Prediction of three-dimensional dose distribution for patient-specific quality assurance based on log files using WingsNet.

[BACKGROUND] This study aims to construct and train the WingsNet model, which leverages the parameters recorded in log files to rapidly and accurately predict the patient-specific three-dimensional (3D) dose distribution for IMRT quality assurance (QA).

[METHODS] We conducted a retrospective analysis of data from 286 lung cancer patients treated with a prescription of 60 Gy in 30 fractions, with 242 cases used for model training and 44 for testing. Log files containing information such as multi-leaf collimator (MLC) positions, monitor units (MU), and gantry angles were collected from Varian treatment accelerators. Pylinac software was employed to extract mechanical parameters from the log files, generating 2D fluence maps, which were then converted into 3D volumes using a ray-tracing algorithm. CT images, RT structures, and 3D volumes were resampled to a uniform dimension of 128*128*128 to serve as input for the WingsNet model, with the 3D dose distribution calculated by the treatment planning system (TPS) serving as output. The model training utilized L1 loss and mean squared error (MSE) as evaluation metrics.

[RESULTS] The results of this study demonstrate that the WingsNet model can effectively predict the 3D dose distribution of IMRT plans based on the parameters recorded in the log files. Evaluation through metrics such as mean absolute error (MAE), root mean square error (RMSE), and dose-volume histogram (DVH) indices reveals that the model performs well in most areas, with some errors observed in the planning target volume (PTV) region and at high dose levels, yet it retains potential for clinical use. Visually, the isodose line distributions are consistent. The Dice coefficients between the predicted and reference dose distributions at varying isodose line levels indicate a decreasing trend as the dose level increases.

[CONCLUSIONS] The WingsNet model developed in this study successfully predicts the patient-specific 3D dose distribution for QA by parsing the parameters recorded in log files. This model shows promise for use in 3D dose distribution verification for IMRT, providing an efficient and reliable tool for the verification of 3D dose distributions in patient-specific QA.