HoG-Net: Hierarchical Multi-organ Graph Network for Head and Neck Cancer Recurrence Prediction from CT Images

In many cancers including head and neck squamous cell carcinoma (HNSCC), pathologic processes are not limited to a single region of interest, but instead encompass surrounding anatomical structures and organs outside of the tumor. To model information from organs-at-risk (OARs) as well as from the primary tumor, we present a Hierarchical Multi-Organ Graph Network (HoG-Net) for medical image modeling which we leverage to predict locoregional tumor recurrence (LR) for HNSCC patients. HoG-Net is able to model local features from individual OARs and then constructs a holistic global representation of interactions between features from multiple OARs in a single image. HoG-Net’s prediction of LR for HNSCC patients is evaluated in a largest yet studied dataset of N = 2,741 patients from six institutions, and outperforms several previously published baselines. Further, HoG-Net allows insights into which OARs are significant in predicting LR, providing specific OAR-level interpretability rather than the coarse patch-level interpretability provided by other methods. Code can be found at https://github.com/bmi-imaginelab/HoGNet .