Development of a Novel Lung Function Imaging Modality for comprehensive management of lung cancer

开发用于肺癌综合管理的新型肺功能成像模式

• 批准号: 10555187
• 负责人: Edward Castillo
• 金额: $ 39.49万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-29 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555187
• 关键词: Address; Algorithms; Anatomy; Cancer Patient; Characteristics; Chest; Clinic; Clinical; Clinical Data; Clinical Trials; Commercial grade; Computer software; Data; Data Set; Development; Dose; Engineering; Environment; Evaluation; Feasibility Studies; Four-dimensional; Generations; Heterogeneity; Image; Imaging Techniques; Individual; Industrialization; Institution; Life; Lung; Malignant neoplasm of lung; Maps; Methods; Patients; Performance; Physiological; Quality of life; Radiation; Radiation Pneumonitis; Radiation therapy; Research; Resolution; Retrospective Studies; Seminal; Software Tools; Study models; System; Techniques; Toxic effect; Translating; Treatment Side Effects; Validation; automated segmentation; clinical care; clinical practice; clinically significant; community center; early phase trial; experience; four-dimensional computed tomography; image processing; imaging modality; imaging program; improved; improved outcome; industry partner; innovation; knowledge base; machine learning method; novel; novel imaging technique; prospective; pulmonary function; quality assurance; side effect; software development; standard of care; tool; ventilation

Our project proposes an academic-industrial partnership to translate a novel lung function imaging modality into clinical care for lung cancer patients receiving radiation therapy. Lung cancer patients being treated with radiation can experience serious and sometimes life threatening thoracic side effects from treatment. There is emerging data demonstrating that a novel lung function imaging modality can reduce side-effects and improve quality of life for lung cancer patients undergoing radiation therapy. The novel lung function imaging modality, referred to as ‘4DCT-ventilation,’ uses 4DCT data along with image processing techniques to innovatively calculate lung ventilation maps. 4DCT-ventilation can improve outcomes for lung cancer patients by enabling the generation of functional avoidance radiotherapy plans. Functional avoidance uses 4DCT-ventilation to avoid functional portions of the lung, with the hypothesis that reducing dose to functional lung will reduce thoracic side- effects. Our 4DCT-ventilation research has progressed from retrospective studies to an early-phase trial using 4DCT-ventilation for functional avoidance radiotherapy. The early promising toxicity results from 4DCT- ventilation clinical trials is providing a strong rationale for national trials and expanded clinical integration across individual clinics. The problem is that expanded clinical integration of 4DCT-ventilation is currently not possible due to a lack of consistent, efficient, and clinically validated methods. We propose an academic-industry partnership with MIM Software to address these challenges precluding clinical integration of 4DCT-ventilation. The purpose of our study is to develop methods that enable safe, efficient, and clinically validated methods for clinical integration of 4DCT-ventilation functional avoidance. Our overarching hypothesis is that the 4DCT-ventilation functional avoidance innovations we develop will be demonstrated to reduce lung toxicity in clinics with no prior 4DCT-ventilation experience. The project will be carried out in 3 aims. Aim 1 will develop methods that enable automated 4DCT-ventilation calculations including auto-segmentation, statistically-robust calculation methods, and clinically-efficient quality assurance tools. Aim 2 will develop methods for 4DCT- ventilation functional avoidance radiotherapy including image heterogeneity assessment, development of knowledge-based functional planning methods, and evaluation of metrics most critical in reducing toxicity. In conjunction with our industry partner, the developed methods from Aims 1 and 2 will be integrated in a commercial-grade software tool. Aim 3 will evaluate feasibility by assessing whether 4DCT-ventilation functional avoidance can be demonstrated to reduce toxicity in clinics with no prior 4DCT-ventilation experience. Our project will generate both the tools and data needed to provide guidance on how to properly incorporate 4DCT-ventilation into clinical care. The methods and data will culminate in a commercial-grade platform suitable for busy clinics. 4DCT-ventilation has great potential to improve outcomes for lung cancer patients and our project will enable the integration of this novel imaging modality into clinical care.

我们的项目提出了一个学术-产业合作伙伴关系来翻译一种新的肺功能成像 肺癌放射治疗患者的临床护理模式。正在接受治疗的肺癌患者 接受放射治疗可能会出现严重的、有时危及生命的胸腔副作用。那里 正在出现的数据表明，一种新的肺功能成像方式可以减少副作用并改善 接受放射治疗的肺癌患者的生活质量。新的肺功能成像方式， 被称为4DCT-通风，使用4DCT数据和图像处理技术创新地 计算肺通气图。4DCT-通风可通过使患者能够 功能回避放射治疗计划的生成。功能避让使用4DCT-通风来避免 肺的功能部分，假设减少对功能肺的剂量将减少胸侧- 效果。我们的4DCT-通风研究已经从回顾性研究进展到早期试验阶段，使用 4DCT-呼吸机用于功能性避免放射治疗。早期有希望的毒性来自4DCT- 通风临床试验为国家试验和扩大临床集成提供了强有力的理论基础 个别诊所。问题是，扩大4DCT-通风的临床集成目前是不可能的 由于缺乏一致的、有效的和临床验证的方法。我们提出了一个学术产业 与MIM软件合作解决这些阻碍4DCT-通风临床集成的挑战。 我们研究的目的是开发能够安全、有效和临床验证的方法 临床结合4DCT-呼吸机功能回避的方法。我们最重要的假设是 4DCT-我们开发的通风功能避免创新将在 以前没有4DCT-通风经验的诊所。该项目将在3个目标下进行。目标1将会发展 支持自动4DCT-通风计算的方法，包括自动分段、统计稳健 计算方法和临床有效的质量保证工具。AIM 2将开发4DCT的方法- 通风功能避免放射治疗包括影像异质性评估，进展 基于知识的功能规划方法，以及对降低毒性最关键的指标的评估。在……里面 与我们的行业合作伙伴合作，目标1和目标2中开发的方法将集成到 商业级软件工具。目标3将通过评估4DCT-通风功能 在没有4DCT-机械通气经验的临床中，可以证明避免使用可以减少毒性。 我们的项目将生成所需的工具和数据，以提供指导如何正确地 将4DCT-通风纳入临床护理。这些方法和数据最终将达到商业级 适合繁忙诊所的站台。4DCT-机械通气在改善肺癌预后方面有很大潜力 患者和我们的项目将使这一新的成像模式集成到临床护理中。