UNASSIGNED:由于骨骼结构复杂,脊柱SBRT目标描绘耗时。最近,ElementsSmartBrushSpine(ESS)由Brainlab开发,可根据总肿瘤体积(GTV)自动生成临床目标体积(CTV)。该项目的目的是评估ESS自动分割的准确性和效率。
UNASSIGNED:在我们机构接受治疗的有21个目标部位的20例脊柱SBRT患者被用于这项回顾性比较研究。计划CT/MRI图像和医生绘制的GTT是ESS的输入。ESS可以自动分割椎骨,把椎骨分成6个扇区,并根据GTV位置生成CTV,根据国际脊柱放射外科协会(ISRC)共识指南。可以通过包括/排除椎骨的扇区来编辑自动分割的CTV,如有必要。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。临床医生使用视觉评估比较了CTV轮廓,相对体积差异(RVD),质心距离(DCM),和其他三个常见的轮廓相似性度量,如骰子相似性系数(DICE),Hausdorff距离(HD),和95%的Hausdorff距离(HD95)。
未经批准:定性,研究表明,在正常曲率条件下,ESS可以比人类更准确,更一致地分割椎骨。如果使用自动分割作为第一步,则可以显着提高CTV轮廓的准确性。相反,当解剖结构中存在较大的曲率时,ESS可能会错误地分裂或连接不同的椎骨。在这项研究中,21例病例中的7例需要通过纳入/排除椎骨部分来产生最终的CTV.在90%的案例中,RVD在±15%以内。RVD,DCM,骰子,HD,21例HD95为3%±12%,1.9±1.5mm,0.86±0.06,13.34±7.47mm,4.67±2.21mm,分别。
UNASSIGNED:ESS可以快速,准确地自动分割CTV,并与临床使用的CTV具有良好的一致性。ESS可以减少人与人之间的变化和轮廓时间。在某些场合需要医生编辑。我们的研究支持使用ESS作为脊柱SBRT目标轮廓的第一步的想法,以提高轮廓的一致性并减少轮廓的时间。
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.