Abstract

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Title:: Dosimetric comparison of volume- versus inverse planning simulated annealing-based dose optimizations in afterloading brachytherapy for cervical cancer

Author(s):: XUE Tao1; SUN Yunchuan1; HE Xiaoyang2; LIU Guangbo1; HE Xinying1; BI Jianqing1; XIAO Li1; HU Tingting1; LIU Xiang1; WU Bin1; 1. Department of Radiotherapy, Cangzhou Hospital of Integrated TCM-WM, Cangzhou 061000, China; 2. Department of Intrarenal Urology, Cangzhou People’s Hospital, Cangzhou 061000, China

Keywords:: ; Keywords: cervical cancer; afterloading brachytherapy; dose optimization; dosimetry; volume-based optimization; inverse planning simulated annealing

Abstract:: Abstract: Objective To compare the differences of dose distributions between volume- and inverse planning simulated annealing (IPSA)-based optimizations in three-dimensional (3D) afterloading brachytherapy plan of cervical carcinoma, and provide the reference for the selection of the plan optimization in 3D afterloading brachytherapy of cervical cancer. Methods Fifteen cases of cervical cancer treated with 3D afterloading brachytherapy were enrolled in this study, and the original optimization was based on volume. Herein another optimization plans based on IPSA were designed based on the original images. Paired t test was used to analyze the dosimetric differences between two optimization methods. Results The V100%, V150% and homogeneity index of target areas were similar in two groups, without statistical differences (P>0.05). The average value of target conformity index in IPSA-based optimization plans was higher than that in volume-based optimization plans, with statistical differences (0.82+0.07 vs 0.77+0.11, P<0.05). Compared with volume-based optimization, IPSA-based optimization significantly reduced the organs-at-risk dose, and the difference was statistically significant (P<0.05). Conclusion IPSA-based optimization is superior to volume-based optimization in target conformity index, organs-at-risk sparing, and the calculation process of IPSA-based optimization is relatively simple, which make IPSA-based optimization be a preferable choice in clinic.