Study of three-dimensional dose distribution based-deep learning in predicting distant metastasis in head and neck cancer
10.3760/cma.j.cn113030-20220812-00275
- VernacularTitle:基于深度学习三维剂量分布的头颈部肿瘤远处转移预测研究
- Author:
Jiajun CAI
1
;
Yongbao LI
;
Fan XIAO
;
Mengke QI
;
Xingyu LU
;
Linghong ZHOU
;
Ting SONG
Author Information
1. 南方医科大学生物医学工程学院，广州 510515
- Keywords:
Head and neck neoplasms;
Distant metastasis;
Three-dimensional dose distribution;
Deep learning
- From:
Chinese Journal of Radiation Oncology
2023;32(5):422-429
- CountryChina
- Language:Chinese
-
Abstract:
Objective: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.
