Department of Radiation Oncology, University of Rochester Medical Center, New York, New York, USA.
Department of Radiation Oncology, University of Toledo Medical Center, Toledo, Ohio, USA.
J Appl Clin Med Phys. 2024 Sep;25(9):e14440. doi: 10.1002/acm2.14440. Epub 2024 Jun 19.
CBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.
Dosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.
MU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.
MU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.
通过使用人工智能和自动化技术,CBCT 引导的在线自适应放疗(oART)系统得以实现,这大大减少了在在线自适应治疗期间的治疗计划时间。在自适应治疗期间评估生成的计划对临床团队来说具有很大的挑战性,因为计划过程被压缩到比离线计划更短的时间窗口内。我们在几个 oART 治疗期间发现自适应计划与参考计划之间的 MU 变化差异高达 30%,这导致临床团队对 oART 剂量计算的准确性提出了质疑。当 MU 变化不必要地很大时,我们调查了 MU 变化的原因以及剂量传递的整体准确性。
收集了 19 名接受 CBCT 引导 oART 的患者的 604 次自适应治疗的剂量学和自适应计划数据。分析包括每个分次的 MU 总数、计划靶区(PTV)和危及器官(OAR)的体积、PTV-OAR 重叠的变化以及剂量体积直方图(DVH)曲线。将 MU 变化大于平均值两个标准差的治疗 session 离线重新优化,由独立的计算系统验证,并使用探测器阵列进行测量。
MU 变化与参考计划之间的关系呈正态分布,平均值为-1.0%,标准差为 11.0%。未发现 MU 变化与解剖变化之间存在显著相关性。离线重新优化并不能可靠地重现参考或在线 MU 总数,这表明 oART 优化器中的随机效应可能是导致变化的原因。独立剂量计算和探测器阵列测量结果与计划剂量具有可接受的一致性。
在 oART 计划之间观察到的 MU 变化不是 oART 工作流程中的任何错误引起的。临床医生不应将 MU 变异性作为表达他们对治疗计划准确性的信心的一种方式。在线自适应治疗期间的临床决策应依赖于经过验证的二次剂量计算,以确保选择最佳的治疗计划。