Deep learning-based automatic reconstruction of interstitial needles in brachytherapy for cervical cancer
10.3760/cma.j.cn113030-20240928-00376
- VernacularTitle:基于深度学习的宫颈癌近距离治疗插植针重建研究
- Author:
Shijing WEN
1
;
Tao LIU
;
Siqi WANG
;
Lipeng XU
;
Qingxian ZHANG
;
Xianliang WANG
Author Information
1. 成都理工大学核技术与自动化工程学院，成都 610059
- Publication Type:Journal Article
- Keywords:
Deep learning;
Brachytherapy;
Uterine cervical neoplasms;
Interstitial needles
- From:
Chinese Journal of Radiation Oncology
2025;34(3):282-288
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of autosegmentation and reconstruction of interstitial needles in intracavitary / interstitial brachytherapy (IC-ISBT) for cervical cancer based on deep learning.Methods:The data of 180 treatment plans from 98 patients who received IC-ISBT were retrospectively collected and divided into the training, validation, and testing sets in a 16:1:1 ratio. Masks of needles were created using the dwell positions of radiation sources, and a 3D U-Net model was trained. The performance of the model was evaluated using the Dice similarity coefficient (DSC). Absolute and relative accuracy rates were used to assess the results of this method, and the position bias was used to evaluate the precision of predictions in the transversal plan of CT scans. Wilcoxon rank-sum test was performed to evaluate the reconstruction efficiency by comparing the time required for automated versus manual reconstruction.Results:DSC of the model was 0.93 ± 0.02. The absolute and relative accuracy rates were 0.44 ± 0.09 and 0.95 ± 0.03, respectively. The distance deviation on the CT horizontal plane was (0.58 ± 0.54) mm. The average time of autosegmentation and reconstruction was (6.2 ± 0.4) s, leading to a significant reduction in time consumption compared with manual construction ( P<0.001). Conclusions:Based on deep learning, using the dwell positions of radiation sources for data annotation, combined with post-processing algorithms, accurate automated segmentation and digital reconstruction of needles in IC-ISBT three-dimensional CT images can be achieved, significantly improving reconstruction efficiency.
