BackgroundAutomatic multi-criteria optimization is necessary for intensity modulated radiation therapy (IMRT) because of low planning efficiency and large plan quality uncertainty in current clinical practice. Most studies focused on imitating dosimetrists’ planning procedures to automate this process and ignored the fact that organ-based objective functions typically used in commercial treatment planning systems (such as dose-volume function) usually lead to sub-optimal plans. To guarantee the optimum results and to automate this process, we incorporate an improved automation strategy and a voxel-based optimization algorithm to generate a novel automatic multi-criteria optimization framework. We then evaluate it in clinical cases.MethodsThis novel automatic multi-criteria optimization framework incorporates a ranked priority-list based automatic constraints adjustment strategy and an in-house developed voxel-based optimization algorithm. Constraints are sequentially adjusted following a pre-defined priority list. Afterward, a voxel-based fluence map optimization (FMO) with an orientation to the newly updated constraints is launched to find a Pareto optimal solution. Loops of constraints adjustment are repeated until each of them could not be relaxed or tightened. The feasibility of the framework is evaluated with 10 automatic generated gynecology (GYN) cancer IMRT cases by comparing the dosimetric performance with the original.ResultsPlan quality improvement is observed for our automatic multi-criteria optimization method. Comparable DVHs are found for the planning target volume (PTV), but with better organs-at-risk (OAR) dose sparing. Among 13 evaluated dosimetric endpoints, 5 of them show significant improvements in automatically generated plans compared with the original plans. Investigation of improvement tendency during optimization exhibits gradual change as the optimization stage proceeds. An initial voxel-based optimization stage and in-low-priority dosimetric criteria tighten can significantly contribute to the optimization procedure.ConclusionsWe have successfully developed an automatic multi-criteria optimization framework that can dramatically reduce the current trial-and-error patterned planning workload while affording an efficient method to assure high plan quality consistency. This optimization framework is expected to greatly facilitate precise radiation therapy because of its advantages of planning efficiency and plan quality improvement.

Purpose: To investigate region-specific models for organ's three-dimensional dose distribution prediction with neural network. Methods: The dose distribution from different bladder regions for 52 prostate volumetric modulated arc therapy cases were first analyzed, the two region-specific models were then built to predict the bladder dose distribution, the initial model and the refined model. For the initial model, the bladder was divided into overlapping region and nonoverlapping region, two artificial neural networks were established with each one corresponding to one region. For the refined model, the nonoverlapping region was further divided into three subregions, and four artificial neural network models were built in total. For each artificial neural network model, several spatial and volumetric features for the bladder were extracted as the input to the neural network. To investigate the feasibility and dose distribution prediction accuracy of the proposed two region-specific models, the mean absolute error, gamma passing rate, dose volume histogram, and dose distribution for the refined model were compared. Results: According to the predicted dose from the initial model and the refined model, the average mean absolute error for all cases is reduced from 5.03 Gy in the initial model to 3.23 Gy in the refined model, the refined model reduce the mean absolute error about 2% relative to the prescription dose. The average area deviation of predicted dose volume histogram by the refined model is 5%, and the average gamma passing rate is 82% and 94% with the 3 mm/3% and 5 mm/5% criteria, which shows that the refined model proposed in this study has high dose-prediction accuracy. Conclusions: Two region-specific three-dimensional dose distribution prediction models for volumetric modulated arc therapy prostate cases based on neural network have been investigated, the models have shown that a more refined consideration of structures improved the accuracy of predicted dose distribution.

In current knowledge-based treatment planning for intensity-modulated radiation therapy (IMRT), 3-dimensional dosimetric goals are predicted to provide abundant and appropriate starting points for planning optimization, but considering there're uncertainties with those dose distribution predictions, how to tailor the objective function and constraints accordingly is quite a concern. Here, we represent a novel automatic treatment optimization method that is capable of making the most of dose distribution prediction meanwhile achieving its optimum as much as possible. On the foundation of an in-house organsat-risk (OARs) dose distribution prediction model, we reformulate a traditional fluence map optimization (FMO) model by a predicted dose distribution-based objective, an equivalent uniform dose sparing for OARs and hard dose constraints for planning target volume (PTV). Feasibility and performance of the method is evaluated with 10 gynecology (GYN) cancer IMRT cases by comparing the plan quality of the generated to the original clinical ones, in the term of dose-volume-histogram (DVH) curves, dose distribution and detailed dosimetric endpoints. Results show plan quality improvement by our proposed method, with comparable PTV dose coverage but further dose sparing for OARs. Among 6 investigated OAR dosimetric endpoints, 4 of them are observed with significant improvement (P<0.05), V 30 , V 45 of rectum is decreased by (8.42±7.88) %, (15.49±7.48) %, respectively and V 30 , V 45 of bladder is decreased by (14.47±5.08) %, (14.24±4.71) %, respectively. We have successfully developed a novel automatic optimization method which is able to make good use of 3D dose prediction and ensure the output plan quality for IMRT. INDEX TERMS 3D dose distribution prediction, equivalent uniform dose, intensity modulated radiation therapy, prediction guided treatment planning optimization.

This study aimed to explore the effect of carbon fiber couch on radiotherapy dose attenuation and gamma pass rate in intensity-modulated radiotherapy (IMRT) plans. A phantom inserted with an ionization chamber was placed at different positions of the couch, and the dose was measured by the chamber. Under the same positioning, the phantom dose was calculated using the real and virtual couch images, and the difference in the planned dose of radiotherapy was compared. Ten clinical IMRT plans were selected as dose verification data, and the gamma pass rates were compared between couch addition and non-addition conditions. When the radiation field was near 110° and 250°, the measured value attenuation coefficient of the ionization chamber at the joint of the couch was up to 34%; the attenuation coefficient of the treatment couch from the actual couch image calculated using the treatment planning system (TPS) was up to 33%; the attenuation coefficient of the virtual couch calculated using the TPS was up to 4.0%. The gamma pass rate of the dose verification near gantry angles 110° and 250° was low, and that of the joint could be lower than 85% under the condition of 3%/3 mm. The gamma pass rates of the radiation field passing through the couch were all affected. The dose was affected by the radiation field passing through the couch, with the largest effect when passing through the joint part of the treatment couch, followed by that of the main couch plate and extension plate. When the irradiation field passed through the joint and near 110° and 250° of the main couch, the dose difference was large, making it unsuitable for treatment.

Objective This study aimed to identify the effects of beamlet width on dynamic intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma (NPC) and determine the optimal parameters for the most effective radiotherapy plan. Methods This study evaluated 20 patients with NPC were selected for dynamic IMRT. Only the beamlet width in the optimization parameters was changed (set to 2, 4, 6, 8, and 10 mm that were named BL02, BL04, BL06, BL08, and BL10, respectively) to optimize the results of the five groups of plans. Using the plan quality scoring system, the dose results of the planning target volumes (PTVs) and organs at risks (OARs) were analyzed objectively and comprehensively. The lower the quality score, the better the quality of the plan. The efficiency and accuracy of plan execution were evaluated using monitor units (MUs) and plan delivery time (PDT). Results The BL04 mm group had the lowest quality score for the targets and OARs (0.087), while the BL10 mm group had the highest total score (1.249). The BL04 mm group had the highest MUs (837 MUs) and longest PDT (358 s). However, the MUs range of each group plan was below 100 MUs, and the PDT range was within 30 s. In the BL02, BL04, BL06, BL08, and BL10 plans, <5 MUs segments accounted for 33%, 16%, 24%, 33%, and 40% of total segments, respectively, with which the lowest was in the BL04 mm group. Conclusion Smaller beamlet widths have not only reduced OARs dose while maintaining high dose coverage to the PTVs, but also lead to more MUs that would produce greater PDT. Considering the quality and efficiency of dynamic IMRT, the beamlet width value of the Monaco treatment planning system set to 4 mm would be optimal for NPC.