bibtext: @InProceedings{ Bae_HoGNet_MICCAI2024,
                   author = { Bae, Joseph and Kapse, Saarthak and Zhou, Lei and Mani, Kartik and Prasanna, Prateek },
                   title = { { HoG-Net: Hierarchical Multi-Organ Graph Network for Head and Neck Cancer Recurrence Prediction from CT Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: In many diseases 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.
title: HoG-Net: Hierarchical Multi-Organ Graph Network for Head and Neck Cancer Recurrence Prediction from CT Images
authors: Bae, Joseph Kapse, Saarthak Zhou, Lei Mani, Kartik Prasanna, Prateek