ESTRO 2023 - Abstract Book

S353

Sunday 14 May 2023

ESTRO 2023

Results Each DL model obtained higher validation (with AUC between 0.79 and 0.82) and test (with AUC between 0.78 and 0.79) performance than the conventional NTCP-model (AUC-validation = 0.75, 95% CI [0.67 – 0.83]; AUC-test = 0.75 [0.67 – 0.84]). This demonstrates the benefit of utilizing 3D information for the prediction of late xerostomia. The ResNet model performed best with validation AUC = 0.82 ([0.73 – 0.89]) and test AUC = 0.79 [0.70 – 0.87]. Hyperparameter tuning showed highest internal validation performances for AdaBound optimizer, learning rate of 0.0001 and batch of 8. Furthermore, attention maps showed that ResNet focused on salivary glands and oral cavity when making its prediction (Figure 2).

Conclusion