Dose-volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform-intensity radiation with equal angle intervals.

In this study, we developed a gated recurrent unit (GRU)-based recurrent neural network (RNN) for dose-volume histogram (DVH) prediction in volumetric modulated arc therapy (VMAT) planning for nasopharyngeal carcinomas (NPCs) based on uniform-intensity radiation with equal angle intervals and investigated the feasibility and usefulness of this method in optimization. METHODS AND MATERIALS: 124 NPC patients were selected from a database containing clinical VMAT plans from 2015 to 2018; of these patients, 100 were used for training the GRU-RNN, and 24 were used for testing. Regarding the prescribed doses covered 95 % of the planning target volume (PTV) for all plans in 30 or 31 fractions, 70 Gy was delivered to PTV70 (gross tumour volume with circumferential margin), 60 Gy were delivered to PTV60, 54 Gy were delivered to PTV54 and 66 Gy was delivered to PTV66 (lymph node gross tumour volume with circumferential margin), respectively. For each NPC patient, an equal-field-weight conformal radiotherapy plan was generated by a treatment planning system (TPS) to offer uniform-intensity radiation. Direction-dependent DVHs were employed to predict the DVH for VMAT with the GRU-RNN, and the regenerated VMAT experimental plans (EPs) guided by the predicted DVHs were evaluated by comparing them with the clinical plans (CPs). RESULTS: For the 24 test patients, the regenerated EPs guided by the GRU-RNN predictive model achieved very good consistency relative to the CPs. The...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research