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Multi-task learning-based three-dimensional dose distribution prediction for multiple organs in a single model
Guo Futong1, Li Yongbao2, Jia Qiyuan1, Qi Mengke1, Wu Aiqian1, Kong Fantu1, Mai Yanhua, Song Ting1, Zhou Linghong1
1Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515,China; 2Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060,China
AbstractObjective To establish a three-dimensional (3D) dose prediction model, which can predict multiple organs simultaneously in a single model and automatically learn the effect of the geometric anatomical structure on dose distribution.Methods Clinical radiotherapy plans of patients diagnosed with the same type of tumors were collected and retrospectively analyzed. For every plan, each organs at risk (OAR) voxel was regarded as the study sample and its deposited dose was considered as the dosimetric feature. A regularized multi-task learning method than could learn the relationship among different tasks was employed to establish the relationship matrix among tasks and the correlation between geometric structure and dose distribution among organs. In this experiment, the spinal cord, brainstem and bilateral parotids involved in the intensity-modulated radiotherapy (IMRT) plan of 15 nasopharyngeal cancer patients were utilized to establish the multi-organ prediction model. The relative percentage error between the predicted dose of voxel and the clinical planning dose was calculated to assess the feasibility of the model.Results Ten cases receiving IMRT plans were utilized as the training data, and the remaining five cases were used as the test data. The test results demonstrated a higher prediction accuracy and less data demand. And the average voxel dose errors among the spinal cord, brainstem and the left and right parotids were (2.01±0.02)%,(2.65±0.02)%,(2.45±0.02)% and (2.55±0.02)%,respectively. Conclusion The proposed model can accurately predict the dose of multiple organs in a single model and avoid the establishment of multiple single-organ prediction models, laying a solid foundation for patient-specific plan quality control and knowledge-based treatment planning.
Fund:National Key Research and Development Project (2017YFC0113203);National Natural Science Foundation of China (81601577,81571771)
Corresponding Authors:
Song Ting,Email:songting0129@163.com;Zhou Linghong, Email:smart@smu.edu.cn
Cite this article:
Guo Futong,Li Yongbao,Jia Qiyuan et al. Multi-task learning-based three-dimensional dose distribution prediction for multiple organs in a single model[J]. Chinese Journal of Radiation Oncology, 2019, 28(6): 432-437.
Guo Futong,Li Yongbao,Jia Qiyuan et al. Multi-task learning-based three-dimensional dose distribution prediction for multiple organs in a single model[J]. Chinese Journal of Radiation Oncology, 2019, 28(6): 432-437.
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