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) 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，以最大限度地减少相关辐射 毒性。定义 CTV 的挑战来自于肿瘤扩散超出范围的固有不确定性。 可见肿瘤大体体积（GTV）。 CTV 的描绘是一个费力的手动过程。此外，还有 CTV 轮廓绘制和随后的治疗计划剂量优化之间存在实际脱节。 探索覆盖恶性肿瘤与有效控制肿瘤的剂量之间的真正权衡 目前不可能向周围的健康组织提供潜在的有毒剂量。广泛的长期 该项目的目标是使 CTV 定义更容易、更好。我们将重点关注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基本上所有其他疾病部位。第一个目标是 自动化 CTV 定义。这将通过屏障结构和解剖学的机器学习来完成 已知会影响可见 GTV 之外的肿瘤扩散的结构域。 CTV将扩展为3D 在不同的解剖区域采取首选的传播方向（例如沿着肌肉纤维传播） 考虑到。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，避免黑匣子的印象。如有必要，用户可以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 扩建将整合到一个多 治疗计划的标准优化系统，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。在第三个 目标我们将测试这个系统将导致 CTV 的定义更加一致、更好的时间的假设 效率和更好的治疗计划可显着改善预期的临床结果。我们 将使该系统作为独立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.