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Abstract
Radiation therapy (RT) is an important and potentially curative modality for head and neck squamous cell carcinoma (HNSCC). Locoregional recurrence (LR) of HNSCC after RT is ranging from 15% to 50% depending on the primary site and stage. In addition, the 5-year survival rate of patients with LR is low. To classify high-risk patients who might develop LR, a deep learning model for predicting LR needs to be established. In this work, 157 patients with HNSCC who underwent RT were analyzed. Based on the National Cancer Institute's multi-institutional TCIA data set containing FDG-PET/CT/dose, a 3D deep learning model was proposed to predict LR without time-consuming segmentation or feature extraction. Our model achieved an averaged area under the curve (AUC) of 0.856. Adding clinical factors into the model improved the AUC to an average of 0.892 with the highest AUC of up to 0.974. The 3D deep learning model could perform individualized risk quantification of LR in patients with HNSCC without time-consuming tumor segmentation.

 
Affiliations

Kyumin Han  1   2 , Joonyoung Francis Joung  3 , Minhi Han  3 , Wonmo Sung  1   4 , Young-Nam Kang  2   5
1Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
2Advanced Institute for Radiation Fusion Medical Technology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
3Department of Chemistry and Research, Institute for Natural Science, Korea University, Seoul 02841, Korea.
4Department of Biomedical Engineering, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.
5Department of Radiation Oncology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea.