Statistical analysis of duration of each phase of Unity MR-linac in clinical application
Sun Yingying, Hong Tianhang, Wang Hong, Li Shenglan, Tian Yuan, Huan Fukui, Qin Shirui
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Abstract：Objective To analyze the duration of each phase of Unity MR-linac in clinical application,aiming to provide reference for clinical optimization of the process time. Methods Clinical data of 55 patients treated with Unity MR-linac were retrospectively analyzed. All patients were divided into the adapt to position (ATP) and adapt to shape (ATS) groups according to the planning method. The duration of each phase in the treatment process, the name and the time of each sequence, the number of beams, segments and total monitor units (MUs) were recorded and compared between two groups. In addition, the set-up time was counted according to different treatment sites. The time of each sequence and set-up time were expressed as the median M (Q1, Q3), and the number of beams, segments and total MUs of each plan were described as the mean±SD. Results 42 patients underwent ATP with a total of 305 treatment sessions:setup time was 3(2, 5) min, MR scanning time was 5(4, 7) min, registration time was 3(3, 4) min, adaptive planning time was 8(4, 12) min, beam on time was 8(6, 11) min, and the total time was 30(25, 36) min. 13 patients received ATS with a total of 65 treatment sessions:setup time was 2(2, 3) min, MR scanning time was 7(5, 8) min,registration time was 4(3, 5) min, time of delineation of target and organs at risk was 12(9,16) min, adaptive planning time was 11(10,14) min, beam on time was 10(9,11) min and the total time was 55(49,61) min. The set-up time according to treatment sites was 4(2, 4) min in the head and neck, 2(2, 4) min in the chest, and 3(2, 5) min in the abdomen. The number of fields, segments and total MUs during ATP were 8.1±1.7, 49.9±31.2, 846.75±363.44 in the head and neck, 8.0±2.0, 60.7±13.3, 790.21±279.00 in the chest, and 9.7±2.0, 81.2±22.3, 2007.32±1053.81 in the abdomen, respectively. The number of fields, segments and total MUs during ATS in head and neck of one case were 13, 39, 993.07, and 9.5±1.5, 65.5±6.3, 2763.26±835.41 in the abdomen. Conclusions MR-guided radiotherapy yields huge potential in clinical application. However, there is still much room for the improvement of shortening the process duration.
Sun Yingying,Hong Tianhang,Wang Hong et al. Statistical analysis of duration of each phase of Unity MR-linac in clinical application[J]. Chinese Journal of Radiation Oncology, 2022, 31(6): 550-555.
[1] Van Herk M, Mc william A, Dubec M, et al. Magnetic resonance imaging–guided radiation therapy:a short strengths, weaknesses, opportunities, and threats analysis[J]. Int J Radiat Oncol Biol Phys,2018,101(5):1057-1060. DOI:10.1016/j.ijrobp.2017.11.009. [2] Raaymakers BW, Lagendijk JJ, Overweg J, et al. Integrating a 1.5 T MRI scanner with a 6MV accelerator:proof of concept[J]. Phys Med Biol, 2009, 54(12):N229-237. DOI:10.1088/0031-9155/54/12/N01. [3] Olaciregui-Ruiz I, Torres-Xirau I, Teuwen J, et al. A deep learning-based correction to EPID dosimetry for attenuation and scatter in the unity MR-linac system[J]. Phys Med, 2020, 71:124-131. DOI:10.1016/j.ejmp.2020.02.020. [4] Bertelsen AS, Schytte T, Møller PK, et al. First clinical experiences with a high field 1.5 T MR linac[J]. Acta Oncol, 2019, 58(10):1352-1357. DOI:10.1080/0284186X.2019.1627417. [5] Lagendijk JJ, Raaymakers BW, van Vulpen M. The magnetic resonance imaging-linac system[J]. Semin Radiat Oncol, 2014, 24(3):207-209. DOI:10.1016/j.semradonc.2014.02.009. [6] Raaymakers BW, Jürgenliemk-Schulz IM, Bol GH, et al. First patients treated with a 1.5 T MRI-linac:clinical proof of concept of a high-precision, high-field MRI guided radiotherapy treatment[J]. Phys Med Biol, 2017, 62(23):L41-L50. DOI:10.1088/1361-6560/aa9517. [7] Lin H, Liu T, Su L, et al. Modeling of radiotherapy linac source terms using ARCHER Monte Carlo code:performance comparison for GPU and MIC parallel computing devices[J]. EPJ Web Confer,2017,153:4010. DOI:10.1051/epjconf/201715304010. [8] 张彦新,郇福奎,朱耿强,等. 乳腺癌术后调强放疗颈胸一体固定的摆位误差分析[J]. 中华放射肿瘤学杂志,2021, 30(8):835-840. DOI:10.3760/cma.j.cn113030-20200229-00081. Zhang YX, Huan FK, Zhu GQ, et,al. Analysis of setup errors of postoperative intensity-modulated radiotherapy immobilized with integrated cervicothoracic board (mask) system in breast cancer patients[J]. Chin J Radiat Oncol,2021, 30(8):835-840. DOI:10.3760/cma.j.cn113030-20200229-00081 [9] Lee SL, Hall WA, Morris ZS, et al. MRI-guided radiation therapy[J]. Adv Oncol, 2021(1):29-39. DOI:10.1016/j.yao.2021.02.003. [10] 黄大钡, 郭纪慈, 李珍, 等. 颈段、胸上段食管癌容积弧形调强放疗与固定野调强放疗分次内误差的比较[J]. 临床肿瘤学杂志, 2017, 22(1):58-61. DOI:10.3969/j.issn.1009-0460.2017.01.012. Huang DB, Guo JC, Li Z, et al. Study of the intra?fractional setup errors for cervical or upper thoracic esophageal carcinoma in VMAT and IMRT[J]. Chin Clin Oncol,2017,22(1):58-61. DOI:10.3969/j.issn.1009-0460.2017.01.012. [11] Werensteijn-Honingh AM, Kroon PS, Winkel D, et al. Feasibility of stereotactic radiotherapy using a 1.5 T MR-linac:multi-fraction treatment of pelvic lymph node oligometastases[J]. Radiother Oncol, 2019, 134:50-54. DOI:10.1016/j.radonc.2019.01.024. [12] 刘镖水,郭旋,丁寿亮,等. MR引导的放疗系统临床应用[J]. 中华放射肿瘤学杂志,2021, 30(02):134-139. DOI:10.3760/cma.j.cn113030-20200317-00118. Liu BS, Guo X, Ding SL, et al. The clinical application of magnetic resonance-guided radiotherapy[J]. Chin J Radiat Oncol, 2021, 30(02):134-139. DOI:10.3760/cma.j.cn113030-20200317-00118. [13] 覃仕瑞,程斌,田源,等. 医科达磁共振加速器初步临床实践[J]. 中华放射肿瘤学杂志,2021, 30(7):688-691. DOI:10.3760/cma.j.cn113030-20200810-00411. Qin SR, Cheng B, Tian Y, et al. Preliminary clinical practice of Elekta Unity MR-linac[J]. Chin J Radiat Oncol,2021, 30(7):688-691. DOI:10.3760/cma.j.cn113030-20200810-00411. [14] Intven M, de Mol van Otterloo SR, Mook S, et al. Online adaptive MR-guided radiotherapy for rectal cancer;feasibility of the workflow on a 1.5T MR-linac:clinical implementation and initial experience[J]. Radiother Oncol, 2021, 154:172-178. DOI:10.1016/j.radonc.2020.09.024. [15] Li XA, Tai A, Arthur DW, et al. Variability of target and normal structure delineation for breast cancer radiotherapy:an RTOG multi-institutional and multiobserver study[J]. Int J Radiat Oncol Biol Phys, 2009, 73(3):944-951. DOI:10.1016/j.ijrobp.2008.10.034. [16] Feng MU, Demiroz C, VIneberg KA, et al. Intra-observer variability of organs at risk for head and neck cancer:geometric and dosimetric consequences[J]. Int J Radiat Oncol Biol Phys, 2010, 78(3):S444-S445. DOI:10.1016/j.ijrobp.2010.07.1044. [17] Nelms BE, Tomé WA, Robinson G, et al. Variations in the contouring of organs at risk:test case from a patient with oropharyngeal cancer[J]. Int J Radiat Oncol Biol Phys, 2012, 82(1):368-378. DOI:10.1016/j.ijrobp.2010.10.019. [18] 刘洋,张烨,易俊林等. 自动分割技术在鼻咽癌靶区及危及器官勾画应用价值的研究[J]. 中华放射肿瘤学杂志,2021, 30(9):882-887. DOI:10.3760/cma.j.cn113030-20210521-00200. Liu Y, Zhang Y,YI JL, et,al. The value of automatic segmentation of target volume and organs at risk for nasopharngeal carcinoma[J]. Chin J Radiat Oncol,2021, 30(9):882-887. DOI:10.3760/cma.j.cn113030-20210521-00200. [19] Liang F, Qian P, Su KH, et al. Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy:an intelligent, multi-level fusion approach[J]. Artif Intell Med, 2018, 90:34-41. DOI:10.1016/j.artmed.2018.07.001. [20] 许文哲,王长建,马一鸣,等. 核磁共振图像引导的放疗技术进展[J]. 生物医学工程学杂志,2021, 38(01):161-168. DOI:10.7507/1001-5515.202002029. Xu WZ, Wang CJ, Ma YM, et al. Advances in magnetic resonance imaging guided radiation therapy.[J]. J Biomed Engineer,2021, 38(01):161-168. DOI:10.7507/1001-5515.202002029. [21] 门阔,戴建荣. 利用深度反卷积神经网络自动勾画放疗危及器官[J]. 中国医学物理学杂志, 2018, 35(3):256-259. DOI:10.3969/j.issn.1005-202X.2018.03.002. Men K, Dai JR. Automatic segmentation of organs at risk in radiotherapy using deep deconvolutional neural network[J]. Chin J Med Phys,2018, 35(3):256-259. DOI:10.3969/j.issn.1005-202X.2018.03.002. [22] 王敏,秦伟,时飞跃,等. 智能放疗云平台自动勾画宫颈癌患者膀胱结构的应用研究[J]. 中国数字医学, 15(7):85-88. DOI:10.3969/j.issn.1673-7571.2020.07.028. Wang M, Qin W, Shi FY, et,al. Research on the Application of Automatic Contouring of the Bladder Structure of Patients with Cervical Cancer by the Intelligent Radiotherapy Cloud Platform[J]. China Digital Medicine,15(7):85-88. DOI:10.3969/j.issn.1673-7571.2020.07.028. [23] 曾靓,钟仁明. MR引导放疗在胰腺癌中的应用[J]. 中华放射肿瘤学杂志,2021, 30(2):204-207. DOI:10.3760/cma.j.cn113030-20200810-00408. Zeng L, Zhong RM. Application of MR-guided radiotherapy for pancreatic cancer[J]. Chin J Radiat Oncol,2021, 30(2):204-207. DOI:10.3760/cma.j.cn113030-20200810-00408.
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