Automated interactive definition of the clinical target volume in radiation oncology

摘要 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 在临床放射治疗中努力提供最大限度的适形治疗，以最大限度地减少与辐射有关的 毒性。定义CTV的挑战来自于肿瘤扩散到 肉眼可见肿瘤体积(GTV)。划定闭路电视是一个费力的手工过程。此外，还有 在CTV轮廓和随后的治疗计划剂量优化之间存在实际脱节。 探索以有效的肿瘤控制剂量覆盖恶性肿瘤与真正的权衡 目前，向周围健康组织提供潜在毒性剂量是不可能的。宽泛的长期 这个项目的目标是让CTV的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地点， 胶质瘤和肉瘤。我们的方法基本上可以推广到所有其他疾病地点。第一个目标是 自动执行闭路电视清晰度。这将通过对屏障结构和解剖学的机器学习来实现 已知的影响肿瘤扩散到可见GTV之外的区域。闭路电视将以3D方式扩展 在不同的解剖领域采取首选的扩散方向(例如沿着肌肉纤维扩散) 考虑到了。第二个目标是开发一个用户界面，让用户与自动闭路电视互动 定义系统，避免黑盒印象。如果需要，用户可以编辑自动生成的等高线。 任何更改都将被记录下来，并用于重新培训系统。闭路电视扩建将整合在多个 治疗计划的标准优化系统，其中用户可以交互地探索剂量学 CTV扩张对肿瘤剂量覆盖率和正常组织内剂量负荷的影响。在第三节 目的我们将检验假设，这个系统将导致更一致的定义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。

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.