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.

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.

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.

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.

Multi-task learning; Three-dimensional dose distribution prediction; Correlation of multi-organ in a single-model; Intensity-modulated radiation therapy