TY - GEN
T1 - HoG-Net
T2 - 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024
AU - Bae, Joseph
AU - Kapse, Saarthak
AU - Zhou, Lei
AU - Mani, Kartik
AU - Prasanna, Prateek
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Graph Neural Networks
KW - Head and Neck Cancer
KW - Tumor Recurrence
UR - https://www.scopus.com/pages/publications/85206576600
U2 - 10.1007/978-3-031-72086-4_30
DO - 10.1007/978-3-031-72086-4_30
M3 - Conference contribution
AN - SCOPUS:85206576600
SN - 9783031720857
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 317
EP - 327
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 - 27th International Conference, Proceedings
A2 - Linguraru, Marius George
A2 - Dou, Qi
A2 - Feragen, Aasa
A2 - Giannarou, Stamatia
A2 - Glocker, Ben
A2 - Lekadir, Karim
A2 - Schnabel, Julia A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2024 through 10 October 2024
ER -