  Early identification of head and neck cancer (HNC) patients who would
experience significant anatomical change during radiotherapy (RT) is important
to optimize patient clinical benefit and treatment resources. This study aims
to assess the feasibility of using a vision-transformer (ViT) based neural
network to predict RT-induced anatomic change in HNC patients. We
retrospectively included 121 HNC patients treated with definitive RT/CRT. We
collected the planning CT (pCT), planned dose, CBCTs acquired at the initial
treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp)
and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model
construction and evaluation. A UNet-style ViT network was designed to learn
spatial correspondence and contextual information from embedded CT, dose,
CBCT01, GTVp, and GTVn image patches. The model estimated the deformation
vector field between CBCT01 and CBCT21 as the prediction of anatomic change,
and deformed CBCT01 was used as the prediction of CBCT21. We also generated
binary masks of GTVp, GTVn, and patient body for volumetric change evaluation.
The predicted image from the proposed method yielded the best similarity to the
real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison
models. The average MSE and SSIM between the normalized predicted CBCT to
CBCT21 are 0.009 and 0.933, while the average dice coefficient between body
mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively. The
proposed method showed promising performance for predicting
radiotherapy-induced anatomic change, which has the potential to assist in the
decision-making of HNC Adaptive RT.
