Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Med Phys. 2023 Dec;50(12):8023-8033. doi: 10.1002/mp.16782. Epub 2023 Oct 13.
Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations.
To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice.
Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D difference for target volumes and D difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia.
For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs.
Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.
自适应质子治疗工作流程依赖于整个治疗过程中的精确成像。我们中心目前使用每周重复 CT(rCT)进行治疗监测和计划调整。然而,基于深度学习的方法最近已经成功地纠正了 CBCT 图像,这些图像受到严重成像伪影的影响,并生成高质量的合成 CT(sCT)图像,从而能够进行基于 CBCT 的质子剂量计算。
比较头颈部(HN)癌症患者的每日 CBCT 基 sCT 图像与计划 CT(pCT)和 rCT,以在模拟实际临床实践的情况下研究基于 CBCT 的 sCT 的剂量学准确性。
使用之前开发和训练的深度卷积神经网络,对头颈部癌症患者的 56 例患者的数据进行处理,生成了 1.962 个 sCT 图像。对 pCT、每周 rCT 和每日 sCT 重新计算了临床 IMPT 治疗计划。将 sCT 的剂量学准确性与同一天的 rCT 和初始计划 CT 进行比较。作为参考,还将 rCT 与 pCT 进行比较。通过计算靶区和危及器官的 D 差异,来量化 sCT 与 rCT/pCT 之间的剂量差异。为了研究可能的剂量差异的临床相关性,计算了吞咽困难和口干的 NTCP 值。
对于靶区,sCT 与 rCT 和 sCT 与 pCT 之间的剂量差异较小,剂量差异大多在±1.5%以内。在 OAR 中观察到更大的剂量差异,普遍存在正向差异,左腮腺的差异最大。90%的 sCT 的 2 级吞咽困难和口干的 delta NTCP 值在±2.5%以内。
靶区剂量在 rCT 和 sCT 之间具有高度相似性。需要进一步调查以确定 OAR 水平剂量差异的来源及其在临床决策中的相关性。