DoseGNN: Improving the Performance of Deep Learning Models in Adaptive
  Dose-Volume Histogram Prediction through Graph Neural Networks
Zehao Dong Yixin Chen Tianyu Zhao
Dose-Volume Histogram (DVH) prediction is fundamental in radiation therapy
that facilitate treatment planning, dose evaluation, plan comparison and etc.
It helps to increase the ability to deliver precise and effective radiation
treatments while managing potential toxicities to healthy tissues as needed to
reduce the risk of complications. This paper extends recently disclosed
research findings presented on AAPM (AAPM 65th Annual Meeting $\&$ Exhibition)
and includes necessary technique details. The objective is to design efficient
deep learning models for DVH prediction on general radiotherapy platform
equipped with high performance CBCT system, where input CT images and target
dose images to predict may have different origins, spacing and sizes. Deep
learning models widely-adopted in DVH prediction task are evaluated on the
novel radiotherapy platform, and graph neural networks (GNNs) are shown to be
the ideal architecture to construct a plug-and-play framework to improve
predictive performance of base deep learning models in the adaptive setting.