Transformer attention mechanism based three-dimensional dose prediction for lung cancer intensity-modulated radiotherapy
10.3760/cma.j.cn113030-20230824-00073
- VernacularTitle:基于Transformer注意力机制的肺癌调强放射治疗三维剂量预测
- Author:
Yangting CHEN
1
;
Xin YANG
;
Fu JIN
;
Bin FENG
;
Wen LUO
Author Information
1. 南华大学核科学技术学院，衡阳 421001
- Keywords:
Deep learning;
Dose prediction;
Lung neoplasms;
Intensity-modulated radiotherapy
- From:
Chinese Journal of Radiation Oncology
2024;33(6):532-539
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a deep learning architecture based on 3D Transformers to predict dose distribution within intensity modulated radiation therapy (IMRT) plans for lung cancer.Methods:Clinical data of 174 lung cancer patients treated with IMRT in Chongqing University Cancer Hospital between January 2020 and December 2022 were retrospectively analyzed. All patients were divided into the training ( n=116), validation ( n=29), and test ( n=29) sets. We employed the Swin Unet Transformer (Swin Unetr) model to predict the three-dimensional dose distribution. The model was trained using computed tomography (CT) images, planning target volume (PTV) images, organs at risk (OAR) images, beam configuration information images, and distance images. We used various evaluation metrics such as mean absolute errors (MAE), Dice similarity coefficients (DSC), and dose volume histogram (DVH) dosimetric parameters to assess the performance of Swin Unetr and compared it with three mainstream deep learning models: CGAN, ResSEUnet, and ResUnet. Results:The MAE of the dose distribution prediction by Swin Unetr was recorded at 0.0143±0.0055. Conversely, the values of CGAN, ResSEUnet, and ResUnet were 0.0162±0.0055, 0.0167±0.0063, and 0.0164±0.0057, respectively. Furthermore, Swin Unetr achieved the highest DSC values (>0.85) across all isodose volumes. Regarding DVH dosimetric parameters, excluding D 2% of PTV and D mean of the heart, Swin Unetr exhibited no statistically significant differences in the remaining DVH dosimetric parameters (all P>0.05), demonstrating the best evaluation results in 66.67% of the overall dosimetric parameters and 75% of the PTV dosimetric parameters. Conclusions:Swin Unetr achieves the best score in multiple dosimetric evaluation indicators, and the highest DSC across all isodose volumes. Swin Unetr has significantly improved the accuracy of three-dimensional dose prediction during IMRT for lung cancer.
