Clinical application
tumor, GS
TUMOR
DCA is a new algorithm to calculate the net gain of the model at different thresholds. We used DCA to examine the potential clinical value of the nomogram predictive model. We also calculated the risk for each patient from the nomogram. Using the subject operating characteristic curve (ROC) as the cut-off value, all patients were divided into high-risk and low-risk groups. Differences in survival between patients at high-risk and low-risk groups were examined using the Log-rank test and Kaplan–Meier (K–M) curves. In addition, we analyzed differences of RT and chemotherapy in high-risk and low-risk groups. When analyzing the difference in chemotherapy between the high risk and low risk groups, we also performed a 1:1 propensity matching (PSM), which made the comparison cohort more reliable. The propensity-matched cohorts were counterbalanced by age, race, marriage, year of diagnosis, tumor grade, TNM stage, RT, PSA, and GS.