Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Department of Radiation Oncology, The University of Alabama-Birmingham, Birmingham, Alabama, USA.
J Appl Clin Med Phys. 2023 Mar;24(3):e13839. doi: 10.1002/acm2.13839. Epub 2022 Nov 22.
To develop and evaluate an automated whole-brain radiotherapy (WBRT) treatment planning pipeline with a deep learning-based auto-contouring and customizable landmark-based field aperture design.
The pipeline consisted of the following steps: (1) Auto-contour normal structures on computed tomography scans and digitally reconstructed radiographs using deep learning techniques, (2) locate the landmark structures using the beam's-eye-view, (3) generate field apertures based on eight different landmark rules addressing different clinical purposes and physician preferences. Two parallel approaches for generating field apertures were developed for quality control. The performance of the generated field shapes and dose distributions were compared with the original clinical plans. The clinical acceptability of the plans was assessed by five radiation oncologists from four hospitals.
The performance of the generated field apertures was evaluated by the Hausdorff distance (HD) and mean surface distance (MSD) from 182 patients' field apertures used in the clinic. The average HD and MSD for the generated field apertures were 16 ± 7 and 7 ± 3 mm for the first approach, respectively, and 17 ± 7 and 7 ± 3 mm, respectively, for the second approach. The differences regarding HD and MSD between the first and the second approaches were 1 ± 2 and 1 ± 3 mm, respectively. A clinical review of the field aperture design, conducted using 30 patients, achieved a 100% acceptance rate for both the first and second approaches, and the plan review achieved a 100% acceptance rate for the first approach and a 93% acceptance rate for the second approach. The average acceptance rate for meeting lens dosimetric recommendations was 80% (left lens) and 77% (right lens) for the first approach, and 70% (both left and right lenses) for the second approach, compared with 50% (left lens) and 53% (right lens) for the clinical plans.
This study provided an automated pipeline with two field aperture generation approaches to automatically generate WBRT treatment plans. Both quantitative and qualitative evaluations demonstrated that our novel pipeline was comparable with the original clinical plans.
开发并评估一种基于深度学习的自动勾画和可定制基于标志的射野孔径设计的全脑放射治疗(WBRT)治疗计划自动化流水线。
该流水线包括以下步骤:(1)使用深度学习技术在 CT 扫描和数字重建射线照片上自动勾画正常结构,(2)使用视线定位标志结构,(3)根据针对不同临床目的和医生偏好的 8 种不同标志规则生成射野孔径。为了质量控制,开发了两种生成射野孔径的平行方法。比较了生成的射野形状和剂量分布与原始临床计划的性能。来自四家医院的五位放射肿瘤学家评估了计划的临床可接受性。
通过 182 名患者在临床中使用的射野孔径的 Hausdorff 距离(HD)和平均表面距离(MSD)评估生成的射野孔径的性能。第一种方法生成的射野孔径的平均 HD 和 MSD 分别为 16±7mm 和 7±3mm,第二种方法分别为 17±7mm 和 7±3mm。第一种和第二种方法的 HD 和 MSD 之间的差异分别为 1±2mm 和 1±3mm。对 30 名患者的射野孔径设计进行的临床评估,两种方法的接受率均为 100%,计划审查的接受率第一种方法为 100%,第二种方法为 93%。满足晶状体剂量学建议的平均接受率为第一种方法左晶状体 80%(右晶状体 77%),第二种方法左右晶状体均为 70%,而临床计划左晶状体为 50%(右晶状体为 53%)。
本研究提供了一种具有两种射野孔径生成方法的自动化流水线,可自动生成 WBRT 治疗计划。定量和定性评估均表明,我们的新流水线与原始临床计划相当。