3.3 Prediction of OS
The radiomic model performed best in predicting OS
[balanced accuracies: 78.69%
and 85.75%; AUC (95% CI): 0.82 (0.69-0.94) and 0.92 (0.82-1) in
training and validation sets]. Additionally, the balanced accuracies
of only using the tumor volume to predict OS were 50% and 50% in the
training and validation sets.
The scores predicted by the models were used to split patients into the
high-risk and low-risk groups using the threshold value defined by the
ROC curve of the training set. The
radiomic scores can significantly stratify patients into the high-risk
and low-risk groups (P = 7.16x10-10), while clinical
score cannot (P = 0.29) (Figure 3) [34].
The clinical model only showed limited predictive power for OS, and
combining clinicopathological parameters with radiomic features didn’t
improve the performance (Table 3 and Figure 2b).
3.3 Prediction ofhypohemoglobin
We identified 9 clinicopathological parameters and 7 pelvis radiomic
features as best predictors of hypohemoglobin [balanced accuracies:
62.42% and 70.96%; AUC (95% CI): 0.65 (0.57-0.73) and 0.74
(0.62-0.87) in training and validation sets]. The clinical model
outperformed radiomic model in predicting hypohemoglobin (Table 3 and
Figure 2c).
3. 4
Prediction of severeleucopenia
The clinical model and the radiomic model alone only showed limited
predictive power for severe leucopenia [balanced accuracies: 56.42%
and 55.08%; AUC (95% CI): 0.56 (0.41-0.71) and 0.57 (0.43-0.72) in the
validation set]. Combining radiomic features with clinicopathological
parameters improved the prediction performance [balanced accuracy:
69.93%; AUC (95% CI): 0.64 (0.48-0.79) in validation set] (Table 3
and Figure 2d).
Figure 4 shows boxplots and data distribution of the predicted scores
for four clinical endpoints.
We repeated the whole process using data of 159 patients treated with
the same therapy. Similar performance was observed in the prediction of
four clinical endpoints (Table S3 and Figure S2-S4).
The wide and overlapping 95% CIs
cannot represent statistical differences between two models.