RESUMO
Objective 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.
RESUMO
Objective To propose a treatment planning optimization algorithm which can make full use of OAR dose distribution prediction meanwhile improving the output planning quality as much as possible.Methods We had reformulated an FMO function under the guidance of dose distribution prediction and also integrated equivalent uniform dose (gEUD) based on the consideration of prediction uncertainty,for providing optimal solution.Performance of the method was evaluated by comparing the optimized IMRT plan quality of 8 cervical cancers in the term of DVH curves,dose distribution and dosimetric endpoints with the original ones.Results The proposed method had a feasible,fast solution.Compared with original plan,its output plan had better plan quality in better dose homogeneity,less hot spot and further dose sparing for OARs.V30,V45 of rectum was decreased by (6.60±3.53)％ and (17.03±7.44)％,respectively,with the statistically significant difference (t=-4.954,-6.055,P＜0.05).V30,V45 of bladder was decreased by (14.74 ± 5.61) ％ and (14.99 ± 4.53) ％,respectively,with the statistically significant difference (t=-6.945,-8.759,P＜0.05).Conclusions We have successfully developed a predicted dose distribution and equivalent uniform dose-based planning optimization method,which is able to make good use of 3D dose prediction and ensure the output plan quality for intensity modulated radiation therapy.