Department of Radiation Oncology, University of Kansas Cancer Center, Kansas City, KS, USA.
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
J Appl Clin Med Phys. 2023 Jun;24(6):e13940. doi: 10.1002/acm2.13940. Epub 2023 Feb 24.
Knowledge-based planning (KBP) and multicriteria optimization (MCO) are two powerful tools to assist treatment planners in achieving optimal target coverage and organ-at-risk (OAR) sparing. The purpose of this work is to investigate if integrating MCO with conventional KBP can further improve treatment plan quality for prostate cancer stereotactic body radiation therapy (SBRT). A two-phase study was designed to investigate the impact of MCO and KBP in prostate SBRT treatment planning. The first phase involved the creation of a KBP model based on thirty clinical SBRT plans, generated by manual optimization (KBP_M). A ten-patient validation cohort was used to compare manual, MCO, and KBP_M optimization techniques. The next phase involved replanning the original model cohort with additional tradeoff optimization via MCO to create a second model, KBP_MCO. Plans were then generated using linear integration (KBP_M+MCO), non-linear integration (KBP_MCO), and a combination of integration methods (KBP_MCO+MCO). All plans were analyzed for planning target volume (PTV) coverage, OAR constraints, and plan quality metrics. Comparisons were generated to evaluate plan and model quality. Phase 1 highlighted the necessity of KBP and MCO in treatment planning, as both optimization methods improved plan quality metrics (Conformity and Heterogeneity Indices) and reduced mean rectal dose by 2 Gy, as compared to manual planning. Integrating MCO with KBP did not further improve plan quality, as little significance was seen over KBP or MCO alone. Principal component score (PCS) fitting showed KBP_MCO improved bladder and rectum estimated and modeled dose correlation by 5% and 22%, respectively; however, model improvements did not significantly impact plan quality. KBP and MCO have shown to reduce OAR dose while maintaining desired PTV coverage in this study. Further integration of KBP and MCO did not show marked improvements in treatment plan quality while requiring increased time in model generation and optimization time.
基于知识的计划（KBP）和多准则优化（MCO）是两种强大的工具，可以帮助治疗计划者实现最佳的靶区覆盖和危及器官（OAR）保护。本研究旨在探讨将 MCO 与传统的 KBP 相结合是否可以进一步提高前列腺癌立体定向体部放射治疗（SBRT）的治疗计划质量。本研究采用两阶段设计来研究 MCO 和 KBP 在前列腺 SBRT 治疗计划中的影响。第一阶段基于 30 例手动优化（KBP_M）生成的临床 SBRT 计划，建立 KBP 模型。使用 10 例验证队列患者比较手动、MCO 和 KBP_M 优化技术。下一阶段通过 MCO 进行额外的折衷优化，重新规划原始模型队列，创建第二个模型 KBP_MCO。然后使用线性整合（KBP_M+MCO）、非线性整合（KBP_MCO）和整合方法的组合（KBP_MCO+MCO）生成计划。对所有计划进行计划靶区（PTV）覆盖、OAR 限制和计划质量指标的分析。生成比较来评估计划和模型质量。第一阶段强调了 KBP 和 MCO 在治疗计划中的必要性，因为这两种优化方法都提高了计划质量指标（适形度和均匀性指数），与手动计划相比，平均直肠剂量降低了 2Gy。与 KBP 或 MCO 单独使用相比，将 MCO 与 KBP 整合并没有进一步提高计划质量，因为没有显著的意义。主成分得分（PCS）拟合显示，KBP_MCO 分别将膀胱和直肠的估计和模拟剂量相关性提高了 5%和 22%；然而，模型改进并没有显著影响计划质量。在本研究中，KBP 和 MCO 已被证明可以降低 OAR 剂量，同时保持所需的 PTV 覆盖。虽然需要更多的时间来生成和优化模型，但 KBP 和 MCO 的进一步整合并没有显示出在治疗计划质量方面的显著改善。
