Ionization Detail - Biologically based treatment planning for particle therapy beyond LET-RBE

电离细节 - LET-RBE 之外的基于生物学的粒子治疗治疗计划

• 批准号: 10689288
• 负责人: BRUCE FADDEGON
• 金额: $ 58.17万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689288
• 关键词: 3-Dimensional; Address; Aerobic; Algorithms; Anatomy; Animals; Artificial Intelligence; Biological; Biological Models; Biometry; Cancer Patient; Cancerous; Carbon; Cell Line; Cells; Charge; Chordoma; Clinical; Clinical Data; Collaborations; Communities; Complication; Computational Technique; Computing Methodologies; Consensus; Custom; Data; Data Set; Development; Disease; Dose; Effectiveness; European; Evaluation; Event; Goals; Head; High-LET Radiation; Human; Human Cell Line; Hypoxia; Implementation readiness; In Vitro; Ions; Japanese; Knowledge; Linear Energy Transfer; Machine Learning; Mammalian Cell; Measurement; Measures; Medical; Methods; Modeling; Modernization; Molecular; Monte Carlo Method; Mus; Normal tissue morphology; Organ; Patients; Pattern; Pelvis; Performance; Photons; Physics; Probability; Procedures; Prostate; Protons; Radiation Oncology; Radiation therapy; Radiobiology; Radiology Specialty; Relative Biological Effectiveness; Research; Research Design; Rodent; Roentgen Rays; Scanning; Science; Structure; Techniques; Technology; Testing; Therapy trial; Tissues; Validation; absorption; cancer cell; clinical application; clinical efficacy; clinical implementation; clinical practice; computer science; design; dosimetry; flasks; improved; in vivo; innovation; ionization; irradiation; mathematical algorithm; mathematical sciences; member; nanoscale; novel; optimal treatments; particle; particle beam; particle therapy; predictive modeling; prospective; prostate cancer cell; radiation response; response; side effect; simulation; treatment planning; tumor; tumor xenograft

Project Summary Current proton and ion therapy treatment planning procedures utilize either the physical quantity linear energy transfer (LET) as a surrogate for biological effectiveness or make use of relative biological effectiveness (RBE) models that convert absorbed dose to biologically weighted dose, assumed to be iso- effective to photons. LET is indeed important clinically for planning treatments with charged particles, but there are known problems. Ion beams with the same LET can have different RBE, depending on particle type and energy. Therefore, LET by itself is not an ideal parameter to use in radiation treatment planning (RTP). For clinical application of carbon therapy, RBE-models have been developed. However, comparisons of different RBE models used for carbon therapy have shown that dose prescriptions implemented with the European local effect model or the Japanese National Institute of Radiological Sciences mixed beam model can be up to 15% different. We use the term ionization detail (ID) to mean the detailed distribution of ionizing events along a particle track on the nanometer scale. Our chief hypothesis, which is supported by strong prior evidence, is that ID can predict, better than LET and existing RBE models, the biological effects associated with high-LET radiation. We have previously shown how ID can be used together with these models to improve their performance, providing a path for integrating ID-based RTP into clinical practice. Our approach could lead to a consensus in proton and ion therapy RTP. With four Specific Aims, we have chosen a translational and stepwise approach to build an ID-based prediction model. We will test this model for different endpoints and model systems ranging from in vitro cell and molecular data, obtained by irradiating human cancer cells in flasks and anatomical phantoms, to in vivo mice/human tumor data. We will develop advanced algorithms and computational GPU- based methods and use them for effective inverse treatment planning with actively scanned proton and ion beams. This technology will be applied to demonstrate the practicality and evaluate the clinical efficacy of our approach in prostate and chordoma treatments, first prospectively in human-size pelvis and head phantoms, and finally, retrospectively in patients treated for these diseases. We have assembled a strong team with the complementary expertise needed for this project. Members of our team have all successfully collaborated together. Upon completion, we will provide a rigorously tested and validated approach to ID-based particle RTP that will be available for cross-correlation with existing clinical data and for careful testing in prospective clinical particle therapy trials.

项目概要 当前的质子和离子治疗治疗计划程序利用物理量线性 能量转移（LET）作为生物有效性的替代品或利用相关的生物效应 有效性（RBE）模型，将吸收剂量转换为生物加权剂量，假设是iso- 对光子有效。 LET 在临床上对于规划带电粒子治疗确实很重要，但是 存在已知问题。具有相同 LET 的离子束可以具有不同的 RBE，具体取决于粒子类型 和能量。因此，LET 本身并不是放射治疗计划 (RTP) 中使用的理想参数。 为了碳疗法的临床应用，已经开发了 RBE 模型。然而，比较 用于碳疗法的不同 RBE 模型表明，采用 欧洲局部效应模型或日本国立放射线科学研究所混合光束模型 差异最多可达 15%。我们使用术语“电离细节”(ID) 来表示 沿纳米尺度粒子轨迹的电离事件。我们的主要假设是 有强有力的先验证据支持，ID 可以预测，比 LET 和现有的 RBE 更好 模型，与高 LET 辐射相关的生物效应。我们之前已经展示过如何 ID可以与这些模型一起使用来提高它们的性能，提供集成的途径 基于 ID 的 RTP 进入临床实践。我们的方法可能会在质子和离子治疗 RTP 方面达成共识。 出于四个具体目标，我们选择了一种平移和逐步的方法来构建基于 ID 的 预测模型。我们将针对不同的终点和模型系统测试该模型，范围从体外 通过照射烧瓶和解剖模型中的人类癌细胞获得的细胞和分子数据 体内小鼠/人类肿瘤数据。我们将开发先进的算法和计算GPU- 基于方法，并使用它们通过主动扫描质子和离子进行有效的逆向治疗计划 横梁。该技术将用于展示实用性并评估我们的临床疗效 前列腺和脊索瘤治疗方法，首先前瞻性地用于人体大小的骨盆和头部模型， 最后，对接受这些疾病治疗的患者进行回顾性研究。我们组建了一支强大的团队 该项目所需的补充专业知识。我们团队的成员都已经成功 一起合作。完成后，我们将提供经过严格测试和验证的方法 基于 ID 的粒子 RTP 将可用于与现有临床数据的交叉关联以及 在前瞻性临床粒子治疗试验中进行仔细测试。

项目成果

Superiorization of projection algorithms for linearly constrained inverse radiotherapy treatment planning.

• DOI: 10.3389/fonc.2023.1238824
• 发表时间: 2023
• 期刊: Frontiers in oncology
• 影响因子: 4.7

Ionization detail parameters and cluster dose: a mathematical model for selection of nanodosimetric quantities for use in treatment planning in charged particle radiotherapy.

电离细节参数和簇剂量：用于选择纳米剂量量的数学模型，用于带电粒子放射治疗的治疗计划。

• DOI: 10.1088/1361-6560/acea16
• 发表时间: 2023
• 期刊: Physics in medicine and biology
• 影响因子: 3.5
• 作者: Faddegon,Bruce;Blakely,EleanorA;Burigo,Lucas;Censor,Yair;Dokic,Ivana;DomínguezKondo,Naoki;Ortiz,Ramon;RamosMéndez,José;Rucinski,Antoni;Schubert,Keith;Wahl,Niklas;Schulte,Reinhard
• 通讯作者: Schulte,Reinhard