Deep learning-based prediction of three-dimensional dose distribution in postoperative intensity-modulated radiotherapy for esophageal cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第2期
- Page:
- 133-138
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Deep learning-based prediction of three-dimensional dose distribution in postoperative intensity-modulated radiotherapy for esophageal cancer
- Author(s):
- WANG Wencheng1; ZHOU Jieping2; ZHANG Peng2; WU Ailin2; WU Aidong1; 2
- 1. School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China 2. Department of Radiation Oncology, the First Affiliated Hospital of University of Science and Technology of China, Hefei 230001, China
- Keywords:
- Keywords: deep learning esophageal cancer intensity-modulated radiotherapy dose distribution prediction
- PACS:
- R735.1；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.02.001
- Abstract:
- Abstract: Objective To develop a deep learning network model for predicting the three-dimensional (3D) dose distribution in postoperative intensity-modulated radiotherapy (IMRT) for esophageal cancer. Methods A total of 100 postoperative patients with upper and middle esophageal cancer treated by IMRT were enrolled in the study. The CT images, segmentations of target areas and organs-at-risk, and conformal beam configuration were taken as input data, and IMRT dose distribution was taken as output data. The established hybrid network 3D U-Res-Net was used for training and obtaining prediction model which was then used for the prediction of 3D dose distribution on the test set. The prediction accuracy was evaluated by the average prediction bias [δ], mean absolute error (MAE), Dice similarity coefficient (DSC) and Husdorff distance (HD95). Results For the test set, the average prediction bias ranged from -0.23% to 0.78%, and MAE varied from 1.67% to 3.07%. The average DSC was above 0.91 for all isodose surfaces, especially when the dose was less than 30 Gy (DSC was higher than 0.95), and the average HD95 was from 0.51 cm to 0.73 cm. The dosimetric parameters of the prediction plan were all within the clinically allowable range, and the relative dose deviation was less than 2%. There is no significant difference in dosimetric parameters except for D2 to target area, Dmax to spinal cord and V30 of whole lung (P<0.05). Conclusion The 3D dose distribution in the postoperative intensity-modulated radiotherapy (IMRT) for esophageal cancer can be accurately predicted by the established 3D U-Res-Net model.
Last Update: 2022-03-06