3.2 Prediction of CR
A combination of 10 clinicopathological parameters and 34 CT radiomic
features performed best in predicting CR [balanced accuracies: 79.73%
and 80.79%; AUC (95% confidence interval (CI)): 0.88 (0.82-0.93) and
0.87 (0.77-0.96) in the training and validation sets]. The CT radiomic
model outperformed clinicopathological model in predicting CR (Table 3).
Figure 2a shows the ROC curves
of three models for
classifying the CR and non-CR
groups. Additionally, only using the tumor volume to predict CR achieved
balanced accuracies of 64.92% and 72.87% in the training and
validation sets.
The CT radiomic model outperformed MRI radiomic model for predicting CR
(balanced accuracy: 74.51% and 52.46% in the validation set), and the
addition of MRI radiomic features decreased the performance of the
clinical and radiomic model (Table S2 and Figure S1).