Chen Yingxuan, Vinogradskiy Yevgeniy, Yu Yan, Shi Wenyin, Liu Haisong
Department of Radiation Oncology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States.
Front Oncol. 2022 Mar 14;12:842579. doi: 10.3389/fonc.2022.842579. eCollection 2022.
Spine SBRT target delineation is time-consuming due to the complex bone structure. Recently, Elements SmartBrush Spine (ESS) was developed by Brainlab to automatically generate a clinical target volume (CTV) based on gross tumor volume (GTV). The aim of this project is to evaluate the accuracy and efficiency of ESS auto-segmentation.
Twenty spine SBRT patients with 21 target sites treated at our institution were used for this retrospective comparison study. Planning CT/MRI images and physician-drawn GTVs were inputs for ESS. ESS can automatically segment the vertebra, split the vertebra into 6 sectors, and generate a CTV based on the GTV location, according to the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines. The auto-segmented CTV can be edited by including/excluding sectors of the vertebra, if necessary. The ESS-generated CTV contour was then compared to the clinically used CTV using qualitative and quantitative methods. The CTV contours were compared using visual assessment by the clinicians, relative volume differences (RVD), distance of center of mass (DCM), and three other common contour similarity measurements such as dice similarity coefficient (DICE), Hausdorff distance (HD), and 95% Hausdorff distance (HD95).
Qualitatively, the study showed that ESS can segment vertebra more accurately and consistently than humans at normal curvature conditions. The accuracy of CTV delineation can be improved significantly if the auto-segmentation is used as the first step. Conversely, ESS may mistakenly split or join different vertebrae when large curvatures in anatomy exist. In this study, human interactions were needed in 7 of 21 cases to generate the final CTVs by including/excluding sectors of the vertebra. In 90% of cases, the RVD were within ±15%. The RVD, DCM, DICE, HD, and HD95 for the 21 cases were 3% ± 12%, 1.9 ± 1.5 mm, 0.86 ± 0.06, 13.34 ± 7.47 mm, and 4.67 ± 2.21 mm, respectively.
ESS can auto-segment a CTV quickly and accurately and has a good agreement with clinically used CTV. Inter-person variation and contouring time can be reduced with ESS. Physician editing is needed for some occasions. Our study supports the idea of using ESS as the first step for spine SBRT target delineation to improve the contouring consistency as well as to reduce the contouring time.
由于脊柱结构复杂，脊柱立体定向放射治疗（SBRT）的靶区勾画耗时较长。最近，Brainlab公司开发了Elements SmartBrush Spine（ESS），可根据大体肿瘤体积（GTV）自动生成临床靶体积（CTV）。本项目旨在评估ESS自动分割的准确性和效率。
本回顾性比较研究使用了在我院接受治疗的20例脊柱SBRT患者的21个靶区。将计划CT/MRI图像和医生绘制的GTV作为ESS的输入。ESS可根据国际脊柱放射外科联盟（ISRC）共识指南自动分割椎体，将椎体分为6个部分，并根据GTV位置生成CTV。如有必要，可通过包含/排除椎体部分来编辑自动分割的CTV。然后使用定性和定量方法将ESS生成的CTV轮廓与临床使用的CTV进行比较。通过临床医生的视觉评估、相对体积差异（RVD）、质心距离（DCM）以及其他三种常见的轮廓相似性测量方法（如骰子相似系数（DICE）、豪斯多夫距离（HD）和95%豪斯多夫距离（HD95））来比较CTV轮廓。
定性分析表明，在正常曲率条件下，ESS分割椎体的准确性和一致性高于人工分割。如果将自动分割作为第一步，CTV勾画的准确性可显著提高。相反，当解剖结构存在大曲率时，ESS可能会错误地分割或合并不同椎体。在本研究中，21例中有7例需要人工干预，通过包含/排除椎体部分来生成最终的CTV。在90%的病例中，RVD在±15%以内。21例的RVD、DCM、DICE、HD和HD95分别为3%±12%、1.9±1.5mm、0.86±0.06、13.34±7.47mm和4.67±2.21mm。
ESS能够快速准确地自动分割CTV，与临床使用的CTV具有良好的一致性。使用ESS可减少人际差异和轮廓勾画时间。某些情况下仍需要医生进行编辑。我们的研究支持将ESS作为脊柱SBRT靶区勾画的第一步，以提高轮廓勾画的一致性并减少轮廓勾画时间的观点。
