Seventy‐First Annual Scientific Meeting of Canadian Organization of Medical Physicists, RBC Place London, London, Ontario, June 5–7, 2025

Purpose: Head and neck cancer (HNC) patients undergoing radiotherapy often experience significant anatomical changes that necessitate replanning.However, replanning is resource-intensive and decided on short notice.Therefore, we aim to predict the progression of anatomical changes throughout radiotherapy so that replanning can be anticipated in advance. Methods:We trained a variational autoencoder (VAE) to learn condensed latent vectors of CBCT scans using our in-house dataset of 420 HNC patients and 5323 CBCT scans.Subsequently, we developed a model to predict changes in these latent-space vectors over time based on the initial CBCT and clinical features (eg.staging, chemotherapy, etc.).Points along the predicted latent trajectory were decoded by the VAE to reconstruct synthetic future CBCT images.We evaluated the model by calculating the Dice score between actual and predicted body masks for each fraction.Additionally, we assessed the percent change of the area between the first and subsequent fractions in true versus predicted images, a useful replanning metric for determining shrinkage.Results: Initially, we trained our models on binary masks of single image slices, rather than the full CBCT.Our model achieved an average test set Dice score of 0.94 and area error of 5.9% for all fractions.The area error decreases as more CBCTs are incorporated throughout treatment.We are expanding our model to predict 3D anatomical changes and implementing a replanning flagging system based on the expected changes. Conclusion:In a DIBH treatment, 3D CRT techniques is dosimetrically advantageous and more forgiving to tolerance over VMAT.