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用于临床前研究的锥束计算机断层扫描成像中器官轮廓勾画的观察者间变异性。

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging.

作者信息

Lappas Georgios, Staut Nick, Lieuwes Natasja G, Biemans Rianne, Wolfs Cecile J A, van Hoof Stefan J, Dubois Ludwig J, Verhaegen Frank

机构信息

Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands.

The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands.

出版信息

Phys Imaging Radiat Oncol. 2022 Jan 24;21:11-17. doi: 10.1016/j.phro.2022.01.002. eCollection 2022 Jan.

Abstract

BACKGROUND AND PURPOSE

In preclinical radiation studies, there is great interest in quantifying the radiation response of healthy tissues. Manual contouring has significant impact on the treatment-planning because of variation introduced by human interpretation. This results in inconsistencies when assessing normal tissue volumes. Evaluation of these discrepancies can provide a better understanding on the limitations of the current preclinical radiation workflow. In the present work, interobserver variability (IOV) in manual contouring of rodent normal tissues on cone-beam Computed Tomography, in head and thorax regions was evaluated.

MATERIALS AND METHODS

Two animal technicians performed manually (assisted) contouring of normal tissues located within the thorax and head regions of rodents, 20 cases per body site. Mean surface distance (MSD), displacement of center of mass (ΔCoM), DICE similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD) were calculated between the contours of the two observers to evaluate the IOV.

RESULTS

For the thorax organs, right lung had the lowest IOV (ΔCoM: 0.08 ± 0.04 mm, DSC: 0.96 ± 0.01, MSD:0.07 ± 0.01 mm, HD:0.20 ± 0.03 mm) while spinal cord, the highest IOV (ΔCoM:0.5 ± 0.3 mm, DSC:0.81 ± 0.05, MSD:0.14 ± 0.03 mm, HD:0.8 ± 0.2 mm). Regarding head organs, right eye demonstrated the lowest IOV (ΔCoM:0.12 ± 0.08 mm, DSC: 0.93 ± 0.02, MSD: 0.15 ± 0.04 mm, HD: 0.29 ± 0.07 mm) while complete brain, the highest IOV (ΔCoM: 0.2 ± 0.1 mm, DSC: 0.94 ± 0.02, MSD: 0.3 ± 0.1 mm, HD: 0.5 ± 0.1 mm).

CONCLUSIONS

Our findings reveal small IOV, within the sub-mm range, for thorax and head normal tissues in rodents. The set of contours can serve as a basis for developing an automated delineation method for e.g., treatment planning.

摘要

背景与目的

在临床前放射学研究中,人们对量化健康组织的放射反应有着浓厚兴趣。由于人工解读引入的变异性,手动轮廓勾画对治疗计划有重大影响。这导致在评估正常组织体积时出现不一致性。对这些差异的评估有助于更好地理解当前临床前放射工作流程的局限性。在本研究中,评估了两名观察者在锥形束计算机断层扫描上对啮齿动物头部和胸部区域正常组织进行手动轮廓勾画时的观察者间变异性(IOV)。

材料与方法

两名动物技术员对啮齿动物胸部和头部区域的正常组织进行手动(辅助)轮廓勾画,每个身体部位20例。计算两名观察者轮廓之间的平均表面距离(MSD)、质心位移(ΔCoM)、DICE相似系数(DSC)和第95百分位豪斯多夫距离(HD),以评估IOV。

结果

对于胸部器官,右肺的IOV最低(ΔCoM:0.08±0.04mm,DSC:0.96±0.01,MSD:0.07±0.01mm,HD:0.20±0.03mm),而脊髓的IOV最高(ΔCoM:0.5±0.3mm,DSC:0.81±0.05,MSD:0.14±0.03mm,HD:0.8±0.2mm)。对于头部器官,右眼的IOV最低(ΔCoM:0.12±0.08mm,DSC:0.93±0.02,MSD:0.15±0.04mm,HD:0.29±0.07mm),而全脑的IOV最高(ΔCoM:0.2±0.1mm,DSC:0.94±0.02,MSD:0.3±0.1mm,HD:0.5±0.1mm)。

结论

我们的研究结果显示,啮齿动物胸部和头部正常组织的IOV较小,在亚毫米范围内。这组轮廓可作为开发例如治疗计划自动勾画方法的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ffc/8790504/e11646363e57/gr1.jpg

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4
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5
Deep learning-enabled multi-organ segmentation in whole-body mouse scans.
Nat Commun. 2020 Nov 6;11(1):5626. doi: 10.1038/s41467-020-19449-7.
7
Advances in Preclinical Research Models of Radiation-Induced Cardiac Toxicity.
Cancers (Basel). 2020 Feb 11;12(2):415. doi: 10.3390/cancers12020415.
8
Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network.
J Appl Physiol (1985). 2020 Jan 1;128(1):42-49. doi: 10.1152/japplphysiol.00465.2019. Epub 2019 Nov 7.
9
Evofosfamide sensitizes esophageal carcinomas to radiation without increasing normal tissue toxicity.
Radiother Oncol. 2019 Dec;141:247-255. doi: 10.1016/j.radonc.2019.06.034. Epub 2019 Aug 17.
10
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Radiother Oncol. 2019 Sep;138:68-74. doi: 10.1016/j.radonc.2019.05.010. Epub 2019 May 27.

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