Duffy Seth R, Zheng Yiran, Muenkel Jessica, Ellis Rodney J, Baig Tanvir N, Krancevic Brian, Langmack Christian B, Kelley Kevin D, Choi Serah
Radiation Oncology, University Hospital Cleveland Medical Center, Cleveland, OH, USA.
Radiation Oncology, Penn State Health, Hershey, PA, USA.
J Appl Clin Med Phys. 2020 Dec;21(12):263-271. doi: 10.1002/acm2.13102. Epub 2020 Dec 3.
PURPOSE/OBJECTIVES: The purpose of this study is to dually evaluate the effectiveness of PlanIQ in predicting the viability and outcome of dosimetric planning in cases of complex re-irradiation as well as generating an equivalent plan through Pinnacle integration. The study also postulates that a possible strength of PlanIQ lies in mitigating pre-optimization uncertainties tied directly to dose overlap regions where re-irradiation is necessary.
A retrospective patient selection (n = 20) included a diverse range of re-irradiation cases to be planned using Pinnacle auto-planning with PlanIQ integration. A consistent planning template was developed and applied across all cases. Direct plan comparisons of manual plans against feasibility-produced plans were performed by physician(s) with dosimetry recording relevant proximal OAR and planning timeline data.
All re-irradiation cases were successfully predicted to be achievable per PlanIQ analyses with three cases (3/20) necessitating 95% target coverage conditions, previously exhibited in the manually planned counterparts, and determined acceptable under institutional standards. At the same time, PlanIQ consistently produced plans of equal or greater quality to the previously manually planned re-irradiation across all (20/20) trials (P = 0.05). Proximal OAR exhibited similar to slightly improved maximum point doses from feasibility-based planning with the largest advantages gained found within the subset of cranial and spine overlap cases, where improvements upward of 10.9% were observed. Mean doses to proximal tissues were found to be a statistically significant (P < 0.05) 5.0% improvement across the entire study. Documented planning times were markedly less than or equal to the time contributed to manual planning across all cases.
Initial findings indicate that PlanIQ effectively provides the user clear feasibility feedback capable of facilitating decision-making on whether re-irradiation dose objectives and prescription dose coverage are possible at the onset of treatment planning thus eliminating possible trial and error associated with some manual planning. Introducing model-based prediction tools into planning of complex re-irradiation cases yielded positive outcomes on the final treatment plans.
目的/目标：本研究的目的是双重评估PlanIQ在预测复杂再照射病例中剂量计划的可行性和结果方面的有效性，以及通过Pinnacle集成生成等效计划。该研究还假设PlanIQ的一个潜在优势在于减轻与再照射必要的剂量重叠区域直接相关的预优化不确定性。
一项回顾性患者选择（n = 20）纳入了一系列使用集成PlanIQ的Pinnacle自动计划进行计划的再照射病例。开发了一个一致的计划模板并应用于所有病例。由医生对手动计划与可行性生成的计划进行直接计划比较，并记录相关近端危及器官（OAR）的剂量测定和计划时间线数据。
根据PlanIQ分析，所有再照射病例均成功预测为可实现，其中三例（3/20）需要95%的靶区覆盖条件，这在手动计划的对应病例中也曾出现，并根据机构标准确定为可接受。同时，在所有（20/20）试验中，PlanIQ始终生成质量等于或高于先前手动计划的再照射计划（P = 0.05）。近端OAR的最大点剂量与基于可行性的计划相比，显示出相似或略有改善，在颅骨和脊柱重叠病例子集中优势最大，观察到改善幅度高达10.9%。整个研究中发现近端组织的平均剂量有统计学显著（P < 0.05）的改善达5.0%。记录的计划时间明显少于或等于所有病例中手动计划所花费的时间。
初步结果表明，PlanIQ有效地为用户提供了清晰的可行性反馈，能够在治疗计划开始时促进关于再照射剂量目标和处方剂量覆盖是否可行的决策，从而消除了与某些手动计划相关的可能的反复试验。将基于模型的预测工具引入复杂再照射病例的计划中，对最终治疗计划产生了积极结果。
