Objective: To compare the dosimetric parameters of step-shoot intensity-modulated radiotherapy (sIMRT), dynamic intensity-modulated radiotherapy (dIMRT), and volume-modulated arc therapy (VMAT) in thymoma and to study the feasibility of the ArcCHECK-3DVH system in three intensity-modulated radiotherapy plans to choose a more appropriate intensity-modulated radiotherapy for thymoma. Materials and Methods: Seventeen patients with thymoma were enrolled in this study. Treatment plans of sIMRT, dIMRT, and VMAT for each patient were based on the Monaco treatment planning system (TPS). Dosimetric verification was performed via the ArcCHECK-3DVH system. We compared and analyzed the 3D γ pass rates of the TPS dose calculation and ArcCHECK-3DVH system dose reconstruction with the three gamma criteria (3 mm/3%, 2 mm/2%, and 1 mm/1%) with a threshold of 10%. Dose–volume histogram analysis was used to compare the dose parameters for target volumes, and organs at risk (OARs), such as D_98%, D_50%, D_2%, D_max, V₂₀, and V_5. Monitor units (MUs) and delivery time were also compared. Results: There were significant differences in the three intensity-modulated radiotherapy plans. For target volume, VMAT showed the highest planning target volume (PTV) D_98% and the lowest PTV D_50% compared with sIMRT or dIMRT. The PTV D_2% of VMAT was lower than that of sIMRT and higher than that of dIMRT, and VAMT demonstrated the highest conformity index and MU, lowest homogeneity index, and shortest treatment delivery time. For the OARs, VMAT is not inferior to sIMRT and dIMRT in OARs protection. For the dosimetric verification, the entire area, PTV, lungs, heart, and spinal cord of VMAT showed the highest γ pass rates than the other two techniques under the gamma 3 mm/3% criteria, which was even more pronounced when the stricter gamma criteria of 2 mm/2% and 1 mm/1% were applied. Conclusion: VMAT can be applied to radiotherapy of thymoma, and the accuracy of treatment plan execution can be guaranteed through the ArcCHECK-3DVH system.

To date, the manual segmentation in radiotherapy contouring is featured with time- and effort-consuming and low efficiency. Therefore, it is imperative to develop novel technology to improve the precision and repeatability about the segmentation of radiotherapy contouring. The use of artificial intelligence (AI) delineation in tumor targets during radiotherapy has shown up, which contains the methods based on template atlas, image segmentation, and deep learning. Intelligent delineation of radiotherapy makes the automatic delineation of organs at risk possible, saves operators' time, and reduces the heterogeneity of contouring, which greatly increases the accuracy and quality of the contouring delineation in radiotherapy. All in all, automatic delineation of radiotherapy based on AI is flourishing. Researchers should further learn to build recognized standards and develop mature technologies to fulfill the clinical application in the near future.