Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
Int J Radiat Oncol Biol Phys. 2018 Mar 15;100(4):1067-1074. doi: 10.1016/j.ijrobp.2017.12.276. Epub 2018 Jan 4.
To use knowledge-based planning (KBP) as a method of producing high-quality, consistent, protocol-compliant treatment plans in a complex setting of spine stereotactic body radiation therapy on NRG Oncology Radiation Therapy Oncology Group (RTOG) 0631.
An internally developed KBP model was applied to an external validation cohort of 22 anonymized cases submitted under NRG Oncology RTOG 0631. The original and KBP plans were compared via their protocol compliance, target conformity and gradient index, dose to critical structures, and dose to surrounding normal tissues.
The KBP model generated plans meeting all protocol objectives in a single optimization when tested on both internal and protocol-submitted NRG Oncology RTOG 0631 cases. Two submitted plans that were considered to have a protocol-unacceptable deviation were made protocol compliant through the use of the model. There were no statistically significant differences in protocol spinal cord metrics (D10% and D0.03cc) between the manually optimized plans and the KBP plans. The volume of planning target volume receiving prescription dose increased from 93.3% ± 3.2% to 98.3% ± 1.4% (P = .01) when using KBP. High-dose spillage to surrounding normal tissues (V105%) showed no significant differences (2.1 ± 7.3 cm for manual plans to 1.8 ± 0.6 cm with KBP), and dosimetric outliers with large amounts of spillage were eliminated through the use of KBP. Knowledge-based planning plans were also found to be significantly more consistent in several metrics, including target coverage and high dose outside of the target.
Incorporation of KBP models into the clinical trial setting may have a profound impact on the quality of trial results, owing to the increase in consistency and standardization of planning, especially for treatment sites or techniques that are nonstandard.
在 NRG 肿瘤放射治疗肿瘤学组（RTOG）0631 脊柱立体定向体部放射治疗这一复杂环境中，利用基于知识的计划（KBP）方法生成高质量、一致且符合方案的治疗计划。
将内部开发的 KBP 模型应用于 NRG 肿瘤 RTOG 0631 提交的 22 例匿名外部验证队列中。通过比较协议符合度、靶区适形度和梯度指数、危及器官剂量和周围正常组织剂量，比较原始计划和 KBP 计划。
当对内部和协议提交的 NRG 肿瘤 RTOG 0631 病例进行测试时，KBP 模型在单次优化中生成了符合所有方案目标的计划。通过使用该模型，使两个被认为不符合方案的提交计划符合方案要求。手动优化计划和 KBP 计划之间，脊髓的协议指标（D10%和 D0.03cc）没有统计学上的显著差异。当使用 KBP 时，计划靶区接受处方剂量的体积从 93.3%±3.2%增加到 98.3%±1.4%（P=0.01）。周围正常组织的高剂量外溢（V105%）没有显著差异（手动计划为 2.1±7.3cm，KBP 为 1.8±0.6cm），并且通过使用 KBP 消除了大量外溢的剂量学离群值。还发现 KBP 计划在几个指标上更加一致，包括靶区覆盖和靶区外的高剂量。
将 KBP 模型纳入临床试验环境可能会对试验结果的质量产生深远影响，因为计划的一致性和标准化得到了提高，特别是对于非标准的治疗部位或技术。
