Automated interactive definition of the clinical target volume in radiation oncology

放射肿瘤学中临床靶区的自动交互定义

• 批准号: 10547813
• 负责人: THOMAS R. BORTFELD
• 金额: $ 30.66万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-06 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10547813
• 关键词: 3-Dimensional; Adherence; Affect; Algorithms; Anatomy; Automation; Cancer Patient; Clinical; Collaborations; Communities; Computing Methodologies; Consensus; Development; Disease; Dose; Glioma; Goals; Growth; Guidelines; Hospitals; Human; Individual; Judgment; Laboratories; Lead; Link; Location; Machine Learning; Malignant Neoplasms; Manuals; Methods; Microscopic; Modernization; Muscle Fibers; Normal tissue morphology; Outcome; Patients; Perception; Physicians; Process; Protocols documentation; Radiation; Radiation Dose Unit; Radiation Oncologist; Radiation Oncology; Radiation therapy; Research; Role; Shapes; Site; Structure; System; Targeted Radiotherapy; Technology; Testing; Therapy Clinical Trials; Time; Tissues; Toxic effect; Training; Treatment outcome; Tumor Volume; Uncertainty; Validation; Variant; Work; X-Ray Computed Tomography; automated algorithm; clinical care; clinical practice; clinical translation; impression; improved; inter-institutional; interest; millimeter; open source; radiation delivery; sarcoma; technology development; tool; treatment optimization; treatment planning; tumor

Abstract Identifying the appropriate clinical target volume (CTV) to capture microscopic disease is the greatest limitation in clinical radiotherapy in efforts to offer maximally conformal treatment to minimize radiation associated toxicity. The challenge of defining the CTV comes from inherent uncertainty in the tumor spread beyond the visible gross tumor volume (GTV). Delineation of the CTV is a laborious manual process. Furthermore, there exists a practical disconnect between CTV contouring and the subsequent treatment plan dose optimization. Exploration of the real tradeoff between covering malignancy with the dose effective for tumor control and delivering potentially toxic dose to surrounding healthy tissues is currently impossible. The broad long-term goal of this project is to make CTV definition easier and better. We will focus on two challenging disease sites, glioma and sarcoma. Our methods can be generalized to essentially all other disease sites. The first aim is to automate CTV definition. This will be accomplished by machine learning of barrier structures and anatomic domains that are known to affect the spread of tumor beyond the visible GTV. The CTV will be expanded in 3D taking the preferred direction of spread in the different anatomic domains (such as spread along muscle fibers) into account. The second aim is to develop a user interface that lets the user interact with the automatic CTV definition system, to avoid a black box impression. The user can edit the auto generated contour if necessary. Any changes will be logged and used to retrain the system. The CTV expansion will be integrated in a multi- criteria optimization system for treatment planning, where the user can interactively explore the dosimetric impact of CTV expansion on the dose coverage of the tumor and dose burden in normal structures. In the third aim we will test the hypotheses that this system will lead to a more consistent definition of the CTV, better time efficiency, and better treatment plans leading to provable improvements of the expected clinical outcome. We will make the system available as a standalone system to academic users and hospitals.

抽象的 确定适当的临床目标体积（CTV）以捕获微观疾病是最大的限制 在临床放射疗法中，为提供最大的保形治疗以最大程度地减少相关的辐射 毒性。定义CTV的挑战来自肿瘤扩散的固有不确定性 可见的总肿瘤体积（GTV）。 CTV的描述是一个费力的手动过程。此外，那里 CTV轮廓与随后的治疗计划优化之间存在实际断开连接。 探索涵盖恶性肿瘤的剂量有效控制肿瘤和 目前不可能向周围健康组织提供潜在的有毒剂量。长期的 该项目的目标是使CTV定义更容易，更好。我们将专注于两个具有挑战性的疾病部位， 神经胶质瘤和肉瘤。我们的方法可以推广到本质上所有其他疾病部位。第一个目的是 自动CTV定义。这将通过机器学习屏障结构和解剖学来实现 已知会影响可见GTV以外的肿瘤扩散的域。 CTV将在3D中扩展 沿不同的解剖结构域（例如沿着肌肉纤维扩散）沿优选的扩散方向 考虑到。第二个目的是开发一个用户界面，使用户可以与自动CTV进行交互 定义系统，以避免黑匣子印象。用户可以在必要时编辑自动生成的轮廓。 任何更改都将被记录并用于重新训练系统。 CTV扩展将集成到多个 用于治疗计划的标准优化系统，用户可以在其中交互式探索剂量计 CTV膨胀对正常结构中肿瘤剂量覆盖的影响。在第三 目的，我们将测试该系统将导致CTV的更一致的定义，更好的时间的假设 效率和更好的治疗计划，从而可以证明可以改善预期的临床结果。我们 将使该系统作为独立系统可用于学术用户和医院。