Abstract

PurposeSpine 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.MethodsTwenty 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).ResultsQualitatively, 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.ConclusionESS 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.

Highlights

  • Bone is a frequent site of metastases and causes significant morbidities including severe pain and spinal cord compression [1–3]

  • The aim of this study is to evaluate the accuracy of the clinical target volume (CTV) autosegmentation based on existing gross tumor volume (GTV) contours using Elements SmartBrush Spine (ESS) for spine Stereotactic body radiotherapy (SBRT) patients

  • Inter-person variation can be reduced with ESS

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Summary

Introduction

Bone is a frequent site of metastases and causes significant morbidities including severe pain and spinal cord compression [1–3]. Stereotactic body radiotherapy (SBRT) has been increasingly used to provide a treatment option in the multidisciplinary management of metastases located within or adjacent (paraspinal) to vertebrae/spinal cord. Target segmentation accuracy becomes critical for spine SBRT due to the requirement of ablative high dose per fraction to the target volume and minimizing the dose to organ at risks, especially the spinal cord. Some methods require human intervention or the manual setting of parameters Deep learning methods such as supervised learning might be a solution for fully automated spine auto-segmentation, but large training sets are needed. Based on the International Spine Radiosurgery Consortium (ISRC) Consensus guidelines [8], different anatomical regions (such as vertebral body, pedicles, spinous process, or transverse processes/lamina) will be included in the clinical target volume (CTV) based on location of gross tumor volume (GTV). Most of the above-mentioned published studies are focused on whole spine segmentation, which might not be available to be applied for clinical spine SBRT treatment yet

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