Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning

Xinyuan Chen; Ji Zhu; Bining Yang; Deqi Chen; Kuo Men; Jianrong Dai

doi:10.3389/fonc.2023.1041769

Combining distance and anatomical information for deep-learning based dose distribution predictions for nasopharyngeal cancer radiotherapy planning

Front Oncol. 2023 Feb 28:13:1041769. doi: 10.3389/fonc.2023.1041769. eCollection 2023.

Authors

Xinyuan Chen^{1

2}, Ji Zhu¹, Bining Yang¹, Deqi Chen¹, Kuo Men¹, Jianrong Dai¹

Affiliations

¹ National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² National Cancer Center/National Clinical Research Center for Cancer/Hebei Cancer Hospital, Chinese Academy of Medical Sciences, Langfang, China.

Abstract

Purpose: Deep-learning effectively predicts dose distributions in knowledge-based radiotherapy planning. Using anatomical information that includes a structure map and computed tomography (CT) data as input has been proven to work well. The minimum distance from each voxel in normal structures to planning target volume (DPTV) closely affects each voxel's dose. In this study, we combined DPTV and anatomical information as input for a deep-learning-based dose-prediction network to improve performance.

Materials and methods: One hundred patients who underwent volumetric-modulated arc therapy for nasopharyngeal cancer were selected in this study. The prediction model based on a residual network had DPTV maps, structure maps, and CT as inputs and the corresponding dose distribution maps as outputs. The performances of the combined distance and anatomical information (COM) model and the traditional anatomical (ANAT) model with two-channel inputs (structure maps and CT) were compared. A 10-fold cross validation was performed to separately train and test the COM and ANAT models. The voxel-based mean error (ME), mean absolute error (MAE), dosimetric parameters, and dice similarity coefficient (DSC) of isodose volumes were used for modeling evaluation.

Results: The mean MAE of the body volume of the COM model were 4.89 ± 1.35%, highly significantly lower than those for the ANAT model of 5.07 ± 1.37% (p<0.001). The ME values of the body for the 2-type models were similar (p >0.05). The mean DSC values of the isodose volumes in the range of 60 Gy were all better in the COM model (p<0.05), and there were highly significant differences between 10 Gy and 55 Gy (p<0.001). For most organs at risk, the ME, MAE, and dosimetric parameters predicted by both models were concurrent with the ground truth values except the MAE values of the pituitary and optic chiasm in the ANAT model and the average mean dose of the right parotid in the ANAT model.

Conclusions: The COM model outperformed the ANAT model and could improve automated planning with statistically highly significant differences.

Keywords: anatomical information; deep-learning; dose-prediction; minimum distance; radiotherapy treatment planning.

Grants and funding

This work was supported by Beijing Natural Science Foundation (7222149), CAMS Innovation Fund for Medical Sciences (2021-I2M-C&T-A-016), the National Natural Science Foundation of China (12005302 and 11875320) and the Beijing Hope Run Special Fund of Cancer Foundation of China (LC2021A15).