Szalkowski Gregory, Xu Xuanang, Das Shiva, Yap Pew-Thian, Lian Jun
Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.
Department of Radiation Oncology, Stanford University, Stanford, California.
Adv Radiat Oncol. 2024 Oct 9;9(12):101649. doi: 10.1016/j.adro.2024.101649. eCollection 2024 Dec.
This study investigated the applicability of 3-dimensional dose predictions from a model trained on one modality to a cross-modality automated planning workflow. Additionally, we explore the impact of integrating a multicriteria optimizer (MCO) on adapting predictions to different clinical preferences.
Using a previously created 3-stage U-Net in-house model trained on the 2020 American Association of Physicists in Medicine OpenKBP challenge data set (340 head and neck plans, all planned using 9-field static intensity modulated radiation therapy [IMRT]), we retrospectively generated dose predictions for 20 patients. These dose predictions were, in turn, used to generate deliverable IMRT, VMAT, and tomotherapy plans using the fallback plan functionality in Raystation. The deliverable plans were evaluated against the dose predictions based on primary clinical goals. A new set of plans was also generated using MCO-based optimization with predicted dose values as constraints. Delivery QA was performed on a subset of the plans to assure clinical deliverability.
The mimicking approach accurately replicated the predicted dose distributions across different modalities, with slight deviations in the spinal cord and external contour maximum doses. MCO optimization significantly reduced doses to organs at risk, which were prioritized by our institution while maintaining target coverage. All tested plans met clinical deliverability standards, evidenced by a gamma analysis passing rate >98%.
Our findings show that a model trained only on IMRT plans can effectively contribute to planning across various modalities. Additionally, integrating predictions as constraints in an MCO-based workflow, rather than direct dose mimicking, enables a flexible, warm-start approach for treatment planning, although the benefit is reduced when the training set differs significantly from an institution's preference. Together, these approaches have the potential to significantly decrease plan turnaround time and quality variance, both at high-resource medical centers that can train in-house models and smaller centers that can adapt a model from another institution with minimal effort.
本研究调查了一个在一种模态上训练的模型所做的三维剂量预测在跨模态自动计划工作流程中的适用性。此外，我们探讨了整合多标准优化器（MCO）对使预测适应不同临床偏好的影响。
我们使用先前创建的在2020年美国医学物理学家协会开放KBP挑战数据集（340例头颈部计划，均采用9野静态调强放射治疗[IMRT]进行计划）上训练的3阶段内部U-Net模型，对20例患者进行回顾性剂量预测。这些剂量预测反过来又用于使用Raystation中的后备计划功能生成可交付的IMRT、容积调强弧形治疗（VMAT）和断层放疗计划。根据主要临床目标，将可交付计划与剂量预测进行比较评估。还使用基于MCO的优化方法，以预测剂量值为约束条件生成了一组新的计划。对一部分计划进行了交付质量保证，以确保临床可交付性。
模拟方法准确地复制了不同模态下的预测剂量分布，脊髓和外部轮廓最大剂量存在轻微偏差。MCO优化显著降低了危及器官的剂量，我们机构对这些器官进行了优先排序，同时保持了靶区覆盖。所有测试计划均符合临床可交付性标准，伽马分析通过率>98%证明了这一点。
我们的研究结果表明，仅在IMRT计划上训练的模型可以有效地为各种模态的计划做出贡献。此外，将预测作为基于MCO的工作流程中的约束条件，而不是直接剂量模拟，能够实现一种灵活的、热启动的治疗计划方法，尽管当训练集与机构的偏好有显著差异时，这种益处会降低。总之，这些方法有可能显著减少计划周转时间和质量差异，无论是在能够训练内部模型的高资源医疗中心，还是在能够轻松采用其他机构模型的较小中心。
