Enabling clinical use of linear energy transfer in proton therapy for head and neck cancer - A review of implications for treatment planning and adverse events study.
Jingyuan Chen, Yunze Yang, Hongying Feng, Chenbin Liu, Lian Zhang, Jason M Holmes, Zhengliang Liu, Haibo Lin, Tianming Liu, Charles B Simone, Nancy Y Lee, Steven J Frank, Daniel J Ma, Samir H Patel, Wei Liu
{"title":"Enabling clinical use of linear energy transfer in proton therapy for head and neck cancer - A review of implications for treatment planning and adverse events study.","authors":"Jingyuan Chen, Yunze Yang, Hongying Feng, Chenbin Liu, Lian Zhang, Jason M Holmes, Zhengliang Liu, Haibo Lin, Tianming Liu, Charles B Simone, Nancy Y Lee, Steven J Frank, Daniel J Ma, Samir H Patel, Wei Liu","doi":"10.1051/vcm/2025001","DOIUrl":null,"url":null,"abstract":"<p><p>Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. In conclusion, translating the advances in LET-related research into clinical practice necessitates a better understanding of the LET-related biological mechanisms and the development of clinically relevant LET-related volume constraints directly derived from the clinical outcomes. Future research is needed to refine these models and conduct prospective trials to assess the clinical benefits of LET-guided optimization on patient outcomes.</p>","PeriodicalId":520485,"journal":{"name":"Visualized cancer medicine","volume":"6 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11945436/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visualized cancer medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/vcm/2025001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/20 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. In conclusion, translating the advances in LET-related research into clinical practice necessitates a better understanding of the LET-related biological mechanisms and the development of clinically relevant LET-related volume constraints directly derived from the clinical outcomes. Future research is needed to refine these models and conduct prospective trials to assess the clinical benefits of LET-guided optimization on patient outcomes.
质子疗法因其独特的物理和生物特性(尤其是布拉格峰)而具有显著优势,可在不损伤健康组织的情况下将剂量精确输送到肿瘤。然而,相对生物效应(RBE)被过度简化为 1.1 的固定值,这并没有考虑到剂量、线性能量传递(LET)和生物终点之间复杂的相互作用,给临床实施带来了挑战。缺乏异质性控制或对复杂的相互作用缺乏了解,可能会导致意想不到的不良事件和不理想的患者预后。另一方面,扩大我们对可变肿瘤 RBE 和 LET 优化的了解可能会为放射抗性肿瘤提供更好的管理策略。本综述探讨了 LET 计算方法的最新进展,包括分析模型和蒙特卡罗模拟。对将 LET 纳入计划评估以加强计划质量控制进行了评估。以 LET 为指导的稳健优化有望最大限度地减少高危器官的高 LET 暴露,从而降低不良事件的风险。讨论了剂量测定种子点分析,以显示其在不良事件初始化时通过寻找病变起源和消除生物过程中的干扰因素来揭示与 LET 相关的真实影响的重要性。讨论了剂量-LET 体积直方图(DLVH),它是将物理剂量和 LET 与临床结果相关联的有效工具,能够推导出与临床相关的剂量-LET 体积约束,而无需依赖不确定的 RBE 模型。在 DLVH 的基础上,引入了剂量-LET 容积约束(DLVC)引导的稳健优化,以升级传统的基于剂量-容积约束的稳健优化,该方法可同时优化剂量和 LET 的联合分布。总之,要将 LET 相关研究的进展转化为临床实践,就必须更好地理解 LET 相关的生物学机制,并开发出直接源自临床结果、与临床相关的 LET 相关体积约束。未来的研究需要完善这些模型并开展前瞻性试验,以评估 LET 引导的优化对患者预后的临床益处。