Abstract
25% at 5 and 10 years post RP. The GC had a c-index of 0.79 (95% CI 0.71-0.86) for predicting metastasis and this was significantly better than any single clinical variable (c-indices: 0.49 0.65). Decision curve analysis showed that the GC model had higher overall net benefit compared to clinical variables over a wide range of ?decision-to-treat? thresholds for risk of metastasis. In multivariable modeling with clinicopathologic variables, GC remained the only significant independent predictor of metastasis (HR 1.51, for each 0.1 unit increment p 0.001). CONCLUSIONS: Our results suggest that GC can better predict metastatic disease progression compared with clinical variables and may help identify both patients at higher risk to progress and those more likely to have an indolent course. This may enable optimization of multimodal therapy for high risk prostate cancer patients.
Published Version
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