Purpose To develop a radiomic graph framework, RadGraph, for spatial analysis of pretreatment CT images to improve prediction of local-regional recurrence (LR) and distant metastasis (DM) in head and neck squamous cell carcinoma (HNSCC). Materials and Methods This retrospective study included four public pre-radiotherapy treatment CT datasets of patients with HNSCC obtained from The Cancer Imaging Archive (images collected between 2003 and 2018). Computational graphs and graph attention deep learning methods were leveraged to holistically model multiple regions in the head and neck anatomy. Clinical features, including age, sex, and human papillomavirus infection status, were collected for a baseline model. Model performance in predicting LR and DM was evaluated via area under the receiver operating characteristic curve (AUC) and qualitative interpretation of model attention. Results A total of 3434 patients (61 years ± 11 [SD], 2774 male) were divided into training (