Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in...

Prostate cancer patients are commonly undergoing Radiotherapy (RT) and treatment planning system have a prominent role for dose calculation, while this would seem that dose distribution uncertainties of treatment planning system (TPS) may effect on RT results. Therefore, this study aimed to design a Dose prediction deep learning-based model for prostate cancer volumetric arc therapy (VMAT) applying MRI in Versa HD linear accelerator (linac).

In this work, MRI of 45 patients who underwent VMAT was acquired, and cycle-consistent GAN (CycleGAN) (that allow image-to-image translation) and U-net deep learning (DL) framework for prostate were employed. The synthetic CT (sCT) images were generated from MR images. The predicted dose among CycleGAN, U-net and Monaco TPS (that calculate dose distribution based on CT simulation images) was compared to each other.

The sCT that was generated employing CycleGAN illustrated more obvious boundaries than the sCT of U-net (sCTU-net). The gamma passing rate of cycleGAN and U-net was exceeded 97% and 90%, respectively, in all areas.

The results of this study illustrates that deep learning models including CycleGAN and U-net are good alternative for dose prediction of VMAT in Versa HD linac, while it seems that CycleGAN may be more accurate compared to U-net.