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Study of three-dimensional dose distribution based-deep learning in predicting distant metastasis in head and neck cancer
Cai Jiajun1, Li Yongbao2, Xiao Fan1, Qi Mengke1, Lu Xingyu1, Zhou Linghong1, Song Ting1
1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; 2State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
AbstractObjective To investigate the role of three-dimensional dose distribution-based deep learning model in predicting distant metastasis of head and neck cancer. Methods Radiotherapy and clinical follow-up data of 237 patients with head and neck cancer undergoing intensity-modulated radiotherapy (IMRT) from 4 different institutions were collected. Among them, 131 patients from HGJ and CHUS institutions were used as the training set, 65 patients from CHUM institution as the validation set, and 41 patients from HMR institution as the test set. Three-dimensional dose distribution and GTV contours of 131 patients in the training set were input into the DM-DOSE model for training and then validated with validation set data. Finally, the independent test set data were used for evaluation. The evaluation content included the area under receiver operating characteristic curve (AUC), balanced accuracy, sensitivity, specificity, concordance index and Kaplan-Meier survival curve analysis. Results In terms of prognostic prediction of distant metastasis of head and neck cancer, the DM-DOSE model based on three-dimensional dose distribution and GTV contours achieved the optimal prognostic prediction performance, with an AUC of 0.924, and could significantly distinguish patients with high and low risk of distant metastasis (log-rank test, P<0.001). Conclusion Three-dimensional dose distribution has good predictive value for distant metastasis in head and neck cancer patients treated with IMRT, and the constructed prediction model can effectively predict distant metastasis.
Fund:Guangdong Basic and Applied Basic Research Foundation, China (2021A1515012044, 2022A1515010639); Guangzhou Science and Technology Foundation, China(202102020968)
Corresponding Authors:
Song Ting, Email: tingsong2015@smu.edu.cn; Li Yongbao, Email: liyb1@sysucc.org.cn
Cite this article:
Cai Jiajun,Li Yongbao,Xiao Fan et al. Study of three-dimensional dose distribution based-deep learning in predicting distant metastasis in head and neck cancer[J]. Chinese Journal of Radiation Oncology, 2023, 32(5): 422-429.
Cai Jiajun,Li Yongbao,Xiao Fan et al. Study of three-dimensional dose distribution based-deep learning in predicting distant metastasis in head and neck cancer[J]. Chinese Journal of Radiation Oncology, 2023, 32(5): 422-429.
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