Background
Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.
Methods
Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.
Results
Among patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.
Conclusions
In terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.
To measure the absorbed dose to water D in proton beams using a radiophotoluminescent glass dosimeter (RGD), a method with the correction for the change of the mass stopping power ratio (SPR) and the linear energy transfer (LET) dependence of radiophotoluminescent efficiency [Formula: see text] is proposed. The calibration coefficient in terms of D for RGDs (GD-302M, Asahi Techno Glass) was obtained using a Co γ-ray. The SPR of water to the RGD was calculated by Monte Carlo simulation, and [Formula: see text] was investigated experimentally using a 70 MeV proton beam. For clinical usage, the residual range R was used as a quality index to determine the correction factor for the beam quality [Formula: see text] and the LET quenching effect of the RGD [Formula: see text]. The proposed method was evaluated by measuring D at different depths in a 200 MeV proton beam. For both non-modulated and modulated proton beams, [Formula: see text] decreases rapidly where R is less than 4 cm. The difference in [Formula: see text] between a non-modulated and a modulated proton beam is less than 0.5% for the R range from 0 cm to 22 cm. [Formula: see text] decreases rapidly at a LET range from 1 to 2 keV µm. In the evaluation experiments, D using RGDs, [Formula: see text] showed good agreement with that obtained using an ionization chamber and the relative difference was within 3% where R was larger than 1 cm. The uncertainty budget for [Formula: see text] in a proton beam was estimated to investigate the potential of RGD postal dosimetry in proton therapy. These results demonstrate the feasibility of RGD dosimetry in a therapeutic proton beam and the general versatility of the proposed method. In conclusion, the proposed methodology for RGDs in proton dosimetry is applicable where R > 1 cm and the RGD is feasible as a postal audit dosimeter for proton therapy.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.