Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning

Purpose: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques.

Methods: The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head-and-neck patients for training and validation, respectively. The final model is a U-Net with additional ResNet blocks between up- and down convolutions. The results were obtained by training the model with AdamW with the One Cycle scheduler. The loss function is a combination of the L1 loss with a feature loss, which uses a pretrained video classifier as a feature extractor. The performance was evaluated on another 100 patients in the OpenKBP test dataset. The DVH metrics of the test data were evaluated, where $D_{0.1cc}$ , and $D_{\mathrm{mean}}$ were calculated for the organs at risk (OARs) and $D_{1\%}$ , $D_{95\%}$ , and $D_{99\%}$ were computed for the target structures. DVH metric differences between predicted and true dose are reported in percentage.

Results: The model achieved 2nd and 4th place in the DVH and dose stream of the OpenKBP challenge, respectively. The dose and DVH score were 2.62 ± 1.10 and 1.52 ± 1.06, respectively. Mean dose differences for the different structures and DVH parameters were within ±1%.

Conclusion: This straightforward approach produced excellent results. It incorporated One Cycle Learning, ResNet, and feature-based losses, which are common computer vision techniques.