Artificial Intelligence-Based Automated Treatment Planning of Postmastectomy Volumetric Modulated Arc Radiotherapy - Jiang, Shengpeng; Xue, Yi; Li, Ming; Yang, Chengwen; Zhang, Daguang; Wang, Qingxin; Wang, Jing; Chen, Jie; You, Jinqiang; Yuan, Zhiyong; Wang, Xiaochun; Zhang, Xiaodong; et al. (Frontiers in Oncology, 2022)
As a useful tool, artificial intelligence has surpassed human beings in many fileds. Artificial intelligence based automated radiotherapy planning strategy is proposed in lots of cancer sites and is the future of treatment planning. Postmastectomy radiotherapy (PMRT) decreases local recurrence probability and improves overall survival and volumetric modulated arc therapy (VMAT) has gradually become the mainstream technique of radiotherapy. However, there is few customized effective automated treatment planning scheme for postmastectomy VMAT so far. This study investigated an artificial intelligence algorithm based automated planning using the M.D. Anderson Cancer Center autoplan (MDAP) system and Pinnacle treatment planning system, to effectively generate high quality postmastectomy VMAT plans. In this study, 20 patients treated with PMRT were retrospectively investigated, including 10 left- and 10 right-sided postmastectomy patients. Chest wall, supraclavicular, subclavicular and internal mammary region were delineated as target volume by radiotherapy oncologists, and 50 Gy in 25 fractions was prescribed. Organs at risk including heart, spinal cord, left lung, right lung and lungs were also contoured. All patients were planned with VMAT using 2 arcs. An optimization objective template was summarized based on the dose of clinical plans and requirements from oncologists. Several treatment planning parameters were investigated using an artificial intelligence algorithm, including collimation angle, jaw collimator mode, gantry spacing resolution (GSR), and number of start optimization times. The treatment planning parameters with best performance or most preferred were applied to the automated treatment planning method. Dosimetric index of automated treatment plans (autoplans) and manual clinical plans were compared by paired t-test. The jaw tracking mode, 2 degree GSR and 3 rounds of optimization were selected in all the PMRT autoplans. Additionally, the 350 and 10 degree collimation angle were selected in the left- and right-sided PMRT autoplans, respectively. The uniformity index and conformity index of planning target volume, mean heart dose, spinal cord D0.03cc, and mean lung dose, V5Gy and V20Gy of lung of autoplans were significant better compared with the manual clinical plans. An artificial intelligence based automated treatment planning method for postmastectomy VMAT has been developed to ensure plan quality and improve clinical efficiency.