Prediction of anatomical images during radiotherapy of nasopharyngeal carcinoma with deep learning method
10.3760/cma.j.cn113030-20230308-00042
- VernacularTitle:利用深度学习方法预测鼻咽癌患者放疗疗程中的解剖图像
- Author:
Bining YANG
1
;
Yuxiang LIU
;
Guoliang ZHANG
;
Kuo MEN
;
Jianrong DAI
Author Information
1. 国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院放疗科，北京 100021
- Keywords:
Nasopharyngeal neoplasms;
Anatomical changes;
Artificial intelligence;
Deep learning;
Radiotherapy, adaptive
- From:
Chinese Journal of Radiation Oncology
2024;33(4):333-338
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a deep learning method to predict the anatomical images of nasopharyngeal carcinoma patients during the treatment course, which could detect the anatomical variation for specific patients in advance.Methods:Imaging data including planning CT (pCT) and cone-beam CT (CBCT) for each fraction of 230 patients with T 3-T 4 staging nasopharyngeal carcinoma who treated in Cancer Hospital Chinese Academy of Medical Sciences from January 1, 2020 to December 31, 2022 were collected. The anatomical images of week k+1 were predicted using a 3D Unet model with inputs of pCT, CBCT on days 1-3, and CBCT of weeks 2- k. In this experiment, we trained four models to predict anatomical images of weeks 3-6, respectively. The nasopharynx gross tumor volume (GTV nx) and bilateral parotid glands were delineated on the predicted and real images (ground truth). The performance of models was evaluated by the consistence of the delineation between the predicted and ground truth images. Results:The proposed method could predict the anatomical images over the radiotherapy course. The contours of interest in the predicted image were consistent with those in the real image, with Dice similarity coefficient of 0.96, 0.90, 0.92, mean Hausdorff distance of 3.28, 4.18 and 3.86 mm, and mean distance to agreement of 0.37, 0.70, and 0.60 mm, for GTV nx, left parotid, and right parotid, respectively. Conclusion:This deep learning method is an accurate and feasible tool for predicting the patient's anatomical images, which contributes to predicting and preparing treatment strategy in advance and achieving individualized treatment.
