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Objectives: Hydrogel spacer (HS) was developed to reduce rectal toxicities caused by radiotherapy, but has been reported to cause major adverse events. Our institute has attempted to introduce a hyaluronic acid (HA) as an alternative spacer. This study aimed to compare rectal doses and geometric distributions between the HS and HA implantation in prostate cancer. Methods: HS and HA were inserted in 20 and 18 patients undergoing high-dose brachytherapy, respectively. The rectum spacer volumes injected were 10 mL and 22 mL, respectively. In the treatment planning system, 13.5 Gy was administered with common catheter positions. The rectal dose indices were assessed between the spacer groups for dosimetry evaluation. Distances between the prostate and rectum and configurations of the spacers were compared. Results: The mean doses irradiated to 0.1 and 2 mL of the rectum were 10.45 Gy and 6.71 Gy for HS, and 6.73 Gy and 4.90 Gy for HA (p

Background and purpose: Online magnetic resonance-guided adaptive radiotherapy (MRgART) is a new technology of radiotherapy and requires a new quality control program in many aspects. This study aimed to gain a deeper understanding of risks in online MRgART through the application of failure mode and effect analysis (FMEA) for more enhanced and effective quality assurance (QA) programs. Materials and methods: We present an FMEA conducted by a multidisciplinary team with more than two years of experience. A process map describing the whole process of online MRgART was developed and potential failure modes were identified. High-risk failure modes and their potential causes and corrective measures were also identified. Failure modes were classified into three categories, MRgRT, online ART, and conventional RT, to investigate their features. A comparison with previous studies was also conducted to gain a general perspective. Results: In total, 153 failure modes and 49 high risks were identified. Among all failure modes, 51, 63, and 66 were related to MRgRT, online ART, and conventional RT, respectively. The hazardous processes were structure segmentation, treatment planning, and treatment beam delivery. Lists of failure modes identified in this study and previous studies were presented. Based on the results, characteristics and general aspects of the risks were discussed. Conclusion: Exploring the results of the FMEA enhanced our understanding of risk characteristics to improve QA program of online MRgART.

Abstract 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.