High atomic number and density of dental implants leads to major problems at providing an accurate dose distribution in radiotherapy and contouring tumors and organs caused by the artifact in head and neck tumors. The limits and deficiencies of the algorithms using in the treatment planning systems can lead to large errors in dose calculation, and this may adversely affect the patient's treatment. In the present study, four commercial dental implants were used: pure titanium, titanium alloy (Ti‐6Al‐4V), amalgam, and crown. The effects of dental implants on dose distribution are determined with two methods: pencil beam convolution (PBC) algorithm and Monte Carlo code for 6 MV photon beam. The central axis depth doses were calculated on the phantom for a source–skin distance (SSD) of 100 cm and a field using both of algorithms. The results of Monte Carlo method and Eclipse TPS were compared to each other and to those previously reported. In the present study, dose increases in tissue at a distance of 2 mm in front of the dental implants were seen due to the backscatter of electrons for dental implants at 6 MV using the Monte Carlo method. The Eclipse treatment planning system (TPS) couldn't precisely account for the backscatter radiation caused by the dental prostheses. TPS underestimated the back scatter dose and overestimated the dose after the dental implants. The large errors found for TPS in this study are due to the limits and deficiencies of the algorithms. The accuracy of the PBC algorithm of Eclipse TPS was evaluated in comparison to Monte Carlo calculations in consideration of the recommendations of the American Association of Physicists in Medicine Radiation Therapy Committee Task Group 65. From the comparisons of the TPS and Monte Carlo calculations, it is verified that the Monte Carlo simulation is a good approach to derive the dose distribution in heterogeneous media.PACS numbers: 87.55.K‐

This study aimed to evaluate the usability and benefit of a new generation of auto segmentation, that automatically identifies organs and auto-contours them directly at CT simulator before creating prostate radiotherapy plans. The prostates of 10 patients were automatically contoured using the DirectORGANS auto-segmentation algorithm at the CT simulator. The CT scans were imported into the Eclipse treatment planning system for contouring. On the same CT image sets, the prostate was manually contoured by a group of five experienced physicians. MR-guided prostate contours were delineated using MRI images and used as a reference structure. The volumes of the prostate were measured, and the Overlap index (OI), Dice similarity index (DSC), and Volume difference (Dv) were calculated based on contours. The Kruskal-Wallis H test was performed with SPSS (P<0.05). MR-based contouring was used as a reference, and the OI, DSC, Dv, and contouring time results of users and artificial intelligence were analyzed accordingly. There was a significant difference in OI, DSC, and Dv between the results of users and artificial intelligence. The most significant difference between users, artificial intelligence, and MR-based contouring was contouring time (p <0.001). MR- based contouring was time-consuming. Artificial Intelligence’s automatic contouring of the prostate required minimal modification.

In study, we aimed to dosimetrically evaluate the usability of a new generation autocontouring algorithm (DirectORGANS) that automatically identifies organs and contours them directly in the computed tomography (CT) simulator before creating prostate radiotherapy plans. The CT images of 10 patients were used in this study. The prostates, bladder, rectum, and femoral heads of 10 patients were automatically contoured based on DirectORGANS algorithm at the CT simulator. On the same CT image sets, the same target volumes and contours of organs at risk were manually contoured by an experienced physician using MRI images and used as a reference structure. The doses of manually delineated contours of the target volume and organs at risk and the doses of auto contours of the target volume and organs at risk were obtained from the dose volume histogram of the same plan. Conformity index (CI) and homogeneity index (HI) were calculated to evaluate the target volumes. In critical organ structures, V60, V65, V70 for the rectum, V65, V70, V75, and V80 for the bladder, and maximum doses for femoral heads were evaluated. The Mann-Whitney U test was used for statistical comparison with statistical package SPSS (P < 0.05). Compared to the doses of the manual contours (MC) with auto contours (AC), there was no significant difference between the doses of the organs at risk. However, there were statistically significant differences between HI and CI values due to differences in prostate contouring (P < 0.05). The study showed that the need for clinicians to edit target volumes using MRI before treatment planning. However, it demonstrated that delineating organs at risk was used safely without the need for correction. DirectORGANS algorithm is suitable for use in RT planning to minimize differences between physicians and shorten the duration of this contouring step.