[Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy].

OBJECTIVE: In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99∓13.31)% to (32.55∓22.27)% and of the bladder from (44.37∓4.08)% to (28.99∓15.25)%.

[Prediction of three-dimensional dose distribution in intensity-modulated radiation therapy based on neural network learning].

OBJECTIVE: To establish the association between the geometric anatomical characteristics of the patients and the corresponding three-dimensional (3D) dose distribution of radiotherapy plan via feed-forward back-propagation neural network for clinical prediction of the plan dosimetric features. METHODS: A total of 25 fixed 13-field clinical prostate cancer intensity-modulated radiation therapy (IMRT)/stereotactic body radiation therapy (SBRT) plans were collected with a prescribed dose of 50 Gy. With the distance from each voxel to the planned target volume (PTV) boundary, the distance from each voxel to each organ-at-risk (OAR), and the volume of PTV as the geometric anatomical characteristics of the patients, the voxel deposition dose was used as the plan dosimetric feature. A neural network was used to construct the correlation model between the selected input features and output dose distribution, and the model was trained with 20 randomly selected cases and verified in 5 cases. RESULTS: The constructed model showed a small model training error, small dose differences among the verification samples, and produced accurate prediction results. In the model training, the point-to-point mean dose difference (hereinafter dose difference) of the 3D dose distribution was no greater than 0.0919∓3.6726 Gy, and the average of the relative volume values corresponding to the fixed dose sequence in the DVH (hereinafter DVH difference) did not exceed 1.7%. The dose differences among the 5 samples for validation was 0.1634∓10.5246 Gy with percent dose differences within 2.5% and DVH differences within 3%. The 3D dose distribution showed that the dose difference was small with reasonable predicted dose distribution. This model showed better performances for dose distribution prediction for bladder and rectum than for the femoral heads. CONCLUSION: We established the relationships between the geometric anatomical characteristics of the patients and the corresponding planning 3D dose distribution via feed-forward back-propagation neural network in patients receiving IMRT/SBRT for the same tumor site. The proposed model provides individualized quality standards for automatic plan quality control.