Combining CT Images and Clinical Features of Four Periods to Predict Whether Patients Have Rectal Cancer
Table 4
The predictive performance of the prediction models using the training set.
Variables
Clinical demographics
Radiomic
Total model
Cut off
0.426
0.265
0.557
Sensitivity (95% CI)
0.910 (0.842–0.979)
0.970 (0.929–1.000)
0.970 (0.929–1.000)
Specificity (95% CI)
0.884 (0.828–0.939)
0.907 (0.857–0.957)
1.000 (1.000–1.000)
PPV (95% CI)
0.803 (0.713–0.892)
0.844 (0.763–0.925)
1.000 (1.000–1.000)
NPV (95% CI)
0.950 (0.911–0.989)
0.983 (0.960–1.000)
0.985 (0.964–1.000)
AUC (95% CI)
0.938 (0.903–0.972)
0.980 (0.966–0.994)
0.999 (0.996–1.000)
Accuracy (95% CI)
0.893 (0.850–0.936)
0.929 (0.893–0.965)
0.990 (0.976–1.000)
Compared with the total model, the difference is statistically significant. CI: confidence interval; PPV: positive predictive value; NPV: negative predictive value; AUC: area under the curve.