Sibolt Patrik, Andersson Lina M, Calmels Lucie, Sjöström David, Bjelkengren Ulf, Geertsen Poul, Behrens Claus F
Department of Oncology, Herlev & Gentofte Hospital, Herlev, Denmark.
Phys Imaging Radiat Oncol. 2020 Dec 18;17:1-7. doi: 10.1016/j.phro.2020.12.004. eCollection 2021 Jan.
Studies have demonstrated the potential of online adaptive radiotherapy (oART). However, routine use has been limited due to resource demanding solutions. This study reports on experiences with oART in the pelvic region using a novel cone-beam computed tomography (CBCT)-based, artificial intelligence (AI)-driven solution.
Automated pre-treatment planning for thirty-nine pelvic cases (bladder, rectum, anal, and prostate), and one hundred oART simulations were conducted in a pre-clinical release of Ethos (Varian Medical Systems, Palo Alto, CA). Plan quality, AI-segmentation accuracy, oART feasibility and an integrated calculation-based quality assurance solution were evaluated. Experiences from the first five clinical oART patients (three bladder, one rectum and one sarcoma) are reported.
Auto-generated pre-treatment plans demonstrated similar planning target volume (PTV) coverage and organs at risk doses, compared to institution reference. More than 75% of AI-segmentations during simulated oART required none or minor editing and the adapted plan was superior in 88% of cases. Limitations in AI-segmentation correlated to cases where AI model training was lacking. The five first treated patients complied well with the median adaptive procedure duration of 17.6 min (from CBCT acceptance to treatment delivery start). The treated bladder patients demonstrated a 42% median primary PTV reduction, indicating a 24%-30% reduction in V to the bowel cavity, compared to non-ART.
A novel commercial oART solution was demonstrated feasible for various pelvic sites. Clinically acceptable AI-segmentation and auto-planning enabled adaptation within reasonable timeslots. Possibilities for reduced PTVs observed for bladder cancer indicated potential for toxicity reductions.
研究已证明在线自适应放疗（oART）的潜力。然而，由于资源需求大的解决方案，其常规应用受到限制。本研究报告了使用基于新型锥形束计算机断层扫描（CBCT）的人工智能（AI）驱动解决方案在盆腔区域进行oART的经验。
在Ethos（瓦里安医疗系统公司，加利福尼亚州帕洛阿尔托）的临床前版本中，对39例盆腔病例（膀胱、直肠、肛门和前列腺）进行了自动预处理计划，并进行了100次oART模拟。评估了计划质量、AI分割准确性、oART可行性以及基于综合计算的质量保证解决方案。报告了前五例临床oART患者（三例膀胱、一例直肠和一例肉瘤）的经验。
与机构参考相比，自动生成的预处理计划显示出相似的计划靶区（PTV）覆盖范围和危及器官剂量。在模拟oART期间，超过75%的AI分割无需或只需少量编辑，并且在88%的病例中，调整后的计划更优。AI分割的局限性与缺乏AI模型训练的病例相关。前五例接受治疗的患者很好地遵守了17.6分钟的中位自适应程序持续时间（从CBCT接受至开始治疗）。接受治疗的膀胱患者的原发性PTV中位数降低了42%，表明与非ART相比，肠腔V降低了24%-30%。
一种新型的商用oART解决方案被证明对各种盆腔部位可行。临床上可接受的AI分割和自动计划能够在合理的时间范围内实现自适应。观察到膀胱癌PTV降低的可能性表明有降低毒性的潜力。
