OAR Dose Distribution Prediction and gEUD Based Automatic Treatment Planning Optimization for Intensity Modulated Radiotherapy

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 organs-at-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), V30, V45 of rectum is decreased by (8.42±7.88) %, (15.49±7.48) %, respectively and V30, V45 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.