图像指导方面的最新创新,治疗交付,和自适应放疗(RT)为前列腺癌患者的目标体积(PTV)边缘设计创造了新的范例。我们对有关完整前列腺RT的PTV边缘选择和设计的最新文献进行了回顾,不包括术后RT,近距离放射治疗,和质子治疗。我们的评论描述了前列腺和精囊作为异质变形结构的日益关注,随着前列腺内GTV增强和盆腔淋巴结治疗的进一步出现。为了捕捉最近的创新,我们强调了锥形束CT制导的演变,越来越多地使用MRI来改善目标描绘和图像配准,并支持在线自适应RT。此外,我们总结了新的和不断发展的图像引导治疗平台,以及最近关于新型固定策略和运动跟踪的报道。我们的报告还捕获了人工智能的最新实现,以支持图像指导和自适应RT。通过基于模型的风险估计和临床试验来描述PTV边缘变化的临床影响,我们强调最近的高影响报告。我们的报告侧重于PTV利润背景下的主题,但也展示了试图通过稳健的优化和概率规划方法超越PTV利润配方的研究。尽管存在常规使用基于CT的图像引导的目标边缘指南,需要进一步验证,以了解单独或结合实时运动补偿的在线适应的最佳裕度,以最大程度地减少前列腺癌患者治疗中的系统和随机不确定性.
Recent innovations in image guidance, treatment delivery, and adaptive radiotherapy (RT) have created a new paradigm for planning target volume (PTV) margin design for patients with prostate cancer. We performed a
review of the recent literature on PTV margin selection and design for intact prostate RT, excluding post-operative RT, brachytherapy, and proton therapy. Our
review describes the increased focus on prostate and seminal vesicles as heterogenous deforming structures with further emergence of intra-prostatic GTV boost and concurrent pelvic lymph node treatment. To capture recent innovations, we highlight the evolution in cone beam CT guidance, and increasing use of MRI for improved target delineation and image registration and supporting online adaptive RT. Moreover, we summarize new and evolving image-guidance treatment platforms as well as recent reports of novel immobilization strategies and motion tracking. Our report also captures recent implementations of artificial intelligence to support image guidance and adaptive RT. To characterize the clinical impact of PTV margin changes via model-based risk estimates and clinical trials, we highlight recent high impact reports. Our report focusses on topics in the context of PTV margins but also showcase studies attempting to move beyond the PTV margin recipes with robust optimization and probabilistic planning approaches. Although guidelines exist for target margins conventional using CT-based image guidance, further validation is required to understand the optimal margins for online adaptation either alone or combined with real-time motion compensation to minimize systematic and random uncertainties in the treatment of patients with prostate cancer.