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.

摘要 确定适当的临床靶体积（CTV）来捕获微观疾病是最大的限制 在临床放射治疗中，努力提供最大程度的适形治疗以最小化与放射治疗相关的辐射， 毒性定义CTV的挑战来自肿瘤扩散的固有不确定性， 可见的大体肿瘤体积（GTV）。CTV的描绘是一个费力的手动过程。而且 CTV轮廓绘制和后续治疗计划剂量优化之间存在实际脱节。 探索用有效控制肿瘤的剂量覆盖恶性肿瘤和 目前不可能将潜在的毒性剂量输送到周围的健康组织。广泛的长期 这个项目的目标是使CTV清晰度更容易和更好。我们将重点关注两个具有挑战性的疾病部位， 胶质瘤和肉瘤。我们的方法可以推广到基本上所有其他疾病的网站。第一个目标是 自动CTV定义。这将通过机器学习屏障结构和解剖结构来实现。 已知影响肿瘤扩散超过可见GTV的结构域。CTV将以3D形式扩展 在不同的解剖学区域中采取优选的伸展方向（例如沿着肌肉纤维伸展） 的理由：建筑第二个目标是开发一个用户界面，让用户与自动CTV进行交互 定义系统，以避免黑箱印象。如有必要，用户可以编辑自动生成的轮廓。 将记录任何更改并用于重新训练系统。CTV的扩建将整合到一个多- 用于治疗计划的标准优化系统，其中用户可以交互式地探索剂量测定 CTV扩展对肿瘤剂量覆盖范围和正常结构剂量负担的影响。第三 我们的目的是测试假设，这个系统将导致一个更一致的定义的CTV，更好的时间 效率，以及更好的治疗计划，导致预期临床结果的可证明的改善。我们 将使该系统作为一个独立的系统提供给学术用户和医院。