Prediction model of local effect model-based rectal dose volume histogram in prostate cancer patients treated with carbon ion radiotherapy
Yang Yifeng1,2, Wang Weiwei3, Li Ping1, Zhao Jingfang2,3, Zhang Qing1
1Department of Abdominal and Pelvic Tumor, Shanghai Proton and Heavy Ion Center / Shanghai Key Laboratory of radiation oncology(20dz2261000) / Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China; 2Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai 201321, China; 3Department of Medical Physics, Shanghai Proton and Heavy Ion Center / Shanghai Key Laboratory of radiation oncology(20dz2261000) / Shanghai Engineering Research Center of Proton and Heavy Ion Radiation Therapy, Shanghai 201321, China
Abstract:Objective To establish a local effect model (LEM)-based rectal dose volume histogram (DVH) prediction model in prostate cancer patients treated with carbon ion therapy based on Japanese experience, aiming to provide reference for clinically reducing the incidence of rectal adverse reactions. Methods The planning CT data of 76 patients with prostate cancer were collected. The microdosimetric kinetic model (MKM) was used for initial planning, and the LEM was selected to recalculate the biological dose based on the same fields to MKM. Then, the geometric features and DVH of the rectum were extracted from the LEM plans. The planning data of 61 cases were used to establish the prediction model with linear regression and the other 15 cases were used for validation. Results The ratio of the overlapped volume between the rectum and the region of interest (ROI) expended from planning target volume by 1cm along the left and right directions of the rectum could be proved to be the characteristic parameters for linear regression. The mean goodness-of-fit R2 of predicted and LEM plan-based DVH of 15 cases was 0.964. The results of predicted rectal adverse reactions based on predicted DVH were consistent with those of LEM plan-based DVH. Conclusions The linear regression method used in this study can establish an accurate prediction model of rectal DVH, which may provide certain reference for reducing the incidence of rectal adverse reactions. Nevertheless, the findings remain to be further verified by clinical trials with larger sample size.
Yang Yifeng,Wang Weiwei,Li Ping et al. Prediction model of local effect model-based rectal dose volume histogram in prostate cancer patients treated with carbon ion radiotherapy[J]. Chinese Journal of Radiation Oncology, 2021, 30(10): 1041-1046.
[1] Inaniwa T, Furukawa T, Kase Y, et al. Treatment planning for a scanned carbon beam with a modified microdosimetric kinetic model[J]. Phys Med Biol, 2010, 55(22):6721-6737. DOI:10.1088/0031-9155/55/22/008. [2] Kase Y, Kanai T, Matsumoto Y, et al. Microdosimetric measurements and estimation of human cell survival for heavy-ion beams[J]. Radiat Res, 2006, 166(4):629-638. DOI:10.1667/RR0536.1. [3] Hawkins RB. A statistical theory of cell killing by radiation of varying linear energy transfer[J]. Radiat Res, 1994, 140(3):366-374. DOI:10.2307/3579114. [4] Inaniwa T, Kanematsu N, Matsufuji N, et al. Reformulation of a clinical-dose system for carbon-ion radiotherapy treatment planning at the National Institute of Radiological Sciences, Japan[J]. Phys Med Biol, 2015, 60(8):3271-3286. DOI:10.1088/0031-9155/60/8/3271. [5] Scholz M, Kraft G. Track structure and the calculation of biological effects of heavy charged particles[J]. Adv Space Res, 1996, 18(1-2):5-14. DOI:10.1016/0273-1177(95)00784-C. [6] Bodensteiner D. RayStation:External beam treatment planning system[J]. Med Dosim, 2018, 43(2):168-176. DOI:10.1016/j.meddos.2018.02.013. [7] Wang W, Huang Z, Sheng Y, et al. RBE-weighted dose conversions for carbon ion radiotherapy between microdosimetric kinetic model and local effect model for the targets and organs at risk in prostate carcinoma[J]. Radiother Oncol, 2020, 144:30-36. DOI:10.1016/j.radonc.2019.10.005. [8] 王巍伟,孙家耀,王征,等. 质子重离子治疗靠近消化道肝癌的剂量学研究[J]. 中华放射肿瘤学杂志,2018, 27(11):999-1003. DOI:10.3760/cma.j.issn.1004-4221.2018.11.010. Wang WW, Sun JY, Wang Z, et al. Dosimetry of particle radiotherapy for liver cancer adjacent to gastrointestinal tract[J]. Chin J Radiot Oncol, 2018, 27(11):999-1003. DOI:10.3760/cma.j.issn.1004-4221.2018.11.010. [9] Fukahori M, Matsufuji N, Himukai T, et al. Estimation of late rectal normal tissue complication probability parameters in carbon ion therapy for prostate cancer[J]. Radiother Oncol, 2016, 118(1):136-140. DOI:10.1016/j.radonc.2015.11.023. [10] TsujⅡ H, Kamada T, Shirai T, et al. Carbon-ion radiotherapy[M]. London:Springer, 2014. DOI:10.1007/978-4-431-54457-9. [11] Xu H, Lu J, Wang J, et al. Implement a knowledge-based automated dose volume histogram prediction module in Pinnacle (3) treatment planning system for plan quality assurance and guidance[J]. J Appl Clin Med Phys, 2019, 20(8):134-140. DOI:10.1002/acm2.12689. [12] 范嘉伟,王佳舟,胡伟刚. 基于核密度估计的自动计划研究[J]. 中华放射肿瘤学杂志,2017, 26(6):661-666. DOI:10.3760/cma.j.issn.1004-4221.2017.06.012. Fan JW, Wang JZ, Hu WG. A study of automatic treatment planning based on kernel density estimation[J]. Chin J Radiat Oncol, 2017, 26(6):661-666. DOI:10.3760/cma.j.issn.1004-4221.2017.06.012.