Image X Institute, University of Sydney, Suite 201, Biomedical Building (C81), 1 Central Ave, Eveleigh, NSW, 2015, Australia.
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, QLD, Australia.
Radiat Oncol. 2024 Oct 8;19(1):139. doi: 10.1186/s13014-024-02524-4.
This observational study aims to establish the feasibility of using x-ray images of radio-opaque chemoembolisation deposits in patients as a method for real-time image-guided radiation therapy of hepatocellular carcinoma.
This study will recruit 50 hepatocellular carcinoma patients who have had or will have stereotactic ablative radiation therapy and have had transarterial chemoembolisation with a radio-opaque agent. X-ray and computed tomography images of the patients will be analysed retrospectively. Additionally, a deep learning method for real-time motion tracking will be developed. We hypothesise that: (i) deep learning software can be developed that will successfully track the contrast agent mass on two thirds of cone beam computed tomography (CBCT) projection and intra-treatment images (ii), the mean and standard deviation (mm) difference in the location of the mass between ground truth and deep learning detection are ≤ 2 mm and ≤ 3 mm respectively and (iii) statistical modelling of study data will predict tracking success in 85% of trial participants.
Developing a real-time tracking method will enable increased targeting accuracy, without the need for additional invasive procedures to implant fiducial markers.
Registered to ClinicalTrials.gov (NCT05169177) 12th October 2021.
本观察性研究旨在确定将患者体内经放射不透性化疗栓塞剂沉积的 X 射线图像用作实时图像引导肝癌放射治疗方法的可行性。
本研究将招募 50 名接受立体定向消融放射治疗且接受经动脉化疗栓塞术联合放射不透性造影剂的肝细胞癌患者。将对患者的 X 射线和计算机断层扫描图像进行回顾性分析。此外，还将开发一种用于实时运动跟踪的深度学习方法。我们假设：（i）可以开发出深度学习软件，成功跟踪三分之二的锥形束 CT（CBCT）投影和治疗期间图像上的造影剂团块；（ii）质量的位置在真实值和深度学习检测之间的平均值和标准偏差（mm）差异分别小于等于 2mm 和小于等于 3mm；（iii）研究数据的统计建模将预测 85%的试验参与者的跟踪成功率。
开发实时跟踪方法将提高靶向准确性，而无需额外的侵入性程序植入基准标记。
于 2021 年 10 月 12 日在 ClinicalTrials.gov 上注册（NCT05169177）。
