Locoregional Recurrence Prediction Using a Deep Neural Network of Radiological and Radiotherapy Images

Journal of Personalized Medicine

Young-nam Kang¹  Kyumin Han²  Wonmo Sung²  Minhi Han³  Joonyoung Francis Joung³ 

[1] Advanced Institute for Radiation Fusion Medical Technology, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea;Department of Chemistry and Research, Institute for Natural Science, Korea University, Seoul 02841, Korea;

关键词: head and neck squamous cell carcinoma;    deep learning;    locoregional recurrence;   

DOI  :  10.3390/jpm12020143

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown