Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI.
Semin Radiat Oncol. 2019 Jul;29(3):236-244. doi: 10.1016/j.semradonc.2019.02.006.
The promise of adaptive therapy to improve outcomes in radiation oncology has been an area of interest and research in the community for many years. One of the sources of data that can be used to drive adaptive therapy is functional information about the tumor or normal tissues. This avenue of adaptation includes many potential sources of data including global markers and functional imaging. Global markers can be assessments derived from blood measurements, patient functional testing, and circulating tumor material and functional imaging data comprises spatial physiological information from various imaging studies such as positron emission tomography, magnetic resonance imaging, and single photon emission computed tomography. The goal of functional adaptation is to use these functional data to adapt radiation therapy to improve patient outcomes. While functional adaptation holds a lot of promise, there are challenges such as quantifying and minimizing uncertainties, streamlining clinical implementation, determining the ideal way to incorporate information within treatment plan optimization, and proving the clinical benefit through trials. This paper will discuss the types of functional information currently being used for adaptation, highlight several areas where functional adaptation has been studied, and introduce some of the barriers to more widespread clinical implementation.
自适应治疗有望改善放射肿瘤学的治疗效果，多年来一直是该领域关注和研究的重点。可用于驱动自适应治疗的数据源之一是关于肿瘤或正常组织的功能信息。这种自适应途径包括许多潜在的数据来源，包括全局标记和功能成像。全局标记可以是来自血液测量、患者功能测试和循环肿瘤物质的评估，而功能成像数据则包含来自各种成像研究（如正电子发射断层扫描、磁共振成像和单光子发射计算机断层扫描）的空间生理信息。功能适应的目标是利用这些功能数据来调整放射治疗，以改善患者的治疗效果。尽管功能适应具有很大的潜力，但也存在一些挑战，如量化和最小化不确定性、简化临床实施、确定在治疗计划优化中整合信息的理想方法，以及通过试验证明临床获益。本文将讨论目前用于自适应的功能信息类型，重点介绍功能自适应研究的几个领域，并介绍更广泛临床应用的一些障碍。
