Purpose:
Biomarker signatures currently are used in several malignancies to guide clinical decision making. Recently, preoperative plasma levels of transforming growth factor-beta1 (TGF-beta1) and interleukin-6 soluble receptor (IL6-SR) have improved the accuracy of a clinical nomogram that predicted biochemical recurrence after radical prostatectomy. However, this model was never externally validated. We tested the accuracy of this nomogram in an independent, external cohort.
Patients And Methods:
Preoperative plasma levels of TGF-beta1 and IL6-SR were measured in 423 consecutive men who underwent radical prostatectomy and bilateral lymphadenectomy and were used, along with preoperative prostate-specific antigen levels, biopsy Gleason sum, and clinical stage to determine nomogram-derived probabilities of biochemical recurrence-free survival at 5 years after radical prostatectomy. The accuracy of predictions was quantified with the area under the curve (AUC) and calibration plots that graphically displayed the nomogram's performance characteristics. The statistical significance of the difference between the biomarker nomogram and a model designed on the basis of clinical variables alone was tested by using the Mantel-Haenszel statistic.
Results:
Biochemical recurrence-free survival at 5 years was 77.0% (95% CI, 72.0% to 82.0%). The biomarker-based nomogram was 87.9% accurate versus 71.1% for the nomogram designed on the basis of clinical variables alone (16.8% difference; P < .001). The performance characteristics of the biomarker-based nomogram were superior to those of the clinical nomogram.
Conclusion:
We confirm that plasma levels of TGF-beta1 and IL6-SR considerably enhance the accuracy of the standard preoperative nomogram for the prediction of biochemical recurrence after radical prostatectomy. This model further refines our ability to identify patients at a high risk of biochemical recurrence after radical prostatectomy.
