Novel Optimization Methods and Treatment Planning System for Clinically-Deliverable Truly-Hybrid Proton-Photon Radiotherapy

用于临床可交付的真正混合质子-光子放射治疗的新型优化方法和治疗计划系统

• 批准号: 10207870
• 负责人: Hao Gao
• 金额: $ 0.42万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2021-06-11
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207870
• 关键词: Automobile Driving; Bathing; Cancer Center; Cancer Patient; Clinic; Clinical; Clinical Trials; Devices; Dose; Equipment; Hospitals; Hour; Hybrids; Immobilization; Individual; Infrastructure; Joints; Methods; Modeling; Organ; Outcome; Patients; Photons; Physicians; Procedures; Protons; Radiation therapy; Radiotherapy Research; Recording of previous events; Risk; Speed; Structure; System; Technology; Testing; Toxic effect; Travel; Treatment Cost; Uncertainty; Work; base; cancer radiation therapy; clinical application; deep learning; improved; innovation; novel; prospective; simulation; success; treatment planning; treatment site

Project Summary Radiotherapy (RT) aims to deliver tumoricidal dose to clinical target volume (CTV) while sparing organs at risk (OAR), for which proton and photon beams are naturally complementary to each other: protons are generally better for OAR sparing, while photons are more robust to delivery uncertainties for CTV coverage. The hybrid proton-photon RT has a long history. However, as it generates proton and photon plans separately without fully utilizing joint proton-photon optimization during the planning stage, current hybrid RT is pseudo- hybrid and very limited in plan quality, treatment sites, and broad applicability. The key to leapfrog from pseudo-hybrid to truly-hybrid RT is new joint proton-photon optimization method that synergizes complementary proton and photon beams. The hypothesis is that truly-hybrid RT via appropriate joint proton- photon optimization will be more favorable than proton or photon-only RT, in terms of CTV coverage robustness and OAR sparing optimality. Broad applicability of truly-hybrid RT to patients: (A) Clinical applicability: unlike pseudo-hybrid RT that is limited in plan quality and treatment sites, truly-hybrid RT may become a new paradigm for general cancer RT, owing to its superior plan quality and thus potentially clinical outcomes to proton-only or photon-only RT. (B) Clinical workflow: our truly-hybrid plans can be individually and safely delivered on existing proton and photon machines, and this effort envisions patients being treated in an integrated cancer center like ours with both proton and photon equipment, under the direction of a single physician, using shared immobilization devices, simulation procedure and structure set, and integrated treatment planning and delivery system. (C) Patient coverage: truly-hybrid RT can be made broadly available to many cancer patients through existing infrastructures in US, since (1) most hospitals with proton centers also have photon centers; (2) 76% of cancer patients live in the states with operational proton centers, while 85% are within 100-mile (2-hour-driving) distances to these proton centers; (3) cancer patients are more willing to travel for advanced treatment options. Proposed effort: Inspired by unprecedented plan quality and broad applicability of truly-hybrid RT via our joint proton-photon optimization method, the next step is to test the hypothesis prospectively via clinical trials. However, a missing prerequisite to advance truly-hybrid RT from research to clinic is a treatment planning system (TPS) that can generate clinically-deliverable hybrid plans. To meet this urgent need, this effort will develop novel optimization methods and TPS for clinically-deliverable truly-hybrid RT, which is a radical step towards prospective clinical trials for testing the hypothesis. Aim 1: Optimization methods and TPS for clinically-deliverable truly-hybrid RT. Aim 2: Optimization methods for accurate and efficient MCO truly-hybrid planning. Aim 3: Deep learning based optimization methods for efficient truly-hybrid planning.

项目摘要 放射治疗（RT）旨在将杀肿瘤剂量递送至临床靶体积（CTV），同时在 风险（OAR），质子和光子束自然互补：质子是 通常更好的OAR备用，而光子更强大的交付不确定性的CTV覆盖。 质子-光子混合RT有着悠久的历史。然而，由于它分别产生质子和光子计划， 如果在规划阶段没有充分利用联合质子-光子优化，当前的混合RT是伪 混合且在计划质量、治疗部位和广泛适用性方面非常有限。跨越式发展的关键 伪杂化到真杂化RT是一种新的联合质子-光子优化方法， 互补质子和光子束。假设是，通过适当的联合质子， 在CTV覆盖方面，光子优化将比质子或仅光子RT更有利 鲁棒性和OAR节约最优性。 真混合RT对患者的广泛适用性：（A）临床适用性：与假混合RT不同， 受计划质量和治疗部位的限制，真混合RT可能成为一般癌症RT的新范例， 这是由于其上级计划质量以及因此相对于仅质子或仅光子RT的潜在临床结果。 (B)临床工作流程：我们的纯混合计划可以单独和安全地交付现有的质子和 光子机器，这一努力设想患者在像我们这样的综合癌症中心接受治疗， 质子和光子设备，在一个医生的指导下，使用共享固定 设备、模拟程序和结构集以及综合治疗计划和实施系统。 (C)患者覆盖范围：通过现有的技术， 美国的基础设施，因为（1）大多数拥有质子中心的医院也有光子中心;（2）76%的癌症 患者居住在质子中心运营的州，而85%的患者在100英里（2小时车程）内 这些质子中心的距离;（3）癌症患者更愿意旅行以获得先进的治疗选择。 建议的努力：灵感来自于前所未有的计划质量和真正的混合RT的广泛适用性， 联合质子-光子优化方法，下一步是通过临床试验前瞻性地测试假设。 然而，将真混合RT从研究推进到临床的一个缺失的先决条件是治疗计划 系统（TPS），可以生成临床交付的混合计划。为了满足这一迫切需要，这一努力将 为临床可交付的真正混合RT开发新的优化方法和TPS，这是一个根本性的步骤 进行前瞻性的临床试验来验证这个假设。 目的1：优化方法和TPS的临床交付truly-hybrid RT。 目标2：精确高效的MCO纯混合规划的优化方法。 目标3：基于深度学习的优化方法，用于高效的真混合规划。