Lou Zhaoyang, Cheng Chen, Mao Ronghu, Li Dingjie, Tian Lingling, Li Bing, Lei Hongchang, Ge Hong
Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.
Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China.
Phys Med. 2023 May;109:102586. doi: 10.1016/j.ejmp.2023.102586. Epub 2023 Apr 14.
To develop an automated planning approach in Raystation and evaluate its feasibility in multiple clinical application scenarios.
An automated planning approach (Ruiplan) was developed by using the scripting platform of Raystation. Radiotherapy plans were re-generated both automatically by using Ruiplan and manually. 60 patients, including 20 patients with nasopharyngeal carcinoma (NPC), 20 patients with esophageal carcinoma (ESCA), and 20 patients with rectal cancer (RECA) were retrospectively enrolled in this study. Dosimetric and planning efficiency parameters of the automated plans (APs) and manual plans (MPs) were statistically compared.
For target coverage, APs yielded superior dose homogeneity in NPC and RECA, while maintaining similar dose conformity for all studied anatomical sites. For OARs sparing, APs led to significant improvement in most OARs sparing. The average planning time required for APs was reduced by more than 43% compared with MPs. Despite the increased monitor units (MUs) for NPC and RECA in APs, the beam-on time of APs and MPs had no statistical difference. Both the MUs and beam-on time of APs were significantly lower than that of MPs in ESCA.
This study developed a new automated planning approach, Ruiplan, it is feasible for multi-treatment techniques and multi-anatomical sites cancer treatment planning. The dose distributions of targets and OARs in the APs were similar or better than those in the MPs, and the planning time of APs showed a sharp reduction compared with the MPs. Thus, Ruiplan provides a promising approach for realizing automated treatment planning in the future.
在Raystation中开发一种自动计划方法,并评估其在多种临床应用场景中的可行性。
利用Raystation的脚本平台开发了一种自动计划方法(Ruiplan)。分别使用Ruiplan自动重新生成和手动重新生成放射治疗计划。本研究回顾性纳入了60例患者,包括20例鼻咽癌(NPC)患者、20例食管癌(ESCA)患者和20例直肠癌(RECA)患者。对自动计划(APs)和手动计划(MPs)的剂量学和计划效率参数进行统计学比较。
在靶区覆盖方面,APs在NPC和RECA中产生了更好的剂量均匀性,同时在所有研究的解剖部位保持了相似的剂量适形性。在危及器官保护方面,APs在大多数危及器官保护方面有显著改善。与MPs相比,APs所需的平均计划时间减少了40%以上。尽管APs中NPC和RECA的机器跳数(MUs)增加,但APs和MPs的照射时间无统计学差异。在ESCA中,APs的MUs和照射时间均显著低于MPs。
本研究开发了一种新的自动计划方法Ruiplan,它对于多种治疗技术和多解剖部位的癌症治疗计划是可行的。APs中靶区和危及器官的剂量分布与MPs相似或更好,且APs的计划时间与MPs相比大幅减少。因此,Ruiplan为未来实现自动治疗计划提供了一种有前景的方法。