Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients
Table 3
Results of the ROC analysis for nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, PSA in the moment of disease reevaluation, and relevant events.
Characteristics
Nadir PSA
Time to nadir PSA
PSA doubling time
PSA velocity
PSA in the moment of disease reevaluation
AUC ROCa (95% CI)
64.3% (51.7-76.9%)
74.2% (62.6-85.8%)
87.2% (78.8-95.5%)
91.8% (86.0-97.6%)
97.1% (93.3-100%)
Likelihood ratio testb
ROC cut-off valuec
0.3095
6.5
5.05
0.55
6.49
Sensitivity (95% CI)
63.0% (50.0-75.9%)
66.7% (53.7-77.8%)
79.6% (68.5-90.7%)
72.2% (59.2-83.3%)
94.4% (89.0-100%)
Specificity (95% CI)
68.0% (48.0-84.0%)
80.0% (64.0-96.0%)
84.0% (68.0-96.0%)
92.0% (80.0-100%)
92.0% (80.0-100%)
aArea under the ROC curve (DeLong’s method); bLikelihood ratio test for AUC ROC; cValue with maximum sensitivity and specificity.
